Most Customers Look Profitable. Only a Few Actually Are.

For leaders in strategy, finance, and customer insights, the implication is clear: revenue data alone is an unreliable compass for identifying your most valuable customers. Firms that rely solely on transactional history risk allocating sales, marketing, and service resources toward segments that look attractive on surface metrics but deliver marginal—or even negative—economic returns after costs are accounted for.

Are Some Customers Costing Your Company Money Instead of Contributing to the Bottom Line?

• Company segment customers to identify the “best” customers–few do this well.
• Most firms identify “best customers” on gross sales–but these huge buyers often require more service and support, command significant discounts, and negotiate terms that may substantially reduce their profitability.
• Customer Value Analytics (CVA) based on internal transactional data – when combined with survey data – can provide the most profitable solution.

To Estimate long-term value, we need Big and Little Data

Start with Your Customer Data

Transactional data, can be used as a starting point to estimate the lifetime value of each customer. By transactional data, we mean customer data such as how much they buy, how much profit is made on products they have purchased, and how often they purchase. But even mountains of transactional data provide minimal information to identify potential NEW customers and what their value might be.

Current Customer Data is Limited – We Need to Understand “Why” They Buy

Looking at the profitability from a customer’s past purchases, we can estimate future value by projecting into the future. While this gives some estimate of our best customers, it does not tell us if there is a customer who should be buying more from us. To estimate this, we need to understand why that customer is buying – I call this Little Data.

To Estimate Long-term Value, We Need Big and Little Data

By Little Data we mean data collected from relatively small sample size research studies to identify customer needs gaps and pain points. We may survey only 500 to 1,000 customers and prospects. But this data is invaluable to allow us to understand “why” customers buy. Once we have our survey data, we can use that to segment customers and develop a predictive model to extrapolate our findings to all customers and prospects.

3 Steps to Identify Truly Profitable Customers

Identify Truly Profitable Customers through Statistical Modeling

To estimate the value of current and potential customers, we develop a multivariate mathematical model. These could include neural networks, clustering, structural equation modeling, latent class analysis. To accomplish this:

1. Identify what makes customers profitable. This is typically accomplished by conducting a segmentation study to group customers and non-customers into 2 or more segments based on their profitability/value.
2. Develop a mathematical model to predict segment membership. We have found that most of our Fortune 500 clients cannot accomplish this with transactional data alone. However, by appending additional, data (such as from Dun & Bradstreet or other secondary data sources) we will have the necessary raw data to develop predictive models.
3. Use the model to tag all of the customers and prospects into value segments. This allows us to maximize profits by focusing on customers and prospects that fall into our most profitable segments.

Example: Determine Customer Value Using Big & Little Data

Let’s say that we conduct a survey that includes both customers and non-customers. We segment our customers and prospects based on their purchasing attitudes and user needs. As an example, let’s pretend that we uncover three main segments: Price Seekers, Feature Seekers, and Brand Loyalists.

Price Seekers – driven to get the best value.

• They have little brand loyalty and buy only when our products are on sale or if they can negotiate large price discounts.
• By looking at our historical transactional big data, we see that customers who fall into this segment have not been very profitable.

Feature Seekers – tend to focus on advanced features.

• They buy our more advanced, higher margin products but rarely buy our profitable service contracts.
• While these products are highly profitable, they are only a small percentage of our overall sales.

Brand Loyalists – They seek excellent service, are willing to pay a premium and want to build relationships with one firm.

• Our transactional big data shows us that they consistently re-order and often sign up for highly profitable add-on service contracts.
• These are our most profitable customers

The average lifetime value of customers for each segment is then calculated. This is done by using transactional big data combined with supplemental purchased data (such as from D&B) to help cluster customers by segment. Each segment is evaluated for its profitability per customer and by the size of the segment.

Using these customer lifetime value results, we can target not only the profitable segments but the most profitable customers & prospects within a segment. We now have a more effective and efficient way of identifying which customers to target and retain.

Maximize Revenue by Targeting Truly Profitable Customers and Prospects

By combining two sources of Big Data (in-house transactional data with purchased secondary data) along with Little Data (custom market research survey results) we are able to develop a mathematical model that can surpass Big Data predictive models based on in-house data alone. These improved models can be used to target customers and prospects in the desired segments.

• We can use our new predictive model to mark all customers in our database so that we can focus on those of greatest value.
• We can better target advertising messages to specific profitable segments.
• Prospects can be more efficiently targeted by purchasing lists of specific types of customers that are more likely to be high-value but who have never purchased from us.

What This Means for Decision-Makers

Before you make your next big investment in customer growth or retention, commit to these steps:

1. Reframe the question — Don’t ask “Who buys the most?”; ask “Who contributes the most profit, net of costs?”

2. Segment with economics in mind — Use segmentation studies that explicitly incorporate profitability drivers, not just revenue behavior.

3. Build predictive models — Augment your internal data with external sources to improve statistical classification of customer value.

4. Tag your portfolio — Apply your model to assign all current and prospective accounts to value segments.

5. Invest where it counts — Prioritize strategy, sales coverage, and service effort toward segments with true profit potential, not just surface revenue.

These actions transform ambiguous performance metrics into decisions you can trust—so your next dollar spent is more likely to generate real return.