How To Segment Customers (Framework)

Step 1: Get Prepared

To work on your customer segmentation you can either build your own whiteboard template for example on Miro and use Google Sheets to build a segmentation database or you can use our ready-to-use templates along with this guide.

Customer Segmentation Template

Included Templates:

• ✅ Customer Segmentation Whiteboard Miro Board
• ✅ Segmentation Database Spreadsheet Template

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Step 2: Segmentation Criteria

First of all we will define which criteria you will take into account in your customer segmentation model. Segmentation criteria are the attributes or characteristics that you use to divide your customer base into distinct segments.

Your segmentation criteria should include indicators about the customer status and value:

💤 Customer Status

This criteria describes the customers status in terms of recency, meaning when they purchased the last time.

💡It can help to develop targeted marketing and sales strategies. For example, new customers may require more education and information about the company’s products or services, while active customers may respond well to loyalty rewards or special offers. Sleeping customers may need a gentle nudge to return to making purchases, while lost customers may require more aggressive tactics such as special promotions or discounts to win back their business.

➡️ Variables could include for example:

• New:  purchased recently (e.g. within the last 6 months) for the first time
• Active:  purchased recently (e.g. within the last 6 months)  timeframe for the last time at least for the second time
• Sleeping: purchase within a medium time frame for the last time (e.g. more than 6 months but less than 12 months passed since the last purchase)
• Lost: has not purchased within a longer time frame (e.g. more than 12 months)

Consider that these are just examples and you can define your own customer status levels.

💰 Customer Value

This criteria determines the value a customer generates for your business. How you measure value depends on your business model. For example, a subscription-based business may measure customer value based on the length of the subscription and the monthly fee, while an e-commerce business may measure customer value based on the total amount spent over time

💡By measuring customer value, you can better understand the most profitable customers and develop targeted retention and upselling strategies to maximize their value over time. For example, a company may identify that customers with a high average order value are more likely to make repeat purchases and develop loyalty programs or special offers to encourage those customers to make additional purchases.

➡️ Variables could include for example:

• Average order value: The average value of a customer’s order, which can help identify high-value customers and target promotions to encourage larger orders.
• Order volume: The total number of orders a customer makes, which can help identify loyal customers and measure their value over time.
• Average monthly/annual customer revenues: The average monthly or annual revenue generated by a customer, which can help identify high-value customers and allocate resources to retain and upsell to those customers.
• Estimated customer lifetime value (CLV): The estimated total value a customer will generate for the business over the course of their relationship, which can help identify high-value customers and inform long-term retention and upselling strategies.

Besides status and value you can add any additional segmentation criteria that you consider important to differentiate your segments:

🛒 Purchasing Behavior

This criteria involves analyzing the different aspects of a customer’s purchasing behavior.

💡Understanding the purchasing behavior you can better understand your identify areas of opportunity for cross-selling or upselling, and optimize marketing and sales efforts to increase revenue and customer retention. For example, a company may identify that customers who purchase certain product categories frequently are also more likely to purchase complementary products, and can create targeted campaigns to encourage such purchases.

➡️ Variables could include for example:

• Purchasing frequency: The frequency with which a customer makes purchases, which can help identify loyal or high-value customers.
• Purchasing moment: The timing or seasonality of purchases, which can inform targeted promotions or discounts.
• Purchasing channel: The channel through which a customer makes purchases, which can help companies optimize their sales and marketing efforts across different channels.
• Purchased product/service categories or mix: The specific products or services purchased by a customer, which can help identify customer preferences and inform product development or marketing strategies.

💜 Level of Brand Engagement

This criteria measures the level of engagement a customer has with a brand, meaning how frequently they interact with the brand through different channels.

💡By segmenting customer based on their brand engagement you can better understand your customers’ affinity for the brand and develop strategies to improve engagement and loyalty. For example, a company may identify that customers who frequently engage with the brand on social media are more likely to make repeat purchases and develop targeted social media campaigns to encourage engagement and loyalty.

➡️ Variables could include for example:

• Social media engagement: This can include metrics such as likes, shares, comments, and followers on the brand’s social media platforms.
• Website engagement: This can include metrics such as time spent on the website, pages viewed, and actions taken on the site (such as adding items to a cart or completing a purchase).
• Email engagement: This can include metrics such as sign-ups, open rates, click-through rates, and conversion rates for email campaigns.
• Loyalty program participation: This can include metrics such as the number of points earned, rewards redeemed, and frequency of participation in the program.

👨‍👩‍👧‍👧 Demographic variables

This criteria involves segmenting customers based on their personal characteristics.

💡Demographic segments can be used to tailor marketing messages to resonate with specific groups of customers and provide personalized product recommendations based on the different need and preferences of the different segments. For example, a campaign targeting families with children may uses marketing messages that emphasize the convenience and durability of the products, while a campaign targeting single professionals may use marketing messages that emphasize the sophistication and style of the products.

➡️ Variables could include for example:

• Age: Segmenting customers by age can help businesses tailor their products and services to specific age groups.
• Gender: Segmenting customers by gender can help businesses create targeted marketing messages that resonate with male or female customers.
• Income: Segmenting customers by income can help businesses understand the purchasing power of different customer segments and create pricing and product strategies accordingly.
• Education level: Segmenting customers by education level can help businesses target marketing messages to customers with higher education levels who may have different interests and preferences.
• Occupation: Segmenting customers by occupation can help businesses tailor their products and services to specific professions or industries.
• Marital status: Segmenting customers by marital status can help businesses target marketing messages to customers with different household structures and needs.
• Number of children: Segmenting customers by the number of children can help businesses target marketing messages to families with different needs and preferences.

🌎 Geographic variables

Geographic segmentation divides customers into groups based on where they live or work.

💡This type of segmentation can be useful for understanding the different needs and preferences of customers in different regions and tailoring marketing messages and sales strategies accordingly. For example, a business operating in multiple cities may find that customers in one city prefer a certain type of product, while customers in another city have different preferences. Additionally, geographic segmentation can help you to identify new growth opportunities in different regions and expand your customer base by targeting customers in new geographic areas.

➡️ Variables could include for example:

• Country
• State
• City
• Zip code/neighborhood

🧠 Psychographic variables

Psychographic segmentation is based on customers’ attitudes, beliefs, values, personality traits, interests, and lifestyles.

💡Psychographic segmentation can be used to understand customers’ motivations and behavior patterns, as well as their emotional and psychological needs. By tailoring marketing messages and sales strategies to customers’ psychographic profiles, businesses can create more personalized and relevant experiences for their customers, which can help increase customer loyalty and retention. For example, a business targeting environmentally conscious customers may create marketing messages that emphasize the sustainable and eco-friendly nature of its products, while a business targeting adventure-seekers may create marketing messages that emphasize the thrill and excitement of its products.

Variables could include for example:

• Personality traits: such as introversion/extroversion, openness, conscientiousness, and emotional stability.
• Attitudes and beliefs: such as political views, religious affiliation, environmentalism, and health consciousness.
• Values: such as family, community, achievement, and status.

Interests and hobbies: such as sports, travel, music, and food.

✅So as a first step, select the segmentation criteria you plan to use. List your selected variables in the first area of your Customer Segmentation 📒Template. The variables you choose depend on the nature of your business, the data you have available and the characteristics of your customer base. Even if there is certain data that you do not have available yet, but which would be a game changer for your segmentation, put it on your list, you can still find ways to collect this data afterwards.

Also, consider that the criteria you use for your segmentation should be relevant, actionable, and aligned with your business’s goals and resources. Prioritize the criteria that are most likely to generate insights and opportunities for growth while balancing the effort needed to collect this data.

Step 3: Collecting Data

Now that you are clear which criteria you want to take into consideration you will have to collect and/or consolidate the customer data. You probably already have set up channels where you collect customer data and likely will have to add further channels to gain additional data.

✅ So to gain an overview go to your Customer Segmentation 📒Template and use the second area to:

1. List all the available channels that you currently use and the type of data (variables) that you collect through each channel by copying the variables you defined in the previous step. Use the ☑️ icon to market the existing channels.
2. Check which data (variables) is not collected yet and brainstorm through which channels you could collect this data. Add those channels to the list as well. Use the 🆕icon to market the existing channels.

Possible ways to collect customer data could be:

• Sales Data: Sales data is information on customers’ purchasing patterns, order history, and average order value that can be collected through your company’s sales channels, such as your website or physical stores.
• Customer Profile (Website Login): Customer profiles are created when customers log in to your website or app, providing information such as name, email, age, and other demographic data.
• Customer Surveys: Surveys are a way to collect various types of data like demographic data, customer preferences and needs, direct feedback or any information you ask for. Surveys can be conducted online, over the phone, or in-person.
• Customer Feedback: Feedback can be collected through various channels, such as feedback forms, customer support interactions, and social media conversations, to gather insights on customer satisfaction, preferences, and needs.
• Website Analytics: Website analytics tools collect data on customer behavior on your website, such as pages visited, time spent, and products viewed, to identify trends and opportunities for improvement.
• Social Media: Social media platforms provide valuable data on customer preferences, interests, and behavior through posts, comments, and engagement with your brand.
• Third-party Data: Third-party data sources, such as market research reports or public data sources, can provide information on customer behavior and preferences that your company can use to gain insights into target markets and consumer trends.
• Email marketing tools: Can be used to collect customer data by tracking customer interactions with email campaigns. These tools can provide insights into customer behavior, such as email open rates, click-through rates, and conversion rates.

✅ You will then have to consolidate the data that you collected from the different channels.

You can do so using, for example, a customer data platform (CDP) or another database or business intelligence tool. A customer data platform (CDP) enables you to collect, organize, and analyze customer data from multiple sources in real-time. It unifies customer data from various channels and provides a centralized platform to consolidate and manage customer data and segment audiences.

There are a number of CDP and analytics tools out there that you can use if you have a large quantity of customers and customer data. You will find a number of tools on the Data Analysis Stack 📋List below.

However, if you are just starting off and have a manageable customer base (up to around 25K records), a spreadsheet does the job as well.

We’ve prepared a Customer Segmentation Database 📒Template that you can adjust to your needs and collect your customer data.

Use the tab 🗃️ “B2C Customer Data” or 🗃️“B2B Customer Data” to collect your customer data (one line per customer). You can adjust the variable fields according to your needs.

Under the tab 🗃️”Segmentation Variables” you will find example lists for different types of segmentation variables which you can use or adapt to your need.

Step 4: Customer Segmentation Model

Now that you have determined your segmentation criteria let’s work on your segmentation and clusters.

To keep it simple at the beginning we recommend to stick with a maximum of four types of segmentations, which you later combine to form your customer clusters.

Start by defining the types of segmentation you want to take into account. As a minimum you should consider:

• Segmentation by Value
• Segmentation by Status

Plus any additional segmentation like:

• Demographic Segmentation
• Geographic Segmentation
• Behavioral Segmentation
• Engagement Level Segmentation

These segmentations can be simple segmentations based on one criteria only.

➡️ e.g. Status segmentation → Considering only the variable “Status”

Or they can be composed segmentations combining various segmentation criteria or variables

➡️ e.g. Value Segment → Combining the Average Order Value and Order Frequency

✅ So go ahead and use the second area Customer Segmentation 📒Template to:

1. Define the types of segmentations that you want to consider for your customer clusters. Keep in mind that your segmentation should include the customer’s activity status and the value segmentation, since these criteria are essential when it comes to defining a plan to grow those customers.

2. Specify the segments and the criteria that need to be fulfilled for each segment. Use either the template for simple or composed segmentation.

3. Unite the segments of the 4 segmentation types in the map and create and describe your customer clusters. The clusters you define should be meaningful with shared characteristics for segmented marketing and sales activities.

✅ Finally, transfer this segmentation to your Customer Segmentation Database 📒 Template.

First, go to the tab 🗃️ “Segmentation” and follow these steps for your segmentation and cluster definition:

1. Introduce the name of the segmentation (e.g. Value Segmentation).
2. Introduce the names of the segments (e.g. high value, medium value, low value).
3. Build the formula to automatically calculate the segment based on the given conditions. You can do so using the IFS/IF formula and the AND formula. (e.g. =IFS(‘B2C Customer Data’!K6<20, “Low Value”,’B2C Customer Data’!K6>50,”High Value”, AND(‘B2C Customer Data’!K6>=20,’B2C Customer Data’!K6<=50), “Medium Value”))

➡️ If you don’t know how to use the IFS/IF/AND formulas in Google Sheets or Excel, you can find more information here:

• IFS -Google
• IF – Google
• AND – Google
• IFS – Excel
• IF – Excel
• AND – Excel

Finally, repeat this process for the clusters using the newly defined segments as a basis.

✅ Next, go to the tab 🗃️“B2C Customer Data” or 🗃️“B2B Customer Data”. Here, you will find several columns under the name “customer segments”.

1. Introduce the name of each of the segmentations in the column header.
2. Copy the corresponding formulas to the first line under the header, then drag it down to the rest of the cells.
3. Once you fill in the formulas for your segments, repeat this process for the clusters.

And with this you should now be able to assign the corresponding segment to each client in your list. You can then easily filter your clients by segment.

Step 4: Customer Segment Analysis

Finally, once you have assigned the segments and clusters to each customer on the list, you can analyze each segment and cluster to understand which ones are the most profitable.

✅ You can do this by simply using a Pivot Table analysis. We already prepared a Pivot table for your clusters and each segmentation type. You can find them in the tabs 🗃️ “B2C Cluster Analysis”  and  🗃️ “B2B Cluster Analysis”.

Here you can see the following KPIs for each cluster and segment:

• The total number customers
• The total revenues
• The total number of orders
• The average order value
• The average number of orders

You can, of course, adjust this analysis to your needs by exchanging or adding the variables you would like to analyze (in values).