Use customer segmentation

Explore the Customer segmentation page and learn how the segmentation works.

Your data access might impact the segmentation results you see. For example, if you're restricted from accessing some customers, they won't be included in the segmentation.

Customer segmentation, a core feature in Insights, automatically analyzes purchasing behavior to group your customers into meaningful segments and uncover key insights. The insights update as your data changes, so there's no need for manual recalculations.

Whereas you might already have a clear picture of your top customers and have strong relationship with them, it’s likely you don’t know much about those customers that don’t buy often or much from you. You might not be aware of potential risks of losing customers.

These insights give you a better understanding of all your customers, helping you to quickly answer key business questions and take informed, targeted actions to drive retention, loyalty, and growth.

Take a tour of Customer segmentation

The Customer segmentation page is organized into several sections, as identified by the numbers in the image and explained below.

1. Configuration bar: Search for a customer, resegment or filter customers, or download the data. If you're the Administration user, you can also edit or delete the configuration settings from here.
2. Customer segmentation summary: This is a quick overview of your most up-to-date customer data. It shows the total number of customers who were active within the current period (by default, the last 12 months). The summary cards on the right highlight the total sales value and the total number of active customers, so you can quickly understand the overall scale of your customer base and sales without digging into detailed data.
3. Customer segmentation chart: This is a visualization of how your customers are grouped based on their buying behavior, with supporting details.

   • The chart visually groups your customers into segments (Champion, Loyalist, At risk, and so on) based on how rec ently and frequently they buy, and how much they spend.
   • Each block in the chart contains the segment name and number of customers in that segment. The size and position of each block correspond to how that segment is structured (thresholds). It bears no relation to how many customers are in the segment. For example, segments higher on the chart show higher sales value; segments further right are more recent buyers.
   • For more information, see View segment details and Understand the segmentation below.
4. Customer segmentation summary table: This is a table that gives you a high-level view of all the segments and what they mean, and how the distribution of customers and revenue is split across them. It’s a quick way to compare the following metrics across all segments: Number of customers, average sales value, and total sales value.

An overview of the Customer segmentation page

View segment details

Hover over a segment in the chart to view a summary of its details, including how much revenue it represents.

Click a segment in the chart to view its details on the right side of the chart, in summary cards and a table.

Understand the segmentation

To understand how your customers were segmented, and the chart was created, click the purple Information button next to the chart section header. This opens the Segmentation setup where you can see:

• A table of the Recency, Frequency, and Monetary (RFM) thresholds configured for the underlying database. They're grouped into five buckets (range 1 to 5) that determine the size and position of the segments in the chart.
• Brief description of the terms Recency, Frequency, and Monetary.
• Information about where the data comes from, including the dimension that was used in the segmentation calculation.

If you're the Administration user, this information will be familiar. For all other users, this is a handy snapshot of what's going in behind the scenes.

Learn more about RFM and the maths behind it

The Customer Segmentation feature uses the Recency, Frequency, and Monetary (RFM) model to group your customers into meaningful segments and provide instant insights into customer behavior across their purchasing journey.

Additional resources on our website:

• Read this blog to learn more about the RFM model and how it can transform your sales approach: Customer segmentation for wholesalers that your customers will appreciate
• Read this guide to learn how you can maximize the potential of your data: Smarter selling starts with segmentation

The RFM model is an industry-standard analysis technique. It’s often used to manage customer accounts. As its name suggests, this model uses three key metrics to assess and rank customer behavior, as described in the table.

Metric	Definition	Why it's important
Recency	How recently did the customer make a purchase?​ Higher threshold = more recent	Customers who purchased more recently are more likely to buy again, compared to those who haven’t purchased in a while. They're also more likely to react positively to marketing campaigns and promotions.
Frequency	How often does the customer make a purchase over the time period?​ Higher threshold = more frequent	Frequent buyers are generally more engaged and satisfied, therefore, more loyal and valuable to the business.
Monetary	How much did the customer spend over the time period (total spend)?​ Higher threshold = higher monetary value (more spent)	Customers who spend more are typically more profitable and worth nurturing. Combining monetary value with how recently and often a customer buys can help identify loyal, high-value customers.

How segments are determined

Segments are based on all three RFM scores working together, not just one score.

• Recency (R) determines the position on the X-axis
• Frequency (F) and Monetary (M) are combined to determine the position on the Y-axis

To calculate the combined Frequency and Monetary score:

• Add the Frequency score and Monetary score
• Divide by 2
• Round up to the nearest whole number

This means a lower Monetary score might be offset by a higher Frequency score.

Example:

Suppose you are looking at a specific customer in the Champion segment and are wondering how they got there, as they did not spend much.

Here are some sample numbers to show how the RFM scores work together:

• Monetary = $1,300 → score of 3
• Frequency = 19 → score of 4

Combined score: (3 + 4) / 2 = 3.5 → rounds up to 4

If this customer also has a high Recency score, they fall into the Champion segment.

This is expected behavior. The model is designed to identify highly engaged customers (recent and frequent), not just those with the highest spend.

How Phocas applies the RFM model

Phocas analyses the data in the selected database within the specified time period (the default is the last 12 months). It scores the customers on each of the three RFM metrics, on a scale from 1 to 5, where:

• 5 = highest recency, frequency, or monetary value.
• 1 = lowest recency, frequency, or monetary value.

It combines these scores into an overall RFM score from which it builds a model of your customers’ behavior, presented in a chart format.

Apply customer segmentation insights

Use customer segments to guide your sales and marketing activities. For example, you might:

• Focus on high-value customers and prioritize opportunities with the greatest return.
• Track purchase behavior, such as how often customers buy and when they last purchased.
• Adjust your outreach based on customer activity. For example, re-engage at-risk customers, tailor marketing campaigns, and focus your resources on the most valuable segments.

See Take action on customer segments to use your segmentation data in other Phocas modules.

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FAQs

Here are answers to some frequently asked questions about the Insights: Customer segmentation feature.

1. Is the segmentation always done on a 12-month basis? Can I update it for seasonal customer behavior? Currently, the segmentation is calculated across a-12 month period. In the future, we'll be introducing for flexibility in the segmentation setup, allowing you to choose a specific time period.
2. Can I add additional measures to the segmentation summary, such as margin percent? Not right now, but we're working on it!