essay on customer segmentation

What is Customer Segmentation? Definition, Models, Analysis, Strategy and Examples

By Nick Jain

Published on: September 8, 2023

Table of Contents

What is Customer Segmentation?

Customer segmentation models, top 13 customer segmentation analysis, 13 best customer segmentation strategy, top 6 examples of customer segmentation.

Customer segmentation is defined as a marketing strategy that involves dividing a company’s target market into distinct groups or segments based on specific criteria or characteristics. The goal of customer segmentation is to better understand and cater to the diverse needs and preferences of different customer groups. By tailoring products, services, and marketing efforts to these segments, businesses can improve customer satisfaction, increase sales, and enhance overall marketing efficiency.

Here are some common criteria or characteristics used for customer segmentation:

• Demographic Segmentation: This involves categorizing customers based on demographic factors such as age, gender, income, education, marital status, and occupation.
• Geographic Segmentation: Customers are grouped by their location, such as country, region, city, or zip code. Geographic segmentation is particularly useful for businesses with location-specific offerings or marketing strategies.
• Psychographic Segmentation: This focuses on customers’ lifestyles, values, beliefs, attitudes, and interests. It helps in understanding the psychological aspects that influence buying decisions.
• Behavioral Segmentation: Customers are categorized based on their behavior, including purchase history, brand loyalty, product usage, and buying frequency. This helps businesses tailor their marketing efforts to different stages of the customer journey.
• Socioeconomic Segmentation: This involves considering factors like social class, social status, and cultural background to segment customers. It’s particularly relevant for businesses offering luxury or culturally specific products.
• Benefit Segmentation: Customers are grouped based on the specific benefits or solutions they seek from a product or service. This approach helps in creating targeted marketing messages.
• Customer Lifecycle Stage: Customers can be segmented based on where they are in their relationship with the company, such as new customers, loyal customers, or churned customers.
• Usage Patterns: Segmenting customers by how often and in what ways they use a product or service can help in tailoring retention strategies or upselling.
• Purchase Intent: Customers can be divided based on their likelihood to make a purchase in the near future. This is valuable for lead generation and conversion-focused marketing.

Once customer segments are identified, businesses can create customized marketing campaigns, product offerings, and customer experiences for each group. This approach allows for more effective communication and engagement, ultimately leading to improved customer satisfaction and business growth.

Customer segmentation is an ongoing process, as customer preferences and behaviors may change over time. Regularly analyzing and updating segments ensures that businesses remain responsive to evolving market dynamics.

There are several customer segmentation models and techniques that businesses can use to categorize their customer base into meaningful segments. The choice of a segmentation model depends on the specific goals, industry, and available data of the business. Here are several prevalent customer segmentation models:

1. Demographic Segmentation: Divides customers based on demographic characteristics such as age, gender, income, education, marital status, and occupation. This model is relatively simple and widely used for broad marketing strategies.

2. Geographic Segmentation: Segment customers based on their geographic location, such as country, region, city, or climate zone. This is especially valuable for businesses that provide location-specific products or services.

3. Psychographic Segmentation: Focuses on customers’ lifestyles, values, beliefs, attitudes, and interests. This model helps in understanding the psychological aspects that influence buying decisions. Tools like surveys and personality assessments can be used for this segmentation.

4. Behavioral Segmentation: Groups customers based on their behaviors and actions, such as purchase history, brand loyalty, product usage, and frequency of interactions with the company. This model is often used for targeted marketing and retention strategies.

5. RFM Analysis: Represents Recency, Frequency, and Monetary Value. This model segments customers based on how recently they made a purchase, how frequently they make purchases, and how much money they spend. It’s particularly useful for e-commerce businesses.

6. Customer Lifecycle Stage: Segment customers based on where they are in their relationship with the company, including new customers, loyal customers, dormant customers, and churned customers. This helps tailor marketing efforts to each stage of the customer journey.

7. Value-Based Segmentation: Divides customers into segments based on their lifetime value (LTV) or potential future value. High-value customers may receive exclusive offers and personalized attention.

8. Benefit Segmentation: Centers on the particular advantages or resolutions customers are looking for from a product or service. This model helps in creating targeted marketing messages that highlight the most relevant benefits.

9. Cluster Analysis: Uses statistical techniques to group customers based on similarities in their purchasing behavior, preferences, or other relevant variables. It’s an unsupervised learning approach that discovers natural patterns within the data.

10. Machine Learning and Predictive Segmentation: Utilizes machine learning algorithms to analyze large datasets and identify hidden patterns and trends in customer behavior. This approach can uncover more complex and dynamic segments.

11. Customer Personas: Involves creating fictional representations of typical customers within different segments. Personas are based on real data and can help in humanizing and visualizing customer segments for marketing teams.

12. Hybrid Segmentation: Combines multiple segmentation models to create a more comprehensive view of customers. For example, combining demographic, behavioral, and psychographic data to create highly targeted segments.

When implementing a customer segmentation model, it’s crucial to collect and analyze relevant data effectively. Additionally, regularly updating and refining the segmentation model is essential to ensure its continued relevance and accuracy as customer preferences and behaviors evolve over time.

Learn more: What is Customer Feedback Analysis?

Customer segmentation analysis is the process of examining and understanding the characteristics, behaviors, and preferences of different customer segments within a target market. The goal of this analysis is to gain insights that can inform marketing strategies, product development, and customer engagement tactics. Here’s a step-by-step guide to conducting customer segmentation analysis:

1. Define the Objectives

Clearly outline the goals of your segmentation analysis. What specific information or understanding are you aiming to acquire? Are you aiming to increase sales, improve customer retention, or tailor marketing messages more effectively?

2. Data Collection

Gather relevant data about your customers. This data can come from various sources, including customer surveys, transaction records, website analytics, social media insights, and customer support interactions. The data should include both demographic and behavioral information.

3. Data Cleaning and Preprocessing

Clean and prepare the data for analysis. This involves removing duplicates, handling missing values, and standardizing data formats. Ensure data accuracy and consistency.

4. Select Segmentation Variables

Decide which variables you will use for segmentation. Common variables include age, gender, location, purchase history, website behavior, and customer preferences. These variables should align with your segmentation objectives.

5. Segmentation Method

Choose the segmentation method or algorithm you will use. Common techniques include k-means clustering, hierarchical clustering, and machine learning algorithms like decision trees or neural networks. The choice depends on the complexity of your data and the desired granularity of segmentation.

6. Segmentation Process

Apply the chosen segmentation method to your dataset to group customers into segments. Each customer should belong to one primary segment based on the selected variables.

7. Profile Segments

Once segments are defined, profile each segment by examining their key characteristics and behaviors. Create customer personas for each segment to humanize and visualize them.

8. Statistical Analysis

Conduct statistical analysis to identify significant differences between segments. This can involve hypothesis testing, regression analysis, or other statistical tests to understand which factors are most influential within each segment.

9. Segment Validation

Validate your segments to ensure they are meaningful and actionable. Assess whether they align with your business goals and whether they can be targeted effectively.

10. Strategy Development

Based on the insights gained from the analysis, develop tailored marketing strategies, product offerings, and communication plans for each segment. Determine which products or services are most appealing to each segment and how to reach them effectively.

11. Implementation

Put your strategies into action by customizing marketing campaigns, messaging, and product features for each segment. Monitor the performance of these efforts closely.

12. Evaluation and Iteration

Continuously evaluate the effectiveness of your segmentation strategies. Analyze the impact on key performance metrics such as conversion rates, customer retention, and revenue. Make adjustments and refinements as needed.

13. Data Privacy and Compliance

Ensure that your data collection and analysis practices comply with relevant data privacy regulations, such as GDPR or CCPA, to protect customer data and privacy.

Customer segmentation analysis is an ongoing process, as customer behavior and preferences can change over time. Regularly updating and refining your segments based on new data and market dynamics is essential to maintaining the effectiveness of your segmentation strategies.

Learn more: What is Customer Feedback?

Developing a customer segmentation strategy is crucial for businesses seeking to better understand their customers and tailor their marketing efforts effectively. Here’s a step-by-step guide to creating a customer segmentation strategy:

• Define Your Objectives: Clearly outline the goals and objectives of your customer segmentation strategy. Are you looking to increase sales, improve customer satisfaction, enhance product development, or optimize marketing efforts?
• Data Collection and Analysis: Gather relevant customer data from various sources, including demographics, purchase history, website behavior, surveys, and social media interactions. Analyze the collected data to identify patterns, trends, and insights. Use data analytics tools and techniques to gain a deeper understanding of your customer base.
• Segmentation Variables Selection: Choose the variables or criteria that will be used to segment your customers. These variables can include demographics (age, gender, location), behavior (purchase history, website visits), psychographics (lifestyle, values), and more. Ensure that the selected variables align with your business goals and are actionable.
• Segmentation Method: Decide on the segmentation method or algorithm you will use. Common methods include clustering techniques (e.g., k-means, hierarchical clustering) and machine learning algorithms (e.g., decision trees, random forests, neural networks). The choice of method should be based on the complexity of your data and the desired granularity of segmentation.
• Segmentation Process: Apply the chosen segmentation method to your customer data to group customers into distinct segments. Each customer should belong to one primary segment. Ensure that the segmentation process is repeatable and can be updated as new data becomes available.
• Segment Profiling: Profile each customer segment by examining their key characteristics, behaviors, and preferences. Create detailed customer personas for each segment to visualize and humanize them.
• Segment Validation: Validate your segments to ensure they are meaningful and actionable. Assess whether they align with your business objectives and whether they can be effectively targeted. Use statistical analysis to confirm the significance of differences between segments.
• Strategy Development: Develop tailored marketing strategies, product offerings, and communication plans for each segment. Determine which products or services are most relevant to each group. Craft messaging and content that resonates with the unique needs and preferences of each segment.
• Implementation: Put your segmentation strategies into action by customizing marketing campaigns, advertising, and customer engagement efforts for each segment. Monitor the performance of these efforts and make real-time adjustments as needed.
• Evaluation and Iteration: Continuously evaluate the effectiveness of your segmentation strategies by analyzing key performance metrics, such as conversion rates, customer retention, and revenue. Be prepared to iterate and refine your segments and strategies as customer preferences and market dynamics evolve.
• Data Privacy and Compliance: Ensure that your data collection and segmentation practices comply with relevant data privacy regulations to protect customer data and privacy.
• Cross-Functional Collaboration: Involve various teams within your organization, including marketing, sales, product development, and customer support, to ensure that the segmentation strategy is integrated into all aspects of your business.
• Regular Updates: Keep your segments up-to-date and relevant by regularly analyzing new data and adapting your strategies accordingly.

A well-executed customer segmentation strategy can lead to improved customer satisfaction, increased sales, and more efficient marketing efforts, as it allows you to deliver tailored experiences and solutions to different customer groups.

Learn more: What is Customer Satisfaction Research?

Customer segmentation can take various forms depending on the industry, business goals, and available data. Here are some customer segmentation examples across different industries:

• Demographic Segmentation: A clothing retailer might segment customers based on age and gender, offering different products and promotions to teenagers, young adults, and older customers.
• Behavioral Segmentation: An online retailer could segment customers based on purchase history, creating segments for frequent shoppers, occasional buyers, and those who haven’t made a purchase in a while.
• Geographic Segmentation: A chain of convenience stores might tailor its product offerings and promotions based on the location of its stores, adjusting for urban, suburban, and rural areas.

2. E-commerce

• RFM Analysis: An e-commerce platform can segment customers based on Recency (how recently they made a purchase), Frequency (how often they buy), and Monetary Value (how much they spend). This helps in targeting high-value, loyal customers differently from one-time shoppers.
• Product Category Preferences: An online marketplace might segment customers based on the product categories they frequently browse or purchase from, such as electronics, fashion, or home decor.

3. Hospitality

• Geographic and Demographic Segmentation: A hotel chain could segment customers based on their location and demographics, offering customized vacation packages for families, business travelers, or couples.
• Booking Behavior: Segments may be created based on booking patterns, such as last-minute bookings, advance reservations, or weekend getaways.

4. Financial Services

• Income and Investment Habits: A bank might segment customers based on their income levels and investment preferences, offering different types of financial products to high-net-worth individuals, middle-income families, and retirees.
• Life Stage: Segmentation based on life stages, such as college students, young professionals, or retirees, can help financial institutions tailor services like savings accounts, loans, and retirement planning.

5. Healthcare

• Health Conditions: Healthcare providers may segment patients based on their health conditions, allowing for personalized treatment plans and communication strategies for patients with chronic illnesses, for example.
• Age and Preventive Care: Age-based segmentation can lead to targeted health check-up reminders and educational materials for pediatric care, adult wellness, or senior health.

6. Technology

• Usage Patterns: A software company can segment users based on how they use the product, offering different features or support options for power users, occasional users, and beginners.
• Upgrade Readiness: Segmentation based on user data can help identify customers who are ready for product upgrades or additional services.

These are just a few examples of customer segmentation. In practice, many businesses use a combination of these segmentation approaches to create a comprehensive view of their customer base and tailor their strategies accordingly. The key is to align segmentation with business goals and customer needs to maximize the effectiveness of marketing, product development, and customer engagement efforts.

Learn more: What is Customer Engagement?

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A New Framework for Customer Segmentation

• Judy Bayer and Marie Taillard

Target your customers according to the jobs they need done.

Her confession was blurted out in the midst of our first conversation about the new digital marketing strategy which we would eventually advise them on: “You know, I don’t think I believe in segmentation anymore.” She said it fast and softly, almost in hope that the sounds around us would make it inaudible. But we did hear it, and responded, “Well, we don’t either.”

• JB Judy Bayer is Director Strategic Analytics for Teradata International. Marie Taillard is a professor of marketing and Director of the Creativity Marketing Centre at ESCP Europe Business School in London, UK.

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Customer Segmentation: The Ultimate Guide

Updated: Jun 14, 2024, 7:57pm

Table of Contents

What is customer segmentation, types of customer segmentation, how to segment customers, customer segmentation tools, frequently asked questions (faqs).

Each customer is different from the next, so a single approach to dealing with different customers won’t work. With customer segmentation, your business can better understand every customer and align relevant strategies and tactics to meet their distinctive needs, helping you to make more profits.

In this customer segmentation guide, Forbes Advisor will show you what customer segmentation is, why you need it and the different types and strategies you can adopt to effectively reach various customers and increase revenue.

Customer segmentation involves grouping existing and potential customers based on shared characteristics. When you segment customers into different classes, you will better understand their needs, preferences and buying patterns. Your marketing and sales team can then tailor their efforts to reach out to your customers in the most fitting way. The result of the guided campaigns and actions will be a boost to customer loyalty and conversations.

Benefits of Customer Segmentation

Customer segmentation will help you learn about customers more deeply, so you can know how to market and sell your products, which customers to invest in and how to improve your marketing techniques. Below are the primary reasons to try out customer segmentation for your business.

• Enhanced customer relationship and brand loyalty: Customer segmentation shows you precisely what each customer seeks so that you can align your marketing messages and know the exact channel to use for communicating with them. It reveals customers’ interests, spending habits, budgets and more to you. And when you interact with customers based on these things, they believe you care, and it’s easier to get more purchases from them. Also, their frequent engagement with your business drives loyalty, which keeps them coming back.
• Enhanced customer experience and sales: With customer segmentation, you will know what customers need, when they need it and what they need it for, which will enable better deliveries for each customer. For example, fine-tuning your marketing messages gets users to make more purchases since they will receive ads or promotions on exactly what they need. When you know how to attend to customers in terms of changing seasons and needs, you can offer better professional services, customer support and product or service offerings. Doing these will bring you more sales since you meet needs, and your business becomes in demand. Also, since you’ll know whom to concentrate on, your business saves time and resources, and customer segmentation ultimately increases your revenues.

Customer Segmentation vs. Market Segmentation

Sometimes, there’s confusion around customer segmentation and market segmentation since many companies use the terms interchangeably. Most businesses consider customer segmentation as a subset of market segmentation. The truth is that the two overlap, and both aim to define their customers, which is the focus of segmentation; however, each has its own uses. And depending on your product or service, you may choose to do one or both. Here’s how customer segmentation and market segmentation compare.

There are several types of customer segmentation you can use for your business. Each one has variables you’ll need to consider when segmenting. Let’s look at the most popular ones below.

Demographic Segmentation

Demographic segmentation groups customers according to shared characteristics, such as gender, age, marital status, educational level, occupation, household income and location.

• Gender: Ensure that this segment is inclusive, with plenty of categorization choices, so you can capture every gender and make customers comfortable.
• Age: This category will direct you to customers’ likely budgets and their most preferred products.
• Marital status: You can segment this as “married,” “in a relationship” and “without a spouse.”
• Occupation: Segmenting customers according to their occupation will give you an idea of customer income and budgets and their interests and availability.

Geographic Segmentation

For geographic segmentation, you will need to divide your customers according to geography, which includes their common language and location. Location can be a neighborhood, city, country or region. You can include their transportation mode, too.

• Preferred language: Knowing this about your customers will help you to communicate better with them. For example, you can use English and Español in a business you run within the United States.
• Location: Knowing where your customers are and how to find them will aid your marketing approach. Your marketing approach to New York residents should be different from your Alabama customers.
• Transportation: Knowing how customers commute will also enhance your marketing and sales. For example, you can use out-of-home advertising on trains, billboards and subway stations if your customers use the train or buses more for transportation.

Psychographic Segmentation

This type of segmentation is based on customer interests, values and personality traits.

• Interests: These are the things, such as sports, games, pets and activities, that customers enjoy. You can, thus, direct your ads towards their areas of interest or collaborate with relevant institutions. For example, you can run a cross-promotional campaign with a seniors’ home if a customer loves spending time with seniors.
• Values: You can determine customer values from surveys or one-to-one interviews. Then, pay attention to fine-tuning your product or service to meet their specific needs.
• Personality traits: You can also segment customers based on their personality traits. Doing this will help you market to them in a way they can better relate to and respond.

Behavioral Segmentation

Behavioral segmentation considers customer purchase history, response to marketing campaigns and product or feature usage patterns when grouping.

• Website activity: To determine this, you will need to track the activities of your customers, such as the elements or pages they interact with the most whenever they visit your website.
• E-commerce activity: Here, you’re monitoring their actions when visiting your online store. It may be based on the products they’ve purchased or the ones they’ve seen but are yet to purchase—their abandoned carts.
• Frequency of purchases: The more purchases a customer makes, the more valuable they are to your business. So you’ll need to determine customer value and consider rewarding customers who have made regular or repeated purchases with exclusive offers.
• Recent customer engagement: Insight into a customer’s most recent interaction with your business will guide you on what to do next with them. You can reward a positive reaction with promotions or have your customer service team work on strengthening the relationship with a customer whose recent interaction with your business was negative, such as returning an item or dropping a negative review.

Needs-based Segmentation

Businesses conduct needs-based segmentation according to the must-haves specific customers require in a product or service delivery.

• Product features: Some customers have certain requirements or buy your products because of specific features they have or needs that they help them fulfill. Find out what these features are, so you can keep your products as inclusive as possible.
• Service needs: For some, it is in how you interact with them, such as your smooth onboarding process, effective customer service or prompt deliveries.
• Delivery method: Customers have specific needs, such as how and when you deliver their products. You need to categorize individuals according to their specific shipping needs.

Technographic Segmentation

This customer segmentation group divides customers based on their use of devices, applications and software.

• Device type: You can divide customers based on the specific type of device they use to interact with your website, which can be a phone, tablet or computer. Knowing this will direct your focus. For example, if most of your customers visit from their phones, you need to consider introducing a mobile app and ensuring that your site is mobile-responsive.
• Browser type: Customers use various browser types, including Google Chrome, Mozilla Firefox and Safari. Find out the ones your customers use to better drive your site layout. You might need to test your content to ensure that they display properly on these browsers.
• Original source: Customers can find you via social media, search engines or even referrals from other customers or websites. You need to know how they discover you so you can optimize the conversion path.

From highlighting your customer segmentation goals to setting up your customer segmentation project, executing data collection, conducting segmentation, incorporating results into marketing and running customer segmentation analysis, customer segmentation can be overwhelming. However, considering the fundamentals of customer segmentation and taking them one step at a time will set your business up for conversion and sales.

Below are the fundamental strategies required for an effective customer segmentation process.

Identify Your Customer Segmentation Goals and Variables

To be able to gather the proper data required to deliver the best customer experience, you first need to determine the type of customer segmentation your business needs. For example, are you looking to offer a new product or feature or expand your market? For a new product or feature offering, consider psychographic, needs-based and technographic segmentation.

Then, examine each segmentation type to determine the elements that require your attention. While this may look insignificant, it can make a difference in the type of messages you send to the segment customers and influence the results of your marketing actions. After that, you can break them into manageable projects.

Set Up Each Customer Segmentation Project

Once you get a clear picture of the customer segmentation types you need, it’s time to set up your projects. An easy approach is to organize the segments and then start with the largest one. Once you’ve set the order, start setting up the projects.

• Set an objective: For each customer segmentation project, you need to first set a goal. Then, create a SMART framework to define your objective and answer questions around areas, such as the demand for the particular segment, how long it will take to build and complete the project, the deadline and how to measure your success.
• Involve stakeholders: Primary stakeholders include employees from the departments and teams that need to be invested in the project’s success since the goals directly affect them. However, you need to also include relevant stakeholders who will use the project most. They include your existing customers, vendors and local businesses. Also, highlight how they can be involved in the segmentation process.
• Define the project scope: To avoid overlaps or confusion later on, define the scope of each project from the onset. Set targets for areas such as data sources, resources and budget.
• Define the project deliverables: The final thing to do when setting up your project is to highlight expected results. These might include segment profiles, highlighted scope of each segment, outlines of processes and workflow wireframes.

Collect and Organize Customer Data

It’s easy to obtain data such as job titles and product purchases from simple purchases. However, you will need to be deliberate about acquiring data such as age and marital status from your customers. Thankfully, there are multiple ways to collect data from customers. Some are direct, such as through customer surveys, while others are indirect—insights are derived from data obtained.

• Surveys: You can use surveys, including post-purchase surveys, after-store surveys and product satisfaction surveys, to gather honest data from customers by asking targeted questions. They can help you cull information about customer thoughts and behaviors necessary for product or service improvement. However, explain why you need answers and ask only questions directly related to the survey goal in the terms and language each customer can connect with. Also, allow them to add their own answers, where pre-provided answers won’t adequately capture their responses.
• Analytics tools: You can also use omnichannel analytics tools to comb through different social platforms. They will find out what customers say about your company and where the conversations occur.
• Social listening: Social media is a good place to obtain customer data. So, search through customer feedback and mentions or discussions about your brand. Then, follow the social listening with an analysis that provides valuable insights for better customer-focused business activities.

Segment Your Customers Into Groups

After pulling the necessary customer data, build your segments. To get the best results, you need to approach this fundamental step with some key ideas in mind.

• Use machine learning: While it’s not compulsory to do this, it can be a huge time saver for your team. Applying automation makes customer segmentation easy, as it can help your business segment contact lists and even create communication workflows, leaving your team with less to do.
• Make segments easy to access: Align each customer segment to relevant marketing and sales channels. For example, to reach Gen Z customers , your marketing strategy should be on TikTok, Instagram or Twitter, not Facebook.
• Include loyal customers: Don’t miss out on existing customers while trying to find new ones. Work on maximizing your interactions with them to increase their number of purchases.
• Make segments easy to use: Ensure that team members find customer segments easy to use. Use clear language they can relate to, and ensure the sections are organized and easy to navigate.

Market to Your Customer Segments

It’s not enough to have customer segments. You need to utilize your customer segments. And that means creating a plan for each segment and making your communications with them customer-centric.

For the best outcome, you need to create specific plans for each segment. You can use the segment information to determine the type of content and products or features that will bring them the most value. Then create new targeted content they can engage with, and craft a strategy to get it to them. Also, find out the best time to send out content.

You can then personalize their emails and create more meaningful landing pages. These will help your customers feel that you understand them, value them and are willing to solve their issues faster.

Run Regular Customer Segmentation Analyses

Change is constant, so you need to analyze your customer segmentation model from time to time. They will help you confirm whether the segments are still necessary and whether or not they are performing by helping you to reach your goals. You can conduct customer segmentation analysis by doing the following.

• Review each customer segment for accuracy
• Compare each segment’s performance with the business goals that prompted its creation
• Ask your internal terms for their feedback
• Collect feedback from customers
• Take actions based on acquired results

Customer segmentation tools help businesses collate data from multiple sources and organize them for effective customer segmentation. These tools automate the technical processes in segmenting customers so that your team members can have more time to focus on other tasks needed for your business’s growth. There are several tools available, including the following.

Qualtrics is a customer segmentation software with machine learning and artificial intelligence (AI) capabilities to help you group customers into segments. Qualtrics also organizes studies on segments, determines optimal communication approaches for each segment and runs customer segmentation analyses. It also provides segmentation features for your products so you can fine-tune your product offerings to specific customer segments.

HubSpot offers customer segmentation tools for creating segments from static and active contact lists. HubSpot uses contact scoring to segment your customers. This software also offers its users event-based segmentation, which can help you locate customers within a specific area and market to them. Also, after events, this customer segmentation tool will use collated details from attendees to create customer segments for future business events.

Segment is a suitable tool for aggregating data points from mobile and website applications. It integrates with more than 300 software tools so you can have your data in one place. Segment tracks customer interaction and offers daily reporting. It will help your marketing team create specific marketing campaigns and deliver personalized customer experiences.

Userpilot has advanced segmentation features for managing customer relationships. With Userpilot, you can track customer engagement, from where they visit to how often they click on specific elements on your website and their journey as customers. Userpilot will segment your customers based on their value to your business or however you want them segmented. It is easy to use and it enables smooth integration with other tools.

Bottom Line

Customer segmentation is relevant for all businesses, as it groups customers based on shared characteristics to provide customers with the exact experience they need. However, to enjoy business growth and sales, which is the aim of customer segmentation, it is important to identify and follow the fundamental strategies for customer segmentation. So, start working on these strategies and use customer segmentation tools for faster processes.

What are the five Ws of customer segmentation?

The five Ws of segmentation are:

• Who: Who the customers are in terms of, for example, age and gender
• What: What customers have done, what they do, what they are likely to do and what they think
• Where: The geographic diversity or concentration of customers
• When: The seasons, life events and periodic activities of customers
• Why and how: Customer interaction with your business, such as online vs. in-store

When should you use customer segmentation?

You should use customer segmentation on an ongoing basis since it will help your teams better understand customers and interact with them. Your marketing team can tailor campaigns to different segments with peculiar characteristics, your product team can identify active and inactive customers and your customer support team will know how to relate with customers.

Why is customer segmentation necessary?

Customer segmentation is necessary because it helps you optimize marketing strategies, which improve customer experience and satisfaction and enhance your customer retention and sales.

Are customer segmentation tools good?

Yes, customer segmentation tools are good. They help you sift through several data points and determine relevant segments. Their automation features save businesses a lot of time and energy.

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How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

• Original Article
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• Published: 06 July 2023
• Volume 11 , pages 677–692, ( 2023 )

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What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.

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Introduction

Business success depends on understanding customers and their needs. A key method to achieve this is customer segmentation , i.e., dividing individual customers into groups based on their similarities and differences (Cooil et al. 2008 ). As postulated by Punj and Stewart ( 1983 : 135), “All segmentation research, regardless of the method used, is designed to identify groups of entities (people, markets, organizations) that share certain common characteristics (attitudes, purchase propensities, media habits, etc.)”. Customer segmentation, in particular, allows businesses to create targeted marketing strategies, improve customer experience, and ultimately, increase revenue (Hosseini and Shabani 2015 ; Simões and Nogueira 2022 ; Spoor 2022 ). As the potential benefits are plentiful, most firms perform customer segmentation across industries, irrespective of their size (Zhou et al. 2021 ). With the rise of artificial intelligence (AI) and machine learning (ML) technologies (Mustak et al. 2021 ), firms are increasingly turning to more advanced AI and ML algorithms (we refer to these as AI/ML algorithms henceforth; for the reader interested in the conceptual distinction of these two terms, we recommend reading Kühl et al. 2022 ) to perform customer segmentation. To this end, customer segmentation undoubtedly represents a cornerstone application of AI for business purposes, as it represents an unsupervised learning problem where AI/ML algorithms are known to be applicable (Joung and Kim 2023 ; Ranjan and Srivastava 2022 ). This relationship is illustrated in Fig.  1 .

The hierarchy of key concepts in this study. AI is a general concept referring to (pseudo-)intelligent algorithms performing tasks that require intelligence. Machine learning is an application of AI. Unsupervised learning is a type of machine learning, of which clustering is the most common approach. Clustering, when applied to a customer dataset, then becomes the customer segmentation task

However, at the same time, firms struggle to understand these novel AI/ML methods and their implementation in everyday customer segmentation—for example, what algorithm to choose for their given data? Should they use one algorithm or many? How many customer segments should be created? How to evaluate the results? These motivational questions reveal a practical research gap for customer segmentation research. Simultaneously, extant academic research lacks a synthesis of how customer segmentation is carried out in research studies, in terms of methods, parameters, evaluation, and so on—in other words, there is a theoretical research gap. Taken together, these two gaps hinder the development of the body of knowledge around the practice and theory of customer segmentation, especially in the light of novel AI technologies (the most cited article assessing the use of clustering in marketing, by Punj and Stewart, is from 1983, so there is a need for an updated study). It becomes harder for firms and researchers to develop better approaches, evaluate them, and implement them if we do not know adequately well how customer segmentation has been done in the past.

We address this knowledge gap by exploring the various customer segmentation methods used in business practices and then delve into the various AI/ML algorithms used for customer segmentation. Therefore, this research emphasizes algorithmic approaches to study what we term algorithmic customer segmentation (ACS). The central premise of our treatise is that AI/ML methods have become increasingly commonplace in marketing, specifically in customer segmentation. However, the extant academic literature lacks a holistic view of how customer ACS is done . Offering such a view is valuable for scholars (in terms of identifying patterns and gaps) and practitioners, as lessons from previous work are likely to offer a useful starting point for robust customer segmentation while helping to identify novel angles for future work in this domain.

Overall, this study aims to increase knowledge of customer segmentation research, and, more explicitly, the role of algorithms and AI in customer segmentation, toward understanding the theory and practice of ACS. In addition, based on our analysis of the extant literature, we offer a detailed and comprehensive agenda for future academic inquiry. To this end, we formulate specific research questions (RQs) that are addressed by a systematic literature review (SLR):

RQ1: What algorithms are typically used for customer segmentation? To understand ACS, we must familiarize ourselves with the different algorithms applied for the customer segmentation task in scholarly literature.

RQ2: Does customer segmentation typically use one or many algorithms? When many, what algorithms emerge in combination? The interaction among different algorithms poses an impactful question, as the field of customer segmentation migrates toward interactive systems that enable stakeholders to interact with the customer segments directly—thus, one algorithm might not be sufficient for more advanced systems (Jung et al. 2018 ).

RQ3: How many customer segments are typically created? There could be two, three, or ten segments—but it is unclear what the optimal number of customer segments should be. Furthermore, this number will likely vary by the dataset in question (Salminen et al. 2022 ). The current body of knowledge lacks systematic insights into how many customer segments are created in research, so we investigate this RQ.

RQ4: How is customer segmentation typically evaluated? ‘Evaluation’ means determining the quality of an algorithmic process; so, how well the customer segmentation worked (Thirumuruganathan et al. 2023 ). This typically measures the segmentation algorithm’s ability to clearly distinguish one group of customers from another group. As evaluation is an essential part of the customer segmentation process, we investigate this RQ.

RQ5: What hyperparameters are used in algorithmic customer segmentation? A ‘hyperparameter’ refers to a parameter external to the model (Jansen et al. 2021 ). The human (or an automated script) selects the “optimal” hyperparameters based on manual viewing or some technical measure. The most central hyperparameter for customer segmentation is the number of segments created (i.e., the segment size). However, there could be other relevant hyperparameters, which is why we are investigating this RQ.

RQ6: How frequently are subject matter experts used for evaluating the customer segments? The quality of customer segmentation can be ascertained using both automatic and manual means. ‘Automatic’ implies that the decision-making relies on technical metrics. However, at least of equal importance is exposing the customer segments to human decision-makers, e.g., managers, software developers, designers, and others that rely on customer segmentation as inputs in their decision-making. To this end, it is worthwhile to investigate how these stakeholders are involved in the customer segmentation process.

Methodology

We adopt the SLR methodology to discover and analyze pertinent existing studies. The SLR process provides a precise and reliable appraisal of the topic under examination, acknowledges existing flaws, and is less biased than typical judgment-based evaluations done by professionals in a specific field. In our approach, we adhered to the review procedure described by Kitchenham et al. ( 2009 ) and conducted the review in three sequential stages: (a) literature search, (b) assessment of the evidence base, and (c) analysis and synthesis of the findings.

Literature search

In this study, we have included literature from various business domains, for instance, marketing and management. Further, as a high degree of relevant knowledge is available in the field of computer science, it is worthwhile to pursue knowledge developed there while concurrently examining its marketing implications (Mustak et al. 2021 ; Salminen et al. 2019 ). Predominantly, we focus on customer segmentation, not market segmentation. Although these two concepts appear similar, they are not: Market segmentation is the process of dividing a market into different subgroups of consumers with similar needs, wants, behavior, or other characteristics. In turn, customer segmentation is the process of dividing customers into groups based on their qualities, attributes, and behaviors. So, the former deals with the overall market and the latter with the specific, current customer base.

To identify relevant literature, we used four prominent academic databases: Web of Science (WoS), Emerald Insight, ACM Digital Library, and ABI/INFORM Collection (ProQuest). WoS is the most comprehensive generic database, encompassing more than 12,000 high-impact journals and research articles from more than 3,300 publishers. The Emerald Insight and ABI/INFORM Collection (ProQuest) are also similar—as generic databases, they offer a collection of many relevant journals and scientific articles. The ACM Digital Library is a specialized database focusing on technical disciplines and thus helps uncover the articles focused on technical aspects of customer segmentation. Combined, these four databases offer balanced coverage of the existing literature on customer segmentation from multiple scientific disciplines.

We conducted detailed searches in each of the four databases. We did not want to pre-limit the searches with highly specific keywords and narrow terms, which may exclude crucial articles, as those articles may address the very same topic but use different terms. Rather, to identify a wide range of publications that may shed light on customer segmentation, we used only the following keywords: “customer segmentation*”, “user segmentation*”, and “audience segmentation*” (‘*’ denotes plural forms) and identified all the associated articles. However, in the case of WoS, after the initial search, which resulted in 574 entries, we kept articles from the fields of business and computer science (as categorized by WoS) and excluded articles from other fields that we deemed outside the scope of this study. These fields included, for example, materials science, physics, chemistry, or integrative complementary medicine. The specific search details, along with the assessment of the evidence base, are presented in Table 1 .

Assessment of the evidence base

We carried out the article screening in two stages. In the first stage, we focused on “hygiene factors” (see Table 2 ), such as removing duplicates, articles with missing information, those written in a language other than English, and articles published before the year 2000, as we considered the past 20 years or so to contain most relevant work for our research purpose. Figure  2 illustrates the general interest in segmentation among studies in or related to marketing.

Approximate search results when searching Google Scholar for [+ segmentation + marketing]. The results indicate a general increase in interest in segmentation among studies in or related to marketing. The vertical line (the year 2000) indicates our sample’s cut-off year

After the first round of screening, we checked the remaining 204 articles based on their contents, i.e., assessing their relevance to our research purpose. We found that 32 (15.7%) articles were irrelevant to our research purpose (e.g., they were literature reviews or did not use empirical data to conduct customer segmentation). This reduction left us with 172 (84.3%) articles, 134 (77.9%) of which contained algorithm-based approaches to customer segmentation (i.e., representing ACS), while 38 (22.1%) articles applied non-algorithm-based customer segmentation (see Fig.  3 ). In our analysis, for RQ1-2 and RQ4-RQ5, we focus on the 134 articles that were algorithm-based. For RQ3 and RQ6, we focus on the full 172 articles that passed the pre-screening as these RQs do not require the articles to be algorithm-based.

Research process leading to article coding. Essentially, this took place in four stages: Screening, Relevance Assessment, Algorithm Assessment, and Data Extraction

Article coding

After the screening, we extracted information from the articles to address our RQs using a data extraction sheet. The information fields were designed to correspond with the RQs (see Table 3 ). The coding was carried out by one researcher, with another researcher verifying the quality of the coding outcomes by randomly investigating a sample of 20 articles. This inspection was carried out successfully and revealed that the data was coded following the guidelines in Table 3 . The results (see Section " RQ2: does customer segmentation typically use one or many algorithms? ") were obtained by carefully reviewing the full-text articles.

RQ1: What algorithms are typically used for customer segmentation?

After a thorough overview of the ACS literature, we identified 46 different algorithms used for customer segmentation , whose usage frequencies are illustrated in Fig.  4 .

Frequency chart for the algorithms used. The y -axis represents the name of the algorithms used for customer segmentation, and the x-axis shows the frequency, i.e., the number of times each algorithm has been used in the literature for customer segmentation. The ‘Other’ class contains all the algorithms that were used only once

As can be seen in Fig.  4 , K-means clustering is the most frequently used algorithm, as it is used 27 times (20.1%) in our sample of reviewed literature. Other prominent algorithms include variants of K-means clustering with a frequency of 10 (7.5%), fuzzy algorithms, and latent class analysis models are all used 8 (6.0%) times, respectively. The recency-frequency-monetary gain (RFM) and its variants have been used 6 (4.5%) times, while Self-Organizing Maps (SOM) and Genetic Algorithms (GA) have been used three times (2.25%) each. Other algorithms, for example, the Louvain algorithm, Ward’s algorithm, and hierarchical clustering algorithms have been used two to three times varyingly. Furthermore, some algorithms have only been used once, indicating that these approaches may not have been thoroughly explored in the context of customer segmentation. Examples include direct grouping iterative merge and consistency-based clustering algorithms, suggesting that there is scope for further nuanced research in these areas.

Overall, the results highlight the wide range of algorithms available for customer segmentation and the need for further exploration and comparison of these methods to determine the most effective approach for different business scenarios.

RQ2: Does customer segmentation typically use one or many algorithms?

After a thorough overview of the ACS literature, it appears that in most cases, researchers have utilized one algorithm for customer segmentation (i.e., in roughly 80% of them). However, there are also instances where multiple algorithms have been combined for more effective results. For example, K-means clustering, SOM, and RFM approaches have been applied in combination with other approaches (see Table 4 ).

In general, the employment of multiple algorithms in combination may aid in addressing the shortcomings specific to each algorithm and may also help in the creation of more robust and distinct customer segments. Furthermore, technical reasons hailing from the domain of applied ML can explain the use of multiple algorithms. It is expected that AI/ML research studies compare and evaluate multiple algorithms for one task; this involves experimenting with combinations of different algorithms and calculating the accuracy/performance among them. This is often done by conducting an ablation study (Symeonidis et al. 2018 ). An ablation study is a form of experimental design used to study the effect of removing a specific part or feature of a model on its overall performance, typically used in the field of ML and AI. It can involve removing individual components of a larger model or system, a subset of data features, or hyperparameters to see how they affect the accuracy, cost, and other metrics of the ML model. Similarly, the researchers developing new customer segmentation algorithms are required to demonstrate the value of their approach; for this reason, algorithms are often compared against one another.

RQ3: How many customer segments are typically created?

Our analyses show that various numbers of customer segments have been suggested/created by researchers based on the application area and/or target market and objectives of the research (see Fig.  5 ). Most commonly, researchers have suggested/created four customer segments ( n  = 32, 21.2%). On average, researchers created 5.7 segments (SD = 3.9). Interestingly, no study produced more than 20 customer segments (apart from the outlier study mentioned in Fig.  1 caption), while the lowest number of segments produced was one (in this particular study, the researchers applied a decision-rule algorithm to discover the most ideal customer type (Lee, J. H. & Park, 2005)). Interestingly, more than half ( n  = 103, 68.2%) of the studies generated between two and five segments (see Fig.  5 ). Furthermore, 92.1% of the studies generated ten or fewer segments.

Number of customer segments created in research articles. The numbers are based on 151 articles (87.7% of the total 172) that expressed the number of segments created. In the case the researchers presented multiple segmentations with different numbers (e.g., 7, 7, 8), as was sometimes the case when experimenting with multiple algorithms or datasets, we have taken the average of the reported numbers and rounded it either up or down based on standard rounding rules (e.g., 7, 7, 8 would yield the average of 7.3 which rounds to the segment size of 7). Twenty-one articles (12.2%) did not report the segment size. Also, we omitted one outlier article from the analysis, as the researchers created 1209 and 8984 segments from two datasets, respectively (Böttcher et al. 2009 )

Typically, the number of segments is not determined beforehand but in a data-driven way (Hiziroglu 2013 ; Hong and Kim 2012 ), which means determining the segment size based on quantitative evaluation metrics (i.e., the number of segments is such that best fits the data based on an evaluation metric when experimentally varying the segment size). For example, the researchers may attempt multiple numbers for k where k indicates the number of segments, and then visualize at what number of k the obtained information noticeably decreases—the so-called elbow method where elbow indicates this decrease (Syakur et al. 2018 ). So, the number of clusters is neither random nor determined a priori, but all the reviewed articles used some quantitative metrics/criteria to validate or choose the number of segments.

RQ4: How is customer segmentation typically evaluated?

In ACS, the evaluation of the customer segments is crucial for the effectiveness of the segmentation process. As ACS tends to be an unsupervised ML task (where there is no single “correct” value for a segment, but instead, the algorithm aims to organize the data into groups, i.e., segments), accuracy is used more rarely in ACS than in other types of ML tasks, including supervised learning tasks such as prediction. Hence, there are several measures and criteria that researchers have used to evaluate the quality of customer segments. Overall, we identified 14 different metrics for evaluating customer segmentation outputs, of which six (42.9%) focused on statistical indicators and eight (57.1%) focused on distances and/or similarity calculation (see Table 5 ). We discuss the metrics in alphabetical order.

Accuracy (ACC) is used to evaluate the quality of segments, i.e., how well “unseen” or new members (customers) the segmentation algorithm can allocate to the correct segment (Wu, S. et al., 2021). ACC is typically calculated by dividing the number of correct values by the total number of predicted values. The use of ACC requires that there are ‘ground truth’ values or labels against which the predictive ability of an algorithm is compared.

The Adjusted Rand Index (ARI) measures the similarity between two segmentations (Xu, X. et al., 2007). The ARI considers the random chance that objects between the segmentations might be similar. It compares object pairs in two sets of segments and computes the difference between the observed agreement and the expected agreement under random labeling. The value of -1 indicates perfect dissimilarity, and + 1 indicates perfect similarity. So, the higher the ARI, the more similar the two customer segmentations are.

The Analysis of Variance (ANOVA) test is a statistical method used to test the difference in means between two or more groups. In customer segmentation, ANOVA can test for significant numerical differences in customer characteristics between the segments (Ballestar et al. 2018 ; Kashwan and Velu 2013 ).

The Average Clustering Error (ACE) evaluates the average distance between data points within each segment (Manjunath and Kashef 2021 ). The ACE is calculated by taking the average of the sum of the distances from each point in the dataset to its closest segment center. It indicates how well the segmentation algorithm has been able to group points with similar characteristics. If the ACE is low, this indicates an effective segmentation algorithm.

The Bayesian Information Criterion (BIC) is a measure used for model selection and comparison, including in segmentation (Bhade et al. 2018 ). To calculate the BIC, the log-likelihood of the segmentation model is adjusted by penalizing complex models. The BIC score is obtained by subtracting the penalty term from the maximum log-likelihood, where a lower BIC value indicates a better-fitting and more parsimonious model.

The Calinski-Harabasz Index (CHI) evaluates the separation between the segments and the compactness within each segment (Kandeil et al. 2014 ). The CHI is calculated by comparing the within-segment dispersion to the between-segment dispersion. A higher value indicates that the segments are well-defined and that the dataset has been well-split into distinct segments.

The Davies Bouldin Index (DBI) assesses the similarity between the segments based on the distance between their centroids (i.e., midpoints). The lower the DBI score, the better the segmentation result, indicating that the segments are more compact and less scattered (Aryuni et al. 2018 ). The DBI penalizes algorithms that produce segments with a wide variety of sizes and shapes, with larger diameters relative to the separation between the segments.

The Dunn Index (DI) evaluates the separation between the segments and the compactness within each segment (Khajvand and Tarokh 2011 ). The calculation of the DI is done by taking the ratio of the minimum intra-segment distance to the maximum inter-segment distance. A higher value indicates that the segments are better separated.

The Fukuyama and Sugeno method (FS) is an approach to evaluate segmentation results based on fuzzy sets and fuzzy logic. The FS involves assigning membership values to data points indicating their degree of belongingness to each segment. The process incorporates expert knowledge through the formulation of fuzzy rules, which guide the decision-making process. These membership values are then used to calculate a validity index, which measures the quality of the segmentation by considering their compactness and separation (Nemati et al. 2018 ).

The Mann–Whitney rank test (MW) is a non-parametric statistical test used to compare the difference in the median between two groups. In customer segmentation, the MW can be used to test whether there are significant differences in customer characteristics between the different segments (Jiang and Tuzhilin 2009 ).

The Silhouette Index (SI) is a metric used to measure how well-defined a segment is and how strongly a data point is assigned to its associated segment (Dzulhaq et al. 2019 ). The SI ranges from − 1 to 1, with a score of 1 indicating that the data point is perfectly matched to its own segment, a score of − 1 indicating that the data point is more closely associated with another segment, and a score close to 0 indicating that the data point does not have a clear segment assignment. The SI is calculated by taking the average of the difference between the data point’s own segment similarity and the lowest segment similarity with the other segments.

The Total Clustering Effectiveness (TCE) is a metric used by Lu and Wu ( 2009 ). The TCE combines an inter-cluster correlation indicator and an inner density indicator. The numerator represents the sum of densities for two segments, where a higher value indicates better performance. The denominator represents the correlation coefficient between the two segments, with a smaller value indicating better results. Incorporating both values, a higher TCE indicates better results.

The Validity Index (VI) is a measure that evaluates the quality of the segmentation result based on specific criteria (Pramono et al. 2019 ). These criteria can include factors such as intra-segment cohesion (compactness), inter-segment separation, or the overall structure of the segments. VIs provide a numerical score or value that indicates the goodness of fit of the segmentation solution, with higher values suggesting better quality or validity. Different VIs employ distinct formulas or methodologies to capture different aspects of segmentation performance.

Finally, the Xie-Beni Index (XBI) is a validity index used to evaluate the quality of segmentation results (Munusamy and Murugesan 2020 ). The XBI quantifies the trade-off between segment compactness and separation by calculating the ratio of the sum of squared distances between data points and segment centroids to the product of the segment compactness and the number of data points. A lower value indicates better segmentation with tighter and well-separated segments.

A few conclusions can be made. First, the metrics can be divided into statistics- and separation-focused metrics, with the latter being slightly more typical. Second, statistics-focused metrics emphasize segment-to-segment differences, while separation-focused metrics emphasize low intra-segment distance (i.e., compactness) and high inter-segment distance (i.e., separation). Third, customer segmentation evaluation is centered on using metrics derived from clustering practices, rather than using metrics especially tailored to customer segmentation, business outcomes, or ecological validity.

RQ5: What hyperparameters are used for algorithmic customer segmentation?

In ML, a hyperparameter refers to a configuration setting (i.e., a numeric value) that is external to the model itself and is typically set before the learning process begins (Jansen et al. 2021 ). Unlike model parameters, which are learned from the training data, hyperparameters are predefined choices (range of values when experimenting with multiple hyperparameter values) that affect the model’s performance and behavior. These parameters can include things like the learning rate, the number of hidden layers in a neural network, or the regularization parameter. Selecting appropriate hyperparameter values is crucial for achieving optimal model performance, and it often involves experimentation, trial and error, or using techniques like grid search or Bayesian optimization (Jansen et al. 2021 ).

In our review, out of the 169 studies that offered information about hyperparameters, more than four out of every five articles ( n  = 138, 81.7%) applied only segment size as a hyperparameter, while less than one out of five ( n  = 31, 18.3%) applied additional hyperparameters. In ACS, the algorithms may combine technical and business hyperparameters, with the technical parameters stemming from the inputs required by the algorithm (most commonly, the number of segments to create, i.e., the segment size and the distance measure—how the distance between the segments is calculated) to perform its computation and the latter arising from the particular business scenario the segmentation aims to address. For example, Munusamy and Murugesan ( 2020 ) performed customer segmentation based on the Fuzzy C-mean clustering algorithm and defined U matrix parameters to make their data compatible with the Fuzzy C-mean algorithm.

In contrast, Peker et al. ( 2017 ) formulated a hybrid approach for customer behavior prediction and used many AI/ML algorithms, including Neural Networks, SVM, Decision Tree, and Radial Basis Functions that make use of different hyperparameters, for instance, cost, gamma, number of hidden layers, weights, number of leaf nodes and number of trees, etc. In another contribution, the authors formulated a hybrid big data model for analyzing customer patterns in an integrated supply chain network (Wang et al. 2020 ). They applied Linked Based Bloom Filters (LBF) that served as parameter functions directly linked with customer segmentation. Liu et al. ( 2009 ) formulated a hybrid approach for a product recommendation that directly relates to customer segmentation. Their proposed approach used learning rate, grid structure, and distance normalization as hyperparameters.

Although these parameters are not directly linked with customer segmentation, they contribute to the overall segmentation process in terms of providing a method for ascertaining a technically optimal number (and structure) of the segments. In turn, the business parameters aim to provide more information about the domain-specific business context. These may include the likes of Length of customer involvement (L) and Periodicity (P) that were applied by Nemati et al. ( 2018 ). Zhu et al. ( 2015 ) applied Profitability ( prof ), Accuracy ( acc ), and lead time as hyperparameters that contribute to customer segmentation in demand fulfillment of customers in case of supply shortage. Wu and Liu ( 2020 ) incorporated group preferences and linguistics parameters into their Type 2 fuzzy customer segmentation models and concluded that these parameters greatly affect the customer segmentation task.

Overall, the hyperparameters applied by most articles stem from the standard/default hyperparameters used by these algorithms in any dimensionality reduction context, of which customer segmentation is a special case.

RQ6: How frequently are subject matter experts used for evaluating the customer segments?

In this study, we encountered only seven cases (4.1%) where subject matter experts were used to evaluate the quality of customer segmentation and provide expert opinions. In other words, it is a rare, perhaps too rare, practice to invite stakeholders and subject matter experts to validate the results of the customer segmentation process in academic research articles. Out of the rare examples that do exist, Nemati et al. ( 2018 ) formulated a customer lifetime value (CLV) approach for prioritizing marketing strategies in the telecom industry. The experts were first asked through a questionnaire to provide different parameters for the said tasks. Once the segmentation was done, the experts were again consulted to evaluate and validate the results. Safari et al. ( 2016 ) formulated an RFM-based CLV determination approach that performs customer segmentation based on the RFM values. To do so, subject matter experts were asked through a questionnaire, and once the segmentation was carried out, a total number of 16 experts expressed their opinions about the accuracy of the segments.

Similarly, Sun et al. ( 2021 ) introduced a heuristic approach to customer segmentation. The experimental results show that the customer segmentation output by their proposed method was consistent with the customer segmentation result given by experts. Manidatta et al. ( 2021 ) introduced an integrated approach for customer segmentation and evaluated their approach through experiments. They collected responses from nine subject matter experts from the Indian retail industry regarding their perception of the relative importance of four CLV criteria and evaluated the weights of each criterion using fuzzy AHP. Transaction data for 18 months was analyzed to segment 1,600 customers into eight segments using the fuzzy c-means clustering analysis technique. The segmentation results of their proposed integrated method were further validated by the nine experts from the Indian retail industry.

In another study, Li et al. ( 2011 ) formulated an agglomerative clustering-based approach for customer segmentation, and as a result of their proposed approach, the customer was segmented into four distinct groups/segments. Subject experts validated and evaluated the experimental results for customer segmentation by their approach (Li et al. 2011 ). Lee and Cho ( 2021 ) formulated a customer segmentation approach based on the Leuven algorithm. To verify and validate the segmentation results of their proposed approach, they consulted a subject matter expert, and the algorithm determined the modularity for ten segments, which was the same number of segments identified by the domain expert. Warner ( 2019 ) conducted a study on audience segmentation using a survey approach. Before conducting the study, a seven-member expert panel was asked to review the instrument and provide their expert opinion on the number of segments created. The domain expert team validated the audience segmentation results.

Thus, our analysis indicates that most often, the results of customer segmentation (including both algorithmic and non-algorithmic customer segmentation research) are not validated using external feedback, but the authors tend to rely on technical evaluation metrics to justify the quality of their work. This practice likely stems from the ML research tradition, in which metrics such as accuracy, precision, recall, etc., are used to evaluate the performance of an algorithm (Bell 2014 ; Kühl et al. 2022 ), rather than “subjective” human feedback. However, when they are used, most typically, multiple subject matter experts participate in evaluating the created segments.

Study highlights

Here, we discuss the highlights of our findings.

We identified 46 different algorithms applied by researchers for customer segmentation . This finding highlights not only the methodological (algorithmic) plurality within customer segmentation studies but also the influence of the current AI and ML technologies in this domain, as these algorithms overwhelmingly stem from the ML research tradition.

Most of the reviewed studies used one segmentation algorithm, making multi-algorithm customer segmentation a minority endeavor . The promise in multi-algorithms customer segmentation is that, in theory, it is better able to handle the plurality of segmentation criteria and be more responsive to organizational requirements for changing the segmentation parameters with updating business requirements.

In ACS, the number of segments is not pre-assigned, but it is inductively determined based on quantitative evaluation metrics. On average, researchers create 5.7 customer segments per study (SD  =  3.9, Mode  =  4, Median  =  5) . So, even though creating four segments is the most common, the number of segments created varies substantially across the reviewed studies. No analyzed study created more than 20 segments (Min = 1, Max = 20).

Few studies explicitly define the concept of customer segmentation . Instead, the concept is often treated implicitly, as “everyone knows what it is”. This conceptual vagueness can hinder the development of scientific advances in customer segmentation, as ‘customer segmentation’ might not be a similar task to other clustering tasks [the (dis)similarity of customer segmentation to other clustering tasks remains unaddressed in the literature].

Research outlines numerous ‘theoretical’ use cases and benefits for customer segmentation . These benefits, ranging from pricing to targeting and personalization of offerings and messaging, emphasize the central role of customer segmentation as a key business application of AI and ML technologies. Concurrently, few studies show empirical evidence of these benefits in organizational use cases or systems (i.e., ecological validity).

There is no one set of customer segmentation criteria, but the studies vastly vary in terms of the segmentation criteria applied . In fact, no two studies may have a single criterion (i.e., customer attribute) in common. This plurality of criteria partially explains the algorithmic or methodological plurality, as different criteria represent different data types that require distinct preprocessing and analysis approaches to apply the algorithms. Therefore, it is unlikely that we would end up in a situation where only one (or even a few) algorithms would cover all use cases for customer segmentation.

Researchers developing novel customer segmentation algorithms tend to see customer segmentation as a computational task . This viewpoint is visible in how algorithms are used, studies are structured, and outputs are evaluated. Machine learning studies follow a particular paradigm of benchmark comparison, which may explain why a large portion of literature puts less emphasis on conceptual and theoretical aspects of customer segmentation and instead focuses on it as a technical problem or ‘task’.

We identified 14 unique evaluation metrics for the quality of customer segmentation, all technical . The many metrics are the consequence of multiple algorithms: because the statistical and mathematical properties of different algorithms vary, one metric cannot be applied to evaluate the (internal) success of the modeling task. However, it would be valuable to have more centralized evaluation metrics for customer segmentation success; as the internal evaluation is affected by computational specificities, perhaps researchers could shift their focus on external (ecological) evaluation metrics, focusing on the business outcomes and the customer dynamics of applying customer segmentation rather than the segments’ creation process.

Business orientation separates customer segmentation from other clustering tasks . In addition to ML-dependent technical hyperparameters (e.g., number of segments, distance function), researchers utilize business-specific hyperparameters (e.g., length of customer involvement, profitability) for customer segmentation inputs. However, most studies reviewed (82%) only applied segment size as the hyperparameter.

Considering the historical development of the use of clustering in marketing, the post-2000 sample we analyzed shows some progress compared to the previous analysis made by Punj and Stewart in 1983. Specifically, the researchers indicated then that there is a “failure of numerous authors in the marketing literature to specify what clustering method is being used.” (p. 134). In our more recent sample, this condition does not take place as the authors are more explicit on the precise method being used—so, there has been progress in the reporting of clustering details in marketing work.

However, our results confirm the fundamental challenges of clustering (segmentation) as stated by Punj and Stewart ( 1983 ): “choice of an appropriate metric, selection of variables, cross- validation, and external validation” (p. 134). As then, these challenges remain topical and fundamentally unresolved. There are many metrics to choose from. The selection of segmentation variables is arbitrary. Cross-validation and external validation (ecological validity) are difficult to execute and thus often omitted. So, the fundamental nature of segmentation has not changed with the novel AI technologies, at least yet.

Interestingly, there can be seen as a continuation of knowledge. That is, the analysis by Punj and Stewart from 1983 (see their Table 4 on pp. 141–142) indicates K-Means as one of the most popular algorithms. Forty years later, this algorithm still maintains its position as the leading segmentation algorithm. We can interpret this finding as either proof of its superiority in this problem, or as traditionalism. However, either way, the conclusion remains that the novel AI-based approaches have not been able to replace the “old AI” approaches, at least when it comes to K-Means.

In addition to the above highlights, in the following subsection, we provide a taxonomy of algorithms for customer segmentation.

Central goals and directions for future research

From our review, there are multiple avenues for future research to advance customer segmentation research. In the following, we outline seven key goals (KG) for future work:

KG01: Providing taxonomies of algorithms and metrics. There is a need for conceptual frameworks, classifications, and taxonomies that help address the undeniable plurality of algorithms in the domain of customer segmentation research, which includes at least (a) algorithm selection plurality, (b) segmentation criteria plurality, (c) hyperparameter plurality, and (d) evaluation metric plurality. Our taxonomy of algorithms for customer segmentation provides a starting point and an example of outputs that can help address this gap. We invite other researchers to provide conceptual work (not only empirical!) that systemically categorizes the extant work on customer segmentation. Additionally, consensus on some fundamental concepts is much needed—for instance, how can ‘high-quality’ customer segmentation be distinguished from ‘low-quality’ customer segmentation? Should we focus on the quality of the process, the quality of the evaluation metrics, or the quality of the actual customer segments? Propositions (or even discussions) concerning these matters are direly needed.

KG02: Providing empirical evidence on customer segmentation outcomes. Based on our literature review, we observed that few studies provide an empirical analysis of the actual application of ACS in organizations. Algorithmic studies tend to stop at the stage of creating the customer segments; their application in companies is not explored. On the one hand, this casts shadows on whether the potential benefits of customer segmentation mentioned in the studies are rather hypothetical, or whether they can be backed up with empirical evidence. Therefore, we encourage researchers to shift their focus from creating segments to applying them in firms and other organizations. This not only represents an exciting research gap but efforts in this regard can help further enhance and incentivize research projects on customer segmentation, as they would be more strongly linked to key performance metrics that firms and other organizations value. To this end, case studies, field studies, A/B tests, experiments, and longitudinal studies would be welcome to address this vast knowledge gap.

KG03: Integrating algorithms into customer segmentation systems. One central direction for segmentation research is developing more comprehensive pipelines that can handle multiple different data types (i.e., customer segmentation criteria of different types) and changing business requirements. A key direction in this regard would be merging customer segmentation research with intelligent systems research (i.e., research that focuses on developing systems that can think, reason, and make decisions independently, without human intervention, interacting with the environment and making decisions to optimize quantitative outcomes (Bauer and Dey 2016 )) to generate and empirically investigate more comprehensive customer segmentation systems that stakeholders can interact with, not merely isolated attempts of testing how ‘Algorithm X’ fares with the customer segmentation task. Again, this corresponds to our postulation that, based on our review, customer segmentation research would benefit from a higher degree of ambition and scope, as the current body of work focuses on developing and testing algorithms instead of systems.

KG04: Proposing a standardized framework for evaluation. Due to methodological plurality, there is a lack of consensus on what constitutes ‘quality’ in customer segmentation. However, the consensus from prior research indicates that quality is perceived as models’ internal consistency and evaluated using technical performance metrics focused on this internal consistency. ACS has inherited its evaluation metrics from the ML research tradition, essentially adopting the metrics used in other clustering and data dimensionality tasks to customer segmentation as well. While we do not deny the merits of these technical metrics—they provide useful information about the model’s fit with the customer dataset—we call for further extensions and contributions to broaden the hierarchy of evaluating customer segmentation outputs. In addition to technical metrics, we ought to consider other metrics as well, such as stakeholder perception (“Are these segments useful? How useful?”) and organizational outcomes (e.g., the “ROI of customer segmentation”, i.e., how much does the implementation of customer segmentation improve the profit of the company?). A more holistic and nuanced, hierarchical way of measuring the quality of customer segmentation would further develop the field in academic and practical circles.

KG05: Exploring organizational challenges of customer segmentation. Challenges in customer segmentation arise, on the one hand, from organizational realities such as culture, capabilities, and individual experience and, on the other hand, from technical rationale such as data availability, selection of algorithms, and validating the quality of customer segmentation. The organizational aspect of applying customer segmentation in decision-making is a clear and present research gap. Again, qualitative studies can help generate rich insights into how customer segmentation algorithms transit into organizational adoption. So, we need a more collaborative approach to customer segmentation research. This means not giving up on developing better algorithms, but in addition to that, engaging with social science researchers in a pursuit to discover the impact of applying these algorithms in the real world. Such extension presents an exciting new field of study.

KG06: Providing more critical analyses. Researchers typically do not discuss the fundamental limitations of using AI and ML in customer segmentation, such as the fact that clustering algorithms were not originally developed for customer segmentation. Questions such as “What are the downsides of using AI for customer segmentation?” are not asked. Yet, they should be asked, as critical analyses can reveal insights into transformative improvements in this field. As a starting point for such analyses, we offer some ideas: first, the use of algorithms may distance the stakeholders from the data, especially if the creation of the segments is not a participatory process but outsourced to a group of analysts/data scientists who will not be part of the eventual use of the segments. So, there can be a problem of detachment and silos. Second, algorithms may cause segmentation to become overly rigid and resistant to change, so the same criteria used to group customers may not be relevant after a certain amount of time. Third, customers may resent being “put into a box” and may not appreciate feeling like just another anonymous statistic; thus, customer perceptions regarding customer segmentation could be explored. These and other negative aspects of ACS should be studied much more.

KG07: Making the role of humans explicit. Currently, humans play a central role in ACS; they select the algorithms and evaluation metrics, program the experiments, interpret the results, and choose final hyperparameter combinations. They also evaluate the results and make the final judgment of whether an algorithm did a good job at the segmentation. Finally, humans apply the segments in practice and make decisions based on them. Yet, we speak of ‘algorithmic customer segmentation’ and seem to delegate a lot of responsibility for the segmentation process to algorithms, often obfuscating the role of humans. As the field of customer segmentation is increasingly reliant on AI to do this work, a vital question is, what can AI learn from non-AI-reliant customer segmentation? In other words, there is a need to better understand human factors in the segmentation process and how these can support or bias the process when co-existing with algorithms.

Practical implications

There are three main practical implications (PI) for researchers and practitioners:

PI01: Segmenting Beyond K-means : Although nearly fifty algorithms were identified, k-means or a derivative is by far the most popular (27.6%) algorithmic approach for customer segmentation. This situation presents an opportunity and a need for exploring and comparing these algorithmic approaches to determine the most effective approach for different business scenarios—earlier research has partially done this (Punj and Stewart 1983 ), but not in a completely systematic way and not considering business scenarios. To do this comparing, it would entail both algorithmic investigation and business context research to identify characteristics for different business scenarios and how these relate to the selection of the best scenarios to address such analysis.

PI02: Sensemaking of the Segments : In addition to investigating approach algorithms given the attributes of business scenarios, there is a general lack of evaluation of the resulting segment, with most of the evaluations done using technical measures related to the chosen algorithm. However, given the algorithmic plurality, this results in a plurality of evaluation techniques as well. This calls for standard evaluation criteria for customer segments extending across the various algorithms. Finally, there is a critical and unmet need for evaluating segmentation beyond the algorithmic nuances, using broader criteria of accuracy, fairness, diversity, or coverage. For these, investigations of segmentation hyperparameters would most likely be needed.

PI03: Finding the “X” in [Segmentation + X] : The richness of segmentation criteria implies a nearly infinite number of ways of dividing a customer population into segments and these segments into sub-segments. Also, the segmentation criteria can be modified by adding or removing specific criteria at any time. Each of these segmentations offers one view of the segmentation that most likely has many possible views. So, which one is correct? This question is probably impossible without an ‘X’, i.e., some criteria external to the data for which to evaluate the segmentation. For this, the segmentation results need to be placed within the given business scenario, as discussed above, and the segments validated using external feedback, organizational key performance indicators, or achievement of business scenario goals.

Overall, customer segmentation, especially when applying AI and ML algorithms, is a socio-technical problem. In other words, both ‘technical’ (algorithm choice, data availability) and ‘social’ aspects (culture, goals) affect the success of customer segmentation projects. Therefore, mere technical solutions or more sophisticated algorithms are not adequate for ensuring successful customer segmentation projects. Thus, it is vital to understand customer segmentation projects as long-term processes that require stakeholder buy-in and effective implementation plans (i.e., the segmentation process does not end with creating the segments but only begins).

The current body of ACS literature focuses on the technical application of algorithms but largely omits the role of humans in this process, whether the role deals with various aspects of using the AI/ML algorithms (i.e., using judgment for the hyperparameter selection), evaluating the results, and eventually applying the results of ACS. Given the predominant focus on technical metrics and algorithms, there is a need to go beyond these aspects into the realm of inspecting the technology’s impact on actual organizations. According to our understanding, this can best be achieved by cross-disciplinary collaboration with social scientists, marketers, and other stakeholders who understand the qualitative side of customer segmentation and have access to organizational performance data. So, while this step of expanding ACS research into the realm of application is likely to involve a certain exit from the ML paradigm’s “comfort zone”, it is a necessary step to establish true scientific progress in this domain. Fernández-Delgado et al. ( 2014 ) famously asked in the context of classification, “Do we need hundreds of classifiers to solve real world classification problems?” (cited 3594 times at the time of writing this); we can paraphrase this question: Do we really need 46 different customer segmentation algorithms? Given the discrepancy between the large number of algorithms and the scarce number of articles applying subject matter experts for the evaluation of successful customer segmentation, the answer might be negative.

Study limitations

We did not include articles published before the year 2000 in our sample. We did not include keywords dealing with market segmentation or consumer segmentation (e.g., Kamakura and Russell 1989 )—conceptually, these are different goals, as ‘market’ includes non-customers as well. However, it could be interesting to compare the methods and variables used in these different segmentation tasks. We leave this for future work.

Customer segmentation has been a major focus in academic literature for many years and continues to be one. It is also of high value to marketers in industry. However, due to a myriad of different approaches, the field suffers from the lack of clarity that we aimed to address with this study. We found that researchers have used 46 different algorithms for customer segmentation. Interestingly, around 80% of them utilized a single algorithm for this purpose. On average, they created about 5.7 customer segments, deciding the exact number inductively based on quantitative evaluation metrics. Surprisingly, few articles offer empirical evidence of the benefits of customer segmentation. Our results point the way for future research, as addressing the proposed key goals helps successfully develop customer segmentation algorithms, make sense of the customer segments, and evaluate the impact of the segmentation.

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Salminen, J., Mustak, M., Sufyan, M. et al. How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation. J Market Anal 11 , 677–692 (2023). https://doi.org/10.1057/s41270-023-00235-5

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Customer Segmentation: Types, Examples And Case Studies

Customer segmentation is a marketing method that divides the customers in sub-groups, that share similar characteristics. Thus, product, marketing and engineering teams can center the strategy from go-to-market to product development and communication around each sub-group. Customer segments can be broken down is several ways, such as demographics, geography, psychographics and more.

Table of Contents

Why customer segmentation matters

No matter how niche your brand may be, it is important to keep in mind that every customer is, in fact, an individual. What’s more, they deserve to be treated as such.

Of course, most businesses will not have the resources to cater to every customer on an individual basis.

They can, however, more broadly assess the needs of their customers according to certain metrics.

Customer segmentation in a nutshell

Customer segmentation is the process of separating your customers into groups according to certain traits (e.g. personality or interests) and factors (age or income level). 

So why should customers be segmented? There are several important reasons:

• It allows businesses to tailor marketing strategies and ad campaigns according to particular groups of people.
• It enables businesses to learn about their consumers on a deeper level. And with this increased understanding, to create better products that resonate with consumer needs.
• Enhanced customer support – since businesses with customer segments are better able to predict problems ahead of time.
• Conversely, segmentation may also identify groups of consumers previously unknown to the business – allowing marketing resources to be directed toward these untapped groups.

Now that we have a basic understanding of customer segmentation and why it should be implemented, let’s look at some common customer segment types.

Demographics 

Demographic data is relatively straightforward and includes information on age, gender, marital status, income, and education level.

It is perhaps the most well-known and well utilized of all customer segments because demographic data is easy to obtain through market research.

A simple example of demographic customer segmentation might involve the marketing of a high-end sports car.

The manufacturer may want to target consumers that are unmarried or divorced, have a high income, and be at or approaching retirement age.

While the above examples deal with business-to-consumer marketing , demographic segmentation can also be used in business-to-business marketing .

In this case, businesses may target the industry, job function, or company size as part of their marketing efforts.

Geographical segments detail such parameters as climate, zip code, land use (urban or rural), and the radius around a particular point of interest. But it also concerns the scope and extent of potential marketing efforts.

Smaller organizations, for example, may target consumers living in specific towns or cities. Larger organizations may target consumers according to their country or continent of residence.

If we return to the sports car example, let’s assume that the car is marketed primarily as a convertible.

As a result, the manufacturer may choose to target specific countries (or geographic areas) with sunny climates that are conducive to driving with the top down, so to speak. 

Public transport operators could also use geographic segments to target commuters living within 15 minutes of a train station.

They could use this information to develop a marketing campaign to convince commuters to leave the car at home and take the train instead.

Psychographics 

Psychographic segments include such things as socioeconomic class, lifestyle, and personality traits.

They also include factors that are big drivers of buying decisions, such as values, motivations, attitudes, and conscious or subconscious beliefs. 

However, psychographic data is more difficult to collect than demographic data. Why? Because it is more subjective and requires deeper research to unearth.

Psychographic segments and the information that comprises them are also more fluid because motivations, beliefs and values can change over time.

The luxury sports car manufacturer may target consumers whose values and motivations relate to status, freedom, and fine craftsmanship.

But if, for example, the consumer who bought a 2-seater convertible suddenly welcomed grandchildren into his life, he may then prioritize safety and reliability over status and freedom.

Of course, marketing departments cannot plan for every contingency. But they must be aware that psychographic customer segmentation is fluid and has the potential to shift over time.

Behavioural 

Behavioral segments include a consumer’s direct interactions with a business.

In other words, behavior dictates how they act according to their demographic and psychographic attributes. 

The behavioral segment encompasses spending habits, product/service usage, and the perceived or actual benefits of such usage.

Behavioral segments are derived from internal data that is collected by the business itself.

It may include data on how consumers use a product and the frequency with which they do so.

Furthermore, information may also include the specific benefits that the consumer is after, such as a time or money saving or loyalty status. 

Perhaps most importantly, behavioral segments clarify a consumer’s willingness to purchase.

If a typical sports-car driver likes to upgrade to the new model every three years, then it is the marketing team’s priority to understand this cycle and market to this segment accordingly.

Similar predictive behavioral learning is also utilized by Netflix, who segment their users according to their content preferences and then recommend content in similar genres.

Technographic 

Technographic segmentation is segmentation according to a consumer’s preferred choice of technology.

Think smartphones, software, operating systems, desktops, and apps.

As technology becomes increasingly prevalent in the lives of consumers, technographic segmentation has never been more important to marketing departments. 

Business-to-consumer marketing can also use technographic segmentation to target consumers according to their social media use.

In their Harvard Business School published book Groundswell , authors Li and Bernoff suggest that marketing teams further divide their technographic segments according to social media use.

Each “sub-division” requires a different marketing strategy . Some of the more common sub-divisions include:

• Creators – who maintain a blog or website or upload music or videos.
• Critics – who post reviews of products or services or who like to contribute to forums or blog posts.
• Joiners – who maintain active social media accounts.
• Spectators – who read blogs, listen to podcasts, or watch video content without contributing or participating. 

Business to business (B2B) also stands to benefit by technographic segmentation. Specific parameters in the B2B sphere include network and storage capabilities, cloud utilization, and big data technologies.

All B2B interactions should segment businesses according to the prevalence of their technological capabilities before the marketing strategy is developed.

Target market examples

To recap, a target market is a segment of customers most likely to purchase a company’s products or services.

While the two terms have some overlap, it’s important to first make the distinction between a target market and a target audience.

The target market is the end consumer who will use the product.

The target audience, on the other hand, is the focus of the brand’s promotional efforts. 

To illustrate this difference, consider the McDonald’s Happy Meal. The product itself is obviously consumed by children, but it is the parents who control the finances and what the child eats.

As a result, McDonald’s may promote the Happy Meal’s nutritional value or low cost – factors that appeal to the parents but which the child cares very little about.

To solidify the concept of a target market further, read through the following examples.

Nike started out marketing to professional athletes and then expanded its business model to incorporate “everyday” athletes and sports enthusiasts.

As part of its rebranding effort, the company analyzed the benefits of owning its apparel, shoes, equipment, and accessories.

From this, Nike defined a target market of mostly younger consumers who were interested in fitness and possessed the disposal income to invest in equipment and achieve their goals.

Today, most of Nike’s promotional efforts focus on aspiring athletes and runners in a way that is motivational and inclusive.

Vans is an American shoe manufacturer founded in 1966 that made the bold decision to champion alternative subcultures such as skateboarding and bicycle motocross (BMX).

The brand appealed to so-called “misfits and rebels” who saw these sports as not only a hobby or passion but a lifestyle choice.

Vans is now taking advantage of the athleisure trend target market and has a much broader appeal, but the company’s stores continue their retro, skateboarding vibe.

In a Manhattan store, for example, vintage posters of skateboarders adorn the walls with industry slogans and skateboards from popular brands.

Next to skateboard accessories such as wheels and trucks is apparel more reminiscent of earlier decades with muted colors and oversized logos.

Dior is a French luxury fashion house founded by Christian Dior in 1946.

The company primarily targets the so-called “Chardonnay Girls” target market which consists of confident, optimistic, fashion-conscious women in the 18-32 age bracket.

Perhaps unsurprisingly, this target market tends to live in world cities such as Moscow, New York, and Milan with above-average salaries and career prospects.

They have also a propensity to shop offline, but having said that, Chardonnay Girls are consumers that are more likely to become advocates for a brand and share their experiences with friends.

Thus, reducing marketing costs through efficient, customer-focused communication.

Customer segmentation examples

In this section, we’ll delve into some additional customer segmentation examples.

Region and culture

With more than 36,000 restaurants in over 119 countries , McDonald’s uses a subset of geographic customer segmentation to promote menu items to users from various cultures.

In India, for example, ads show McSpicy Paneer alongside Green Chili Naan-Aloo. 

Another region and culture-specific advertisement promotes the Maharaja Mac – better known as the Big Mac – which is “ made with handpicked ingredients from across India” and features the #TrulyIndianBurger hashtag. 

Customer segmentation based on the forecast weather conditions enables the company to predict the moods, needs, and purchase behavior of its customers.

This is usually achieved via the integration of real-time weather data into an existing personalization platform.

Segmentation based on the weather is especially important for retail brands whose products are highly seasonal.

A clothing brand based in the United States, for example, can segment its users based on location and direct those living in the colder northern states to a page promoting scarves, jackets, and gloves.

An undisclosed football club – but one of the largest in England – used weather targeting to recommend merchandise to fans based on their location which is positioned on a Google Maps image in the background.

Some airlines are also using the approach to promote destinations with warmer or sunnier weather than the customer’s home conditions.

Home Chef is a food delivery company that segments its customers based on their profession.

In one email campaign aimed at the healthcare and education industries, the company referenced the upcoming National Teachers and Nurses Day and took the opportunity to thank these individuals for their service.

For those that could verify their teaching or nursing credentials, Home Chef offered 50% off the cost of their first box of food.

Cart abandonment

Almost 70% of desktop users and 86% of those on mobile abandon their cart before finalizing the purchase.

This represents a major source of lost income that can at least be partly recovered with laser-focused customer segmentation.

To encourage users to complete their purchases, companies can create a series of drip campaigns or emails based on metrics such as product type or customer activity level.

Google’s approach for abandoned items in its Google Store is to send users an email with personalization, excellent copywriting, and a clear call to action.

This is normally accompanied by a message that creates urgency such as “ Our popular items sell fast ” and “ Going, going, (almost) gone ”.

Politics is a divisive issue that can easily result in negative publicity for a brand. But rather than shy away from the topic, some brave companies use it as a tool for advanced and highly targeted customer segmentation.

Ben & Jerry’s is one brand that uses political segmentation to sell different flavors of ice cream across the United States.

In the democratic state of Vermont, for example, it released an “Empower Mint” ice cream with a slogan that read “ Democracy is in your hands” .

Key takeaways

Customer segmentation is a crucial part of any marketing strategy , but some businesses may be daunted by the initial investment of time and money. 

However, customer segmentation concerns serving customers and serving them well. Those who do not invest in segmentation run the risk of losing their customers to a competitor.

Accurate and detailed segmentation allows businesses to understand their customers on a deeper level and increases the probability of retaining them.

For the business, this increases conversion rates and drives down costs.

• In essence, a target market is a segment of customers most likely to purchase a company’s products or services. A target market should not be confused with a target audience, which is the focus of the brand’s promotional efforts.
• Nike’s target market consists of younger consumers who are interested in fitness and possess the disposable income to invest in equipment and achieve their goals. 
• Vans once appealed to smaller alternative subcultures such as skateboarding and BMX. Today, the company’s target market has broadened to include athleisure wearers.

Key Highlights:

• Customer Segmentation Overview: Customer segmentation is a marketing technique that divides customers into sub-groups based on similar characteristics. This allows businesses to tailor their strategies to specific groups and understand customer needs better.
• Importance of Customer Segmentation: Market segmentation helps businesses understand customer preferences, locations, and communication preferences. Treating each customer as an individual is essential, even if catering to every customer individually isn’t feasible.
• Demographics: Segmenting by age, gender, income, education, etc., allows businesses to target specific customer groups effectively.
• Geography: Targeting customers based on location, climate, and proximity to certain points of interest.
• Psychographics: Segmenting based on lifestyle, values, motivations, attitudes, and beliefs.
• Behavioral: Segmenting by customer behavior, including spending habits, product usage, and benefits sought.
• Technographic: Segmenting based on preferred technology, such as devices, software, and social media usage.
• Target Market vs. Target Audience: A target market is the end consumer of a product, while the target audience is the focus of promotional efforts. For example, McDonald’s targets parents as the target audience for Happy Meals, even though children consume the product.
• Nike: Initially targeting professional athletes, expanded to include everyday athletes with a focus on fitness enthusiasts.
• Vans: Initially targeted skateboarding and BMX subcultures, now appealing to athleisure wearers.
• Dior: Targets confident, fashion-conscious women aged 18-32 with above-average salaries.
• Region and Culture: McDonald’s tailors ads and menu items based on cultural preferences in different countries.
• Weather: Brands use real-time weather data to personalize offers based on weather conditions.
• Profession: Home Chef offers discounts to customers in specific professions, like teachers and nurses.
• Cart Abandonment: Brands send targeted emails to customers who abandon their shopping carts.
• Politics: Ben & Jerry’s uses political segmentation to offer ice cream flavors tied to political messages.
• Key Benefits: Customer segmentation leads to better understanding, tailored marketing, improved product development, enhanced customer support, cost efficiency, and increased conversion rates.
• Risk of Not Segmenting: Businesses that don’t invest in customer segmentation risk losing customers to competitors and missing out on opportunities to connect on a deeper level.
• Bottom Line: Accurate customer segmentation leads to higher retention rates, improved conversion rates, reduced costs, and overall business success. It’s a crucial part of any effective marketing strategy .

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Please note you do not have access to teaching notes, a practical yet meaningful approach to customer segmentation.

Journal of Consumer Marketing

ISSN : 0736-3761

Article publication date: 1 October 1998

This paper introduces the concept of the Customer Value Matrix, a customer segmentation approach that is especially well‐suited for small retail and service businesses. The discussion offers insights into the reasons for the development of this practical approach, a concrete methodology for its implementation, and strategic and tactical applications of the concept. The material is supported with strong evidence from “real‐world” examples featuring a variety of small retail and service businesses. The paper concludes with a discussion of the managerial implications for companies that manage chains of small retail or service businesses as to how they can take advantage of local relationship marketing.

• Customer orientation
• Franchising
• Market segmentation
• Relationship marketing
• Small firms

Marcus, C. (1998), "A practical yet meaningful approach to customer segmentation", Journal of Consumer Marketing , Vol. 15 No. 5, pp. 494-504. https://doi.org/10.1108/07363769810235974

Copyright © 1998, MCB UP Limited

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Customer Segmentation Analytics: Precision Targeting for Maximum Impact

By The Pecan Team

September 5, 2024

In a nutshell:

• Customer segmentation analytics is crucial for personalized marketing strategies and enhanced customer experiences.
• Predictive analytics and AI are transforming segmentation by focusing on customer behavior.
• Precision targeting through predictive analytics helps businesses identify high-value customers and new opportunities.
• Behavior-based segmentation goes beyond demographics to create dynamic segments based on actual customer behavior.
• Implementing precision targeting strategies with advanced analytics tools can drive business impact and improve customer experiences.

Customer segmentation analytics provides the foundation for developing personalized marketing strategies and enhancing customer experiences. However, traditional demographic segmentations no longer cut it. Without advanced analytics, it can be nearly impossible to exercise precision and create segments that power up your marketing campaigns.

With the help of AI and predictive analytics, you have an opportunity to streamline your audience segmentation by focusing on the intricate elements of customer behavior. Learning how to leverage these techniques is key to staying ahead of the competition.

Understanding Advanced Customer Segmentation Techniques

As businesses continue to evolve, so does the need for advanced customer segmentation techniques. Today, predictive analytics is playing a huge role in enhancing segmentation and precision targeting.

Predictive Analytics for Customer Segmentation

Predictive analytics uses historical data, machine learning (ML), and statistical algorithms to predict future customer behavior. This technique enhances customer segmentation by providing deeper insights into customer preferences and potential actions. Instead of relying solely on past behavior, predictive analytics allows your team to anticipate future trends. This helps you segment customers based on their likely future behaviors.

For example, predictive models can analyze purchase history, browsing patterns, and demographic information. This data lets them forecast which products a customer is likely to buy next.

By integrating these predictions into the segmentation process, you can create highly relevant customer segments . This proactive approach not only improves marketing efficiency but also enhances customer satisfaction.

The key benefits of predictive analytics for customer segmentation are:

Precision Targeting

By accurately predicting customer behavior, businesses can tailor their marketing efforts to meet specific customer needs and preferences. This level of precision reduces wasted marketing resources and increases the likelihood of converting prospects into loyal customers.

High-Value Segments

Predictive analytics also helps in identifying high-value customers and those at risk of churn . By segmenting customers based on their predicted lifetime value, you can allocate resources more effectively. Meaning, you can focus on retaining high-value customers and re-engaging those who might leave.

New Opportunities

Predictive analytics can uncover hidden patterns and correlations within the data, revealing new opportunities for segmentation and targeting.

For example, a retailer can use predictive analytics to identify customers who are likely to respond positively to a loyalty program. By segmenting these customers and targeting them with personalized offers, the retailer can increase engagement and drive repeat purchases.

Similarly, a subscription-based service can predict which customers are at risk of canceling their subscriptions and implement targeted retention strategies.

Behavior-Based Segmentation

Traditional segmentation often relies heavily on demographic factors such as age, gender, and income. While demographics provide a useful starting point, they fail to capture the complexity of customer behavior. Behavior-based segmentation moves beyond these limitations by focusing on customers’ behavior.

Behavior-based segmentation involves analyzing data from various customer touchpoints, including website visits, social media interactions, email responses, and purchase history. This approach allows you to understand customer preferences, identify patterns, and create segments. All of this is based on actual behavior rather than assumptions.

By leveraging behavioral data, your marketing team can develop more accurate and meaningful segments that reflect the true diversity of their customer base.

For example, a travel agency might segment customers based on their travel frequency, preferred destinations, and booking channels. This behavior-based approach provides a richer understanding of customer needs and enables the agency to offer tailored recommendations.

Creating Dynamic Segments Based on Customer Behavior

Behavior-based segmentation also facilitates the creation of dynamic segments that evolve in real time. Unlike static demographic segments, dynamic segments adjust as customer behavior changes. This makes sure that marketing efforts remain relevant and timely. Such agility is particularly valuable in fast-paced industries where customer preferences can shift rapidly.

To create dynamic segments, you would need to continuously monitor and analyze customer behavior using advanced analytics tools. By tracking key metrics such as purchase frequency, average order value, and engagement rates, you can identify shifts in customer behavior so you can update segments accordingly.

For example, an e-commerce platform can use dynamic segmentation to identify customers who frequently browse but rarely purchase. By segmenting these customers and targeting them with personalized incentives, the platform can encourage conversions and boost sales .

Dynamic segments enable businesses to identify cross-selling and upselling opportunities by analyzing purchasing patterns and preferences.

Implementing Precision Targeting Strategies

Once you make a decision to leverage predictive analytics for customer segmentation, you can start implementing precision targeting strategies. Here is how to begin.

Utilizing Advanced Analytics Tools

Advanced analytics platforms are at the forefront of precision targeting, offering powerful tools for data analysis and customer segmentation.

One notable example is Pecan AI, an analytics platform that empowers businesses to harness the power of machine learning without requiring extensive technical expertise. Pecan AI helps organizations predict customer behavior, identify key drivers of sales, and optimize marketing strategies.

Another example is Google Analytics 360, a comprehensive suite that provides in-depth insights into customer interactions across various channels. This platform allows businesses to segment customers based on behavior, track their journey, and measure the effectiveness of marketing campaigns.

These platforms use sophisticated algorithms and ML techniques to analyze large datasets. They have the capability to uncover hidden patterns and generate actionable insights.

By integrating these insights into their segmentation process, you can move beyond traditional demographic-based targeting and develop more effective marketing strategies.

Leveraging Machine Learning for Segmentation

Machine learning revolutionizes customer segmentation by enabling businesses to target customers based on predicted future behavior. Unlike traditional methods that rely on vague demographics or past activities, machine learning models can analyze a vast array of data points. They can predict how customers are likely to behave in the future. This predictive capability allows for more accurate and relevant segmentation.

For example, a machine learning model can analyze a customer's browsing history, purchase patterns, and interaction with marketing materials to predict their likelihood of making a future purchase. By identifying these high-potential customers, your marketing team can create targeted campaigns. This approach increases the chances of conversion and enhances customer satisfaction.

In addition, ML models can identify subtle patterns and correlations within the data that may not be apparent through traditional analysis. For instance, a model might reveal that customers who purchase certain products together are more likely to respond to specific types of promotions.

Personalized Marketing Through Precision Targeting

Personalized marketing through precision targeting involves tailoring marketing campaigns to segments based on predictive behavior. By understanding what customers are likely to do in the future, you create more relevant and engaging marketing. This approach goes beyond generic messaging and offers a personalized experience.

Customer Profiles

To implement personalized marketing, businesses need to develop detailed customer profiles that incorporate predictive behavior data. These profiles should include information on customer preferences, purchase history, and predicted future actions. With this comprehensive view, you can craft marketing messages that address specific pain points, preferences, and motivations.

For example, an online retailer can use predictive behavior data to identify customers who are likely to be interested in a new product launch. By segmenting these customers and targeting them with personalized emails that highlight the product's features and benefits, the retailer can increase the likelihood of conversions.

Similarly, a travel company can predict which customers are planning their next vacation and send them tailored offers based on their travel history.

Dynamic Content

Personalized marketing also involves dynamic content that adjusts based on customer behavior. For instance, a website can display personalized product recommendations based on a customer's browsing history and predicted preferences. This real-time personalization enhances the customer experience and increases the chances of a purchase.

Re-engagement

You can also use personalized marketing to re-engage inactive customers. By analyzing past behavior and predicting future actions, you can identify customers who aren’t properly engaged and implement targeted retention strategies. Personalized offers, discounts, and loyalty programs can be used to re-engage these customers and encourage them to return.

The success of personalized marketing through precision targeting relies on continuous optimization. You’ll need to track the performance of their campaigns, analyze customer responses, and adjust your strategies accordingly.

Driving Business Impact With Dynamic Segmentation

Dynamic segmentation, powered by predictive analytics, transforms customer targeting strategies by continuously adapting to changes in customer behavior. This approach enhances customer experiences and provides measurable business impacts.

Improved Customer Experiences Through Predictive Targeting

Predictive targeting helps businesses provide timely and appropriate interactions. For example, an airline can predict when a frequent traveler is likely to book their next flight and send them personalized offers and updates. This proactive approach not only enhances customer satisfaction but also fosters loyalty, as customers appreciate the convenience and relevance of the communication.

By consistently delivering personalized experiences, you can build stronger relationships with your customers. Satisfied customers are more likely to return, make repeat purchases, and recommend the brand to others. This loyalty translates into long-term business success because retaining existing customers is often more cost-effective than acquiring new ones.

Measuring the Impact of Predictive Segmentation on Business Results

To measure the impact of predictive segmentation on business results , you need to track key performance indicators (KPIs) that reflect the effectiveness of implemented segmentation strategies.

These KPIs provide valuable insights into how well predictive segmentation is driving business outcomes and where adjustments may be necessary.

Customer Lifetime Value (CLV)

CLV measures the total revenue a business expects to earn from a customer over their entire relationship. By segmenting customers based on predicted behavior, you can identify high-value customers and focus your efforts on retaining them. An increase in CLV indicates that predictive segmentation is successfully identifying and engaging valuable customers.

Conversion Rate

The conversion rate measures the percentage of targeted customers who complete a desired action, such as making a purchase or signing up for a service. A higher conversion rate suggests that predictive targeting effectively reaches customers with relevant offers, leading to more successful outcomes.

Customer Retention Rate

This KPI tracks the percentage of customers who continue to engage with your business over a specified period. An improved retention rate indicates that predictive segmentation enhances customer satisfaction and loyalty, reducing churn and fostering long-term relationships.

Customer Satisfaction (CSAT) Scores

CSAT scores provide direct feedback from customers about their experiences. By analyzing CSAT scores for different segments, you can assess how well predictive targeting meets customer needs and expectations. Higher CSAT scores reflect increased satisfaction levels due to personalized interactions.

Return on Investment (ROI)

ROI measures the profitability of marketing campaigns and segmentation strategies. By comparing the revenue generated from targeted segments to the costs of predictive analytics and marketing efforts, you can evaluate the financial impact of their segmentation strategies. A positive ROI indicates that predictive segmentation is driving profitable growth.

Engagement Metrics

Metrics such as click-through rates (CTR), email open rates, and website interaction rates provide insights into customer engagement levels. Higher engagement metrics suggest that predictive targeting effectively captures customer interest and drives interaction with the brand.

By continuously monitoring these KPIs, you can assess the effectiveness of your predictive segmentation strategies and make data-driven adjustments. Regular analysis identifies successful tactics and areas for improvement, ensuring that segmentation efforts stay aligned with business goals and customer needs.

Making the Most Out of Segmentation Analytics

Through predictive analytics and behavior-based segmentation, you can experience improved customer experiences, increased loyalty , and overall business impact.

To stay competitive in the world of AI and ML, data leaders must adopt precision targeting. With this tool under your belt, you can achieve maximum positive impact with your marketing and sales efforts.

You need highly reliable tools to make the most out of segmentation analytics. If you want to get started with an accessible, intuitive, and accurate platform, you're in the right place. To learn more, book a demo and discover how Pecan AI could revolutionize your approach to customer segmentation.

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Implementing Customer Segmentation Using Machine Learning [Beginners Guide]

These days, you can personalize everything. There’s no one-size-fits-all approach. But, for business, this is actually a great thing. It creates a lot of space for healthy competition and opportunities for companies to get creative about how they acquire and retain customers.

One of the fundamental steps towards better personalization is customer segmentation. This is where personalization starts, and proper segmentation will help you make decisions regarding new features, new products, pricing, marketing strategies, even things like in-app recommendations.

But, doing segmentation manually can be exhausting. Why not employ machine learning to do it for us? In this article, I’ll tell you how to do just that.

What is customer segmentation

Customer segmentation simply means grouping your customers according to various characteristics (for example grouping customers by age).

It’s a way for organizations to understand their customers. Knowing the differences between customer groups, it’s easier to make strategic decisions regarding product growth and marketing.

The opportunities to segment are endless and depend mainly on how much customer data you have at your use. Starting from the basic criteria, like gender, hobby, or age, it goes all the way to things like “time spent of website X” or “time since user opened our app”. 

There are different methodologies for customer segmentation, and they depend on four types of parameters: 

• geographic, 
• demographic, 
• behavioral,
• psychological.  

Geographic customer segmentation is very simple, it’s all about the user’s location. This can be implemented in various ways. You can group by country, state, city, or zip code.

Demographic segmentation is related to the structure, size, and movements of customers over space and time. Many companies use gender differences to create and market products. Parental status is another important feature. You can obtain data like this from customer surveys.

Behavioral customer segmentation is based on past observed behaviors of customers that can be used to predict future actions. For example, brands that customers purchase, or moments when they buy the most. The behavioral aspect of customer segmentation not only tries to understand reasons for purchase but also how those reasons change throughout the year.

Psychological segmentation of customers generally deals with things like personality traits, attitudes, or beliefs. This data is obtained using customer surveys, and it can be used to gauge customer sentiment.

Advantages of customer segmentation

Implementing customer segmentation leads to plenty of new business opportunities. You can do a lot of optimization in:

• budgeting, 
• product design, 
• promotion, 
• marketing, 
• customer satisfaction. 

Let’s discuss these benefits in more depth.

Nobody likes to invest in campaigns that don’t generate any new customers. Most companies don’t have huge marketing budgets, so that money has to be spent right. Segmentation enables you to target customers with the highest potential value first, so you get the most out of your marketing budget. 

• Product design

Customer segmentation helps you understand what your users need. You can identify the most active users/customers, and optimize your application/offer towards their needs. 

Properly implemented customer segmentation helps you plan special offers and deals. Frequent deals have become a staple of e-commerce and commercial software in the past few years. If you reach a customer with just the right offer, at the right time, there’s a huge chance they’re going to buy. Customer segmentation will help you tailor your special offers perfectly.

The marketing strategy can be directly improved with segmentation because you can plan personalized marketing campaigns for different customer segments, using the channels that they use the most.

• Customer satisfaction

By studying different customer groups, you learn what they value the most about your company. This information will help you create personalized products and services that perfectly fit your customers’ preferences.

In the next section, we’re going to discuss why machine learning for customer segmentation.

Machine Learning for customer segmentation

Machine learning methodologies are a great tool for analyzing customer data and finding insights and patterns. Artificially intelligent models are powerful tools for decision-makers. They can precisely identify customer segments, which is much harder to do manually or with conventional analytical methods.

There are many machine learning algorithms, each suitable for a specific type of problem. One very common machine learning algorithm that’s suitable for customer segmentation problems is the k-means clustering algorithm . There are other clustering algorithms as well such as DBSCAN, Agglomerative Clustering, and BIRCH, etc.

Why would you implement machine learning for customer segmentation?

Manual customer segmentation is time-consuming. It takes months, even years to analyze piles of data and find patterns manually.  Also if done heuristically, it may not have the accuracy to be useful as expected.

Customer segmentation used to be done manually and wasn’t too precise. You’d manually create and populating different data tables, and analyze the data like a detective with a looking glass. Now, it’s much better (and relatively easy thanks to rapid progress in ML) to just use machine learning, which can free up your time to focus on more demanding problems that require creativity to solve.

Ease of retraining

Customer Segmentation is not a “develop once and use forever” type of project. Data is ever-changing, trends oscillate, everything keeps changing after your model is deployed. Usually, more labeled data becomes available after development, and it’s a great resource for improving the overall performance of your model. 

There are many ways to update customer segmentation models, but here are the two main approaches:

• Use the old model as the starting point and retrain it.
• Keep the existing model and combine its output with a new model.

Better scaling 

Machine learning models deployed in production support scalability, thanks to cloud infrastructure. These models are quite flexible for future changes and feedback. For example, consider a company that has 10000 customers today, and they’ve implemented a customer segmentation model. After a year, if the company has 1 million customers, then ideally we don’t need to create a separate project to handle this increased data. Machine Learning models have the inherent capability to handle more data and scale in production.

Higher accuracy

The value of an optimal number of clusters for given customer data is easy to find using machine learning methods like the elbow method. Not only the optimal number of clusters but also the performance of the model is far better when we use machine learning.

F1 Score vs ROC AUC vs Accuracy vs PR AUC: Which Evaluation Metric Should You Choose?

Exploring customer dataset and its features

Let’s analyze a customer dataset . Our dataset has 24,000 data points and four features. The features are:

• Customer ID – This is the id of a customer for a particular business.
• Products Purchased – This feature represents the number of products purchased by a customer in a year.
• Complaints – This column value indicates the number of complaints made by the customer in the last year
• Money Spent – This column value indicates the amount of money paid by the customer over the last year.

In the upcoming section, we’ll pre-process this dataset.

Pre-processing the dataset

Before feeding the data to the k-means clustering algorithm, we need to pre-process the dataset. Let’s implement the necessary pre-processing for the customer dataset.

Moving on, we’ll implement our k-means clustering algorithm in Python.

Might be useful

A Comprehensive Guide to Data Preprocessing

Implementing K-means clustering in Python

K-Means clustering is an efficient machine learning algorithm to solve data clustering problems. It’s an unsupervised algorithm that’s quite suitable for solving customer segmentation problems. Before we move on, let’s quickly explore two key concepts

Unsupervised Learning

Unsupervised machine learning is quite different from supervised machine learning. It’s a special kind of machine learning algorithm that discovers patterns in the dataset from unlabelled data. 

Unsupervised machine learning algorithms can group data points based on similar attributes in the dataset. One of the main types of unsupervised models is clustering models.

Note that, supervised learning helps us produce an output from the previous experience.

Clustering algorithms

A clustering machine learning algorithm is an unsupervised machine learning algorithm. It’s used for discovering natural groupings or patterns in the dataset. It’s worth noting that clustering algorithms just interpret the input data and find natural clusters in it.

Some of the most popular clustering algorithms are:

• K-Means Clustering
• Agglomerative Hierarchical Clustering
• Expectation-Maximization (EM) Clustering
• Density-Based Spatial Clustering 
• Mean-Shift Clustering

In the following section, we’re going to analyze the customer segmentation problem using the k-means clustering algorithm and machine learning. However, before that, let’s quickly discuss why we’re using the k-means clustering algorithm.

Why use K-means clustering for customer segmentation?

Unlike supervised learning algorithms, K-means clustering is an unsupervised machine learning algorithm. This algorithm is used when we have unlabelled data. Unlabelled data means input data without categories or groups provided. Our customer segmentation data is like this for this problem. 

The algorithm discovers groups (cluster) in the data, where the number of clusters is represented by the K value. The algorithm acts iteratively to assign each input data to one of K clusters, as per the features provided. All of this makes k-means quite suitable for the customer segmentation problem.

Given a set of data points are grouped as per feature similarity. The output of the K-means clustering algorithm is:

• The centroids values for K clusters,
• Labels for each input data point. 

At the end of implementation, we’re going to get output such as a group of clusters along with which customer belongs to which cluster.

First, we need to implement the required Python libraries as shown in the table below. 

We’ve imported the pandas, NumPy sklearn, plotly and matplotlib libraries. Pandas and NumPy are used for data wrangling and manipulation, sklearn is used for modelling, and plotly along with matplotlib will be used to plot graphs and images.

After importing the library, our next step is to load the data in the pandas data frame. For this, we’re going to use the read_csv method of pandas.

After loading the data, we need to define the K- means model. This is done with the help of the KMeans class that we imported from sklearn, as shown in the code below.

After defining the model, we want to train is using a training dataset. This is implemented with the use of the fit method, as shown in the code below.

Note that we’re passing three features to the fit method, namely products_purchased, complains, and money_spent.

Though we have trained a K-means model up to these points, we haven’t found the optimal number of clusters required in this case of customer segmentation. Finding the optimal number of clusters, for the given dataset is important for producing a high-performant k-means clustering model. 

In the upcoming section, we’re going to find the optimal number of clusters of the given dataset and then re-train the k-means clustering model with these optimal values of k. This will produce our final model.

Finding the optimal number of clusters

Finding the optimal number of clusters is one of the key tasks when implementing a k-means clustering algorithm. It’s worth noting that a k-means clustering model might converge for any value of K, but at the same time, not all values of K will produce the best model.

For some datasets, data visualization can help understand the optimal number of clusters, but this doesn’t apply to all datasets. We have a few methods, such as the elbow method, gap statistic method, and average silhouette method, to assess the optimal number of clusters for a given dataset. We’ll discuss them one by one.

• The elbow method  finds the value of the optimal number of clusters using the total within-cluster sum of square values. This represents how spread-apart the generated clusters are from one another. In this case, the K-means algorithm is evaluated for several values of k, and the within-cluster sum of square values is calculated for each value of k. After this, we plot the K versus the sum of square values. After analyzing this graph, the number of clusters is selected, so that adding a new cluster doesn’t change the values of the sum of square values significantly.
• Average silhouette method is a measure of how well each data point fits its corresponding cluster. This method evaluates the quality of clustering. As a general rule, a high average silhouette width denotes better clustering output.
• Gap statistic method  is a measure of the value of gap statistics. Gap statistics is the difference between the total intracluster changes for various values of k compared to their expected values. This calculation is done using the null reference distribution of data points. The optimal number of clusters is the value that maximizes the value of gap statistics.

We’re going to use the elbow method. The K-means clustering algorithm clusters data by separating given data points in k groups of equal variances. This effectively minimizes a parameter named inertia. Inertia is nothing but within-cluster sum-of-squares distances in this case.

When we use the elbow method, we gradually increase the number of clusters from 2 until we reach the number of clusters where adding more clusters won’t cause a significant drop in the values of inertia. 

The stage at this number of clusters is called the elbow of the clustering model. We’ll see that in our case it’s K =5.

For implementing the elbow method, the below function named “try_different_clusters” is created first. It takes two values as input:

• K (number of clusters),
• data (input data).

The method try_different_clusters is called using the below code, where we pass the values of K from 1 to 12 and calculate the inertia for each value of k.

Using the below code, we plot the value of K (on the x-axis) against corresponding values of inertia on the Y-axis.

We can generate the below plot using the above code. The elbow of the code is at K=5. We have chosen 5 as if we increase the number of clusters to more than 5, there is very small change in the inertia or sum of the squared distance.

Optimal value of K = 5

The stage at which the number of clusters is optimal is called the elbow of the clustering model. For example, in the below image, the elbow is at five clusters (K =5). Adding more than 5 clusters will cause the creation of an inefficient or less performant clustering model.

As discussed before, we need to train the k-means clustering model again with the optimal number of clusters found.

Note that we’re using the fit_predict method to train the model.

In the next section, we’re going to discuss how to visualize customer segmentation clusters in three dimensions.

Visualizing customer segments

In this section, we’ll be implementing some code using plotly express. This way we’ll visualize the clusters in three dimensions, formed by our k-means algorithm. Plotly express is a library based on plotly that works on several types of datasets and generates highly-styled plots.

First, let’s add a new column named ‘clusters’ to the existing customer data dataset. This column will be able to tell which customer belongs to what cluster.

Note that we’re using NumPy expm1 methods here. NumPy expm1 function returns the exponential value of minus one for each element given inside a NumPy array as output. Therefore, the np.expm1 method accepts arr_name and out arguments and then returns the array as outputs.

After adding the new column, named clusters, the customer data dataset will look as below.

Finally, we’re going to use the below code to visualize the five clusters created. This is done using plotly with the express library.

Plotly is a Python library used for graphing, statistics, plotting, and analytics. This can be used along with Python, R, Julia, and other programming languages. Plotly is a free and open-source library. 

Want to organize your experimentation process? Check how you can have interactive Plotly charts stored in the same place as the rest of your model metadata (metrics, parameters, weights, and more).

Plotly Express is a high-level interface over Plotly, that works on several types of datasets and generates highly-styled plots.

The plotly.express class has functions that can produce entire figures in one go. Generally, it’s referred to as px. It’s worth noting plotly express is the built-in module of the plotly library. This is the starting point of creating the most common plots as recommended. Note that each plotly express function creates  graph objects  internally and returns plotly.graph_objects. 

A graph created by a single method call using plotly express can be also created using graph objects only. However, in that case, it takes around 5 to 100 times as much code.

As the  2D scatter plot , px.scatter plots individual data in a two-dimensional space, and the 3D method px.scatter_3d plots individual data in a three-dimensional space.

Visualization of clusters of data points is very important. Various edges of the graph provide a quick view of the complex input data set.

It’s not wise to serve all customers with the same product model, email, text message campaign, or ad. Customers have different needs. A one-size-for-all approach to business will generally result in less engagement, lower-click through rates, and ultimately fewer sales. Customer segmentation is the cure for this problem.

Finding an optimal number of unique customer groups will help you understand how your customers differ, and help you give them exactly what they want. Customer segmentation improves customer experience and boosts company revenue. That’s why segmentation is a must if you want to surpass your competitors and get more customers. Doing it with machine learning is definitely the right way to go. 

If you made it this far, thanks for reading!

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Customer segmentation: Guide to types, tips, and strategy

Article Summary ：This essay will explore the different types of customer segmentation, provide tips on how to effectively segment customers, and discuss how segmentation can inform an overall marketing strategy.

Table of contents for this article

Types of Customer Segmentation

Tips for effective customer segmentation, using segmentation to inform strategy.

Customer segmentation is a vital marketing strategy that involves dividing customers into distinct groups based on common characteristics. By segmenting customers, businesses can better understand their target audiences and tailor their products, services, pricing, and communications accordingly. This essay will explore the different types of customer segmentation, provide tips on how to effectively segment customers, and discuss how segmentation can inform an overall marketing strategy.

There are several common approaches to segmenting customers:

Demographic segmentation divides customers based on variables like age, gender, income, occupation, education level, and more. This type of segmentation is useful for gaining insights into how different demographic groups view and interact with a brand.

Geographic segmentation focuses on factors such as country, region, state, city, density, and climate. Businesses can use geographic segmentation to tailor their approach based on local tastes, regulations, infrastructure, and other location-specific considerations.

Behavioral segmentation examines customers' behaviors, usage patterns, benefits sought, and loyalty status. Behavioral data provides a deeper understanding of what customers do and why they make certain purchasing decisions.

Psychographic segmentation analyzes personality traits, values, opinions, interests, and lifestyles. This deeper level of insight can be particularly helpful for differentiating customers and crafting compelling brand messaging.

When segmenting customers, businesses should keep the following tips in mind:

• Use multiple variables. The most accurate segmentation combines demographic, geographic, behavioral, and psychographic factors rather than relying on a single dimension.
• Define segments clearly. Segments should be distinct, measurable, accessible, substantial, and actionable to properly inform marketing strategy.
• Analyze quantitative and qualitative data. Leverage both hard data like purchase histories as well as soft inputs like customer surveys and interviews.
• Validate segments. Test segmentation assumptions by examining how well segments predict behaviors and responses to marketing initiatives.
• Keep segments dynamic. Customer preferences and the market are always evolving, so segmentation needs periodic review and refinement.
• Align segments with goals. Ensure the identified segments are directly relevant to key business objectives like increasing sales, boosting loyalty, or gaining market share.
• Communicate insights. Share the results of segmentation analysis across departments to optimize how the entire organization engages different customer groups.

The ultimate goal of customer segmentation is to develop targeted strategies that resonate more strongly with each audience. Some ways segmentation can shape a comprehensive marketing approach include:

• Tailored messaging. Craft customized content, promotions, advertising, and product recommendations for each segment.
• Personalized experiences. Leverage customer data to deliver more individualized service, communications, and online/in-store experiences.
• Channel preferences. Deploy campaigns through the channels segments prefer to receive, evaluate, and act on information.
• Product development. Prioritize developing or modifying offerings to better satisfy the needs of high-value segments.
• Pricing strategies. Consider segment-specific pricing, discounts, loyalty programs or bundling/packaging options.
• Partnership opportunities. Identify segments that could be reached through strategic alliances with complementary businesses.
• Performance tracking. Use well-defined segments to benchmark goals, measure ROI for each initiative, and optimize approaches over time.

In today's competitive landscape, a one-size-fits-all approach to customers simply does not work. Segmentation provides the insights necessary to attract, engage and retain customers in a tailored manner. When done strategically and continually refined based on results, segmentation can be a powerful tool for boosting customer loyalty, increasing revenues and gaining market share over the long term.

Take our Insight Tool for a spin—for free—to see how it can work for your business.

The article is original by Udesk, and when reprinted, the source must be indicated：https://www.udeskglobal.com/blog/customer-segmentation-guide-to-types-tips-and-strategy.html

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Essays on Market Segmentation and Retailers' Competing Strategies

This dissertation focuses on exploring U.S. food retailers’ strategic interactions and the impacts on consumers. Specifically, I examine food retailers’ strategies on segmenting consumers, conducting price discrimination, and designing their product portfolio in the context of the U.S. yogurt market. The first essay examines the segmentation strategies employed by food retailers, with a focus on the use of advanced machine learning techniques (i.e., K-means clustering) to group consumers based on various characteristics, including demographics and purchase history. The second essay applies the data-driven market segmentation obtained in the first essay to a second-degree price discrimination model. The third essay relaxes the implicit assumption made in the first two essays that consumers’ choice set is fixed, and studies a non-price strategy, namely, adjusting assortment, that is adopted by food retailers in response to regulations. By analyzing the retailers’ strategies on market segmentation and responses to regulations, this dissertation aims to shed light on the strategic interactions of food retailers and consumers, and the competitive landscape of food market in general.

Understanding the strategies employed by food retailers is of utmost importance in agricultural and food economics as it directly influences consumers and their purchasing decisions. The food retail industry in the U.S. is highly competitive, with retailers continuously devising tactics to attract and retain customers. Dimensions of competition such as pricing strategies, product assortment, promotional activities, and customer service can significantly impact consumers’ choices and behaviors. Investigating the strategies employed by food retailers not only provides insights into their business operations but also sheds light on how these strategies affect consumers.

The first essay explores the application of machine learning methods in consumer segmentation under different information environments. Machine learning methods become popular in economic and marketing research, partly because of their flexibility in application. Although recent studies apply these advanced methods to various topics including water, housing, health, and food markets, much is less known about using machine learning methods to facilitate firms’ market segmentation decisions. Using Nielsen Consumer Panel data, I show that K-means clustering, one of the unsupervised learning methods, can be applied to conduct market segmentation. From the retailers’ perspective, incorporating more consumer information (i.e., purchase history) leads to the change in segments consumers belong to.

The second essay assesses the effectiveness of data-driven market segmentation in enhancing price discrimination models. Price discrimination models are commonly adopted by firms to optimize revenue and profitability by customizing prices to different customer segments. Existing studies often rely on exogenous assumptions for consumer segmentation, which may or may not be applicable in practice. This study advances the existing literature by replacing the consumer segment assumption with data-driven market segmentation obtained through K-means clustering. The results are then applied to the second-degree price discrimination model to analyze how sensitive the firms optimal profits are under different consumer information environments. The findings reveal that adding consumer information to consumer segment leads to a more inelastic demand for the consumer segments and an increase in firm’s profits.

The third essay focuses on the non-price strategies retailers adopt to respond to the Unit Pricing Regulation (UPR). UPR requires retailers to display unit prices in addition to product prices and helps consumers make more informed decisions. Despite extensive research on consumers perceptions of unit prices, little is known about retailers price and non-price responses under intensified price competition brought by UPR. Relying on the geographic variation in UPR implementation across U.S. states, we use product-store-level scanner data on the U.S. yogurt market and identify UPR effects on store product offerings and pricing. We find that mass merchandisers reduce product offerings under UPR. Grocery stores that belong to a retail chain entirely under UPR add brands, while other grocery stores make no significant assortment responses. UPR price effects are limited for mass merchandisers as well as grocery stores. Using a structural demand model, we find that the average consumer surplus falls under UPR, highlighting an unintended policy effect.

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4 Awesome Customer Segmentation Examples (And Why They Work)

Written by Michael Keenan

@upmostmike

February 24, 2020

If you’re struggling to engage customers, don’t know where you’re going wrong, yet you push out content like there’s no tomorrow without a clear return, your problem may be your segmentation methods — or lack thereof. 

Customer segmentation could be the strategy you need to see some results from your labor. Combined with Chat Marketing , you can enhance the power of both.

Here’s how you can use customer segmentation to raise the bar for engagement, loyalty, and profit — no matter the size of your business. 

What is customer segmentation?

Customer segmentation is a way to divide your customer base into smaller groups according to a set of criteria. This method of categorizing your customers enables you to send messages that appeal to a specific sub-group. 

These sub-groups can be characterized as:

• Demographic: gender, age, occupation, marital status, income, etc.
• Geographic: country, state, city, town or county. 
• Technographic: preferred technologies, software, and mobile devices.
• Psychographic: personal attitudes, values, interests, or personality traits.
• Behavioral: actions or inactions, spending habits, feature use, session frequency, browsing history, average order value, content consumption

You can mix and match different categories. For example, you can create a segment characterized by women who: 

• fall between ages 40-55
• are interested in pants
• have spent over $500 with your business 
• have completed three transactions over the past three months
• haven’t engaged with you in two weeks

Armed with this specific information, you can enable personalized messages to a customer segment and help sales for an online store.

Four examples of customer segmentation 

Here are a few successful customer segmentation examples that enable valuable customer interactions. 

1. Don Marler’s Food Cart

Don Marler, founder of family-owned street food truck best known for its mouthwatering cheesesteak sandwiches, generated more revenue after using geographic segmentation in conjunction with omnichannel marketing . 

Here’s how it worked: Don created a post on Facebook announcing his next location, to attract more location-based sales. The post also included a discount code that clicked-through to Messenger.

Once in Messenger, the user was asked to provide their email, phone number, and SMS consent opt-in. Don then segmented each subscriber by their location in ManyChat. This segmentation allowed him to send a relevant SMS to those people whenever he was in their area. 

This omnichannel approach, made razor-sharp by geographic segmentation, resulted in more than $7,000 in additional revenue per month for the business. You can find out more about Don’s strategy in this case study on SMS Marketing .

Netflix uses behavioral segmentation to deliver customized content to its 158.3 million subscribers every day on an automated basis.

It relies on Machine Learning (ML) to learn about its customers via their behavior on the TV streaming app. Netflix then stores this information to segment customers into groups based on their actions, allowing the media services provider to offer a more personalized customer experience. 

To illustrate this type of segmentation: If you’re a Netflix user, the company will know which shows you’ve watched in the last month. Based on the information, it can identify your favorite genres and pick content recommendations for you to watch in the future. 

This type of customer segmentation can also be used to monetize active prospects . For example, armed with information on a prospect’s recent engagement with your brand, you can encourage them to purchase through personalized recommendations and discounts via SMS , email or Messenger. 

Create more targeted customer segments with ManyChat. It’s free to get started.

3. zinvo watches.

The international timepiece maker, Zinvo Watches , collaborated with Dillon Ceglio — a ManyChat Agency Partner — to segment prospects based on their responses to a quiz executed through Messenger. This let the e-commerce business to offer personalized promos and curated offerings that encouraged purchases. 

It worked like this: Customers entered the funnel from a paid ad to the Style Guide. Inside Messenger, they were invited to take part in Zinvo’s quiz. The questions covered preferences such as styles, favorite colors, and when they wear watches.

Users were then segmented into groups based on their responses. For example, a subgroup was made for users who preferred bold colors. 

Using this strategy, Zinvo achieved the following goals: 

• Help customers at the earliest stages of their buying journey
• Gain user data to deliver tailored offerings and delight customers
• Improve segmentation further through a data feedback loop
• Bring in new leads at a reduced cost

To learn more, we recommend reading How To Use Advanced Targeting in ManyChat to collect customer information and segment using tags and conditions in Flow Builder.

Porsche recently used psychographic segmentation , dividing its customers based on their lifestyle choices, personality traits, and values. The company then constructed specific profiles for each list, such as ‘The top gun’ for ambitious and driven individuals who crave power and want to be noticed. 

This allowed Porsche to:

• Create personalized content based on its customers’ psychographic traits
• Distribute relevant content to the right customers

You can make this approach more effective through Chat Marketing, delivering content to customers through their most frequented channels (like Messenger or email ). This way, your customers are more likely to see and engage with your content. 

Quick tip: Long-form content is best for emails while short-form content should be reserved for Messenger and SMS. Want to send larger files like videos and images? Messenger will help you deliver them at speed with super-fast uploads.

Using customer segmentation to grow your business

To leverage the power of customer segmentation and Chat Marketing, start by answering the following questions:

• What is your marketing objective? (To improve engagement, increase sales, strengthen customer loyalty, bolster your reputation?)
• What type of customer do you need to target in order to achieve your objective? (Will you segment them by geographics, demographics, psychographics?) 
• What form will your content take? (Promotions, offers, educational articles, product videos, surveys.)
• What are your customers’ preferred means of communication? (SMS, email, Messenger or all three?)

This information will help you segment your customers effectively and create a personalized Chat Marketing strategy for your target audience. If you get stuck, simply read our customer segmentation examples for inspiration and direction.

Create dynamic customer segments and drive more revenue for your business with ManyChat. It’s free to get started.

The Importance of Market Segmentation Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Advantages and disadvantages of segmentation, linking segmentation and other marketing mix elements.

Market segmentation is a method that is used to manage a larger market. It involves the division of the larger market into smaller subsets. These subsets have an internal homogeneity where the buyers within them have common needs for goods and services. They also show the same buying behaviour. Buyers in different subsets exhibit different needs and therefore it can be concluded that these subsets have an external homogeneity (Hutt & Thomas, 2002).

Different criteria can be used to divide the market into subsets. These may include geographic boundaries, demographic divisions or even psychographic profiles. The segments that are developed should be viable in such a way that they result in the profitability of an organization. They should also be accessible and measurable such that data can be obtained from them easily for analysis. This will ensure that their profitability is checked progressively.

When a company uses segmentation, it is bound to experience both its advantages and disadvantages. One of its advantages is that the needs of the customers are matched well. This is because segmentation may result in the division of the market according to the needs of the buyers. This will ensure that the needs of each subset are identified and therefore met in the best possible way. Another advantage is that it may also result in retaining more customers. Customers are dynamic in the way they interact with the market. The different subsets ensure that if such changes occur, the needs of a customer are still met by a different subset. This will ensure that the customers do not switch to other products (Malcolm & Ian, 2004). Segmentation also allows the company to know how it is viewed by its customers. This is important since it will enable the business to make changes where necessary to be able to satisfy its customers more. Another advantage is that it offers a platform for growth. When segmentation is carried out effectively, it increases sales and this will result in the growth of the company.

The disadvantages that are associated with segmentation include neglecting other segments in the market. This may be a result of concentrating on a particular segment. This results in the competitors taking advantage of the isolated segments and therefore losses for the company (Robert, 2006). Another disadvantage comes when the market is over segmented. This is by having very small segments such that they cannot be assessed in terms of their profitability to the company. This may also result in losses being incurred by the company.

Segmentation can easily be linked with other marketing mix elements such as promotional activities. This is because the needs of the segments are identified beforehand. For example, if a segment consists of customers of a given age group, the advertisements that will be done within this particular segment will take into account their age. This ensures that the intended message is received and understood well and results in making the marketing of the products effective and easy. The intended message is also delivered to the intended recipients.

Segmentation is therefore important since when it is well carried out it results in the profitability of the company. It is seen that its disadvantages come about as a result of poor segmentation and not from its shortcomings. Therefore, it is advisable as part of the marketing structure to include segmentation as it will improve the profitability of the company. Its implementation should also be done carefully to ensure that the company does not suffer the shortcomings of poor segmentation.

• Hutt, M. D., & Thomas, W. S. (2002). Business Marketing Management. Ohio: South-Western.
• Malcolm, M., & Ian, D. (2004). Market Segmentation. London: MacMillian.
• Robert, W. B. (2006). The White Paper Marketing Handbook. Ohio: Thompson.
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IvyPanda. (2022, January 13). The Importance of Market Segmentation. https://ivypanda.com/essays/the-importance-of-market-segmentation/

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IvyPanda . 2022. "The Importance of Market Segmentation." January 13, 2022. https://ivypanda.com/essays/the-importance-of-market-segmentation/.

1. IvyPanda . "The Importance of Market Segmentation." January 13, 2022. https://ivypanda.com/essays/the-importance-of-market-segmentation/.

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Customer Segmentation Essays

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Add a method, remove a method, edit datasets, assnet: adaptive semantic segmentation network for microtumors and multi-organ segmentation.

12 Sep 2024  ·  Fuchen Zheng , Xinyi Chen , Xuhang Chen , Haolun Li , Xiaojiao Guo , Guoheng Huang , Chi-Man Pun , Shoujun Zhou · Edit social preview

Medical image segmentation, a crucial task in computer vision, facilitates the automated delineation of anatomical structures and pathologies, supporting clinicians in diagnosis, treatment planning, and disease monitoring. Notably, transformers employing shifted window-based self-attention have demonstrated exceptional performance. However, their reliance on local window attention limits the fusion of local and global contextual information, crucial for segmenting microtumors and miniature organs. To address this limitation, we propose the Adaptive Semantic Segmentation Network (ASSNet), a transformer architecture that effectively integrates local and global features for precise medical image segmentation. ASSNet comprises a transformer-based U-shaped encoder-decoder network. The encoder utilizes shifted window self-attention across five resolutions to extract multi-scale features, which are then propagated to the decoder through skip connections. We introduce an augmented multi-layer perceptron within the encoder to explicitly model long-range dependencies during feature extraction. Recognizing the constraints of conventional symmetrical encoder-decoder designs, we propose an Adaptive Feature Fusion (AFF) decoder to complement our encoder. This decoder incorporates three key components: the Long Range Dependencies (LRD) block, the Multi-Scale Feature Fusion (MFF) block, and the Adaptive Semantic Center (ASC) block. These components synergistically facilitate the effective fusion of multi-scale features extracted by the decoder while capturing long-range dependencies and refining object boundaries. Comprehensive experiments on diverse medical image segmentation tasks, including multi-organ, liver tumor, and bladder tumor segmentation, demonstrate that ASSNet achieves state-of-the-art results. Code and models are available at: \url{https://github.com/lzeeorno/ASSNet}.

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