How to measure customer value with analytics

If you work across wholesale distribution or retail, you need to meet customer expectations and differentiate your offer from competitors. Businesses can no longer afford to view customers as a homogenous group defined only by revenue. Instead you need to use customer value analytics (CVA) to measure customer profitability accurately.

By analyzing customer data, sales managers and finance professionals can measure which customers deliver long-term financial value, those that have a high cost to serve and others that offer opportunities for growth. Understanding these segments enables better decision-making around pricing, retention and customer experience. Ultimately, measuring customer value provides clarity on where to allocate resources for the best impact on the bottom line.

The benefit of measuring customer value

Customer value analytics is about quantifying the worth of a customer to the business over time. Companies often rely heavily on top-line indicators like total revenue, total orders or transaction counts. While useful, these metrics often mask the reality that not every high-spending customer is profitable, and not every low-spending customer is unprofitable.

For example, a customer who generates substantial revenue might also be expensive to serve which means they place small, frequent orders, request frequent support or demand customized solutions. On the other hand, a seemingly modest customer could be highly profitable due to low cost-to-serve, timely payments and high loyalty.

With customer value analysis, sales people and finance teams can identify these nuances, avoid misallocation of resources and focus on strengthening customer relationships that maximize both profitability and long-term sustainability.

Building customer profiles and segments

The first step in customer value analytics is to develop detailed customer profiles. Profiles help businesses categorize customer segments and understand differences in behavior, needs and profitability.

Profiles can include demographics, sales habits, needs and payment behaviour. To make these profiles useful, companies find it worthwhile to them group them into segments. One of the most effective customer segmentation methods is the Recency Frequency Monetary (RFM) analysis model. The RFM segmentation process was developed by Jan Roelf and Tom Wansbeek in the 1990s.

At Phocas, we studied how much time sales and marketing teams were wasting on manual segmentation processes. That’s why we designed Insights, an RFM-based customer segmentation tool built to connect to Phocas Analytics. Customers nominate the typical parameters within their companies for when customers buy and the frequency that aligns with their business norms.

Insights then uses your customer data in Phocas Analytics to group customers into 10 segments like ‘champions’ at the top or ‘at-risk’ at the bottom. It helps wholesalers and retailers focus on accounts with the highest potential return and clearly summarizes all customers that qualify in each segment.

Key metrics in customer value analytics

Measuring customer value requires a combination of historical and current data so you can obtain a comprehensive picture of customer profitability.

There are 4 common metrics used to measure customer value.

1. Historical value

This measures value over a set period like a quarter or a fiscal year. By comparing historical performance across your customer base, companies can benchmark profitability, track trends and identify which types of customers drive consistent returns.

2. Current value

A short-term view shows the immediate impact of marketing efforts, promotions or pricing strategies. Current value can help detect emerging high-value customers or highlight risks of customer churn.

3. Customer lifetime value (CLV or LTV)

CLV is the cornerstone of customer value analytics. It estimates the total profit a customer will generate throughout their relationship with the business. CLV is calculated by multiplying a customer’s average order value by purchase frequency, and then extending that over their expected lifespan as a customer.

Understanding CLV helps businesses decide how much to invest in acquiring new customers, what to spend on retaining loyal customers and how to structure incentives for customer retention.

4. Cost to serve

Profitability is not just about revenue it’s also about costs. Customers who require frequent service, return products often or place small orders may erode profits despite strong revenue. By monitoring service costs, businesses can distinguish between loyal customers worth retaining and service-drain customers that may need a different customer service strategy.

Once data is analyzed, the next step is applying it to maximize profitability. Research shows it is more cost-effective to retain existing customers than acquire new ones. By identifying profitable customers early, companies can design strategies to strengthen relationships, improve customer satisfaction and reduce churn.

Analytics provides insight into customers’ price sensitivity and value perception, enabling data-driven pricing models. For example, businesses can offer preferential terms or loyalty rewards to high-value customers while adjusting margins for others.

Using dashboards and visualizations, businesses can track touchpoints across the customer journey. This helps improve user experience, ensure on-time delivery and streamline service.

With customer segmentation, companies can personalize marketing campaigns, focus resources on high-value customers and test use cases for new products. Market research combined with customer feedback enriches these insights further.

Data accessibility makes customer value analytics much easier to attain. By measuring profitability at the customer level and factoring in revenue, cost-to-serve and long-term customer loyalty, sales people can prioritize the work that is most lucrative. Customer value analytics also help your people align their value proposition with customer needs and strengthen the foundation for long-term growth.