If you can predict the lifetime value of current and prospective customers, you can make educated decisions on what efforts should be made to retain or recruit these customers, if any. – Dr. Peter Fader, Wharton Business School
Virtually every company segments its customers and identifies the “best” customers. But few companies do this well. Most firms base this list of best customers on gross sales. But these huge buyers often require much more service and support, command significant discounts, and negotiate terms that may substantially reduce their profitability. Customer Value Analytics (CVA) based on Big Data – when combined with survey data – can provide the most profitable solution.
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.
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.
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.
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:
Let’s say that we conduct a survey of 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.
Feature Seekers – tend to focus on advanced features.
Brand Loyalists – They seek excellent service, are willing to pay a premium and want to build relationships with one firm.
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.
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.