A customer wants a better credit card interest rate from your financial services firm. Choosing between “yes” and “no” is not as simple a decision as it might appear. It’s like playing high-stakes poker. Should you hold ‘em or fold ‘em? Should you spend money advertising new investment and insurance products to this customer? If you play it the wrong way, you risk losing him to a competitor. But, maybe that’s what SHOULD happen. Making the right call requires an in-depth, data-driven analysis of the customer’s lifetime value. This is what customer retention analytics is all about.
Customer Retention and the Lifetime Value of a Customer
A skilled businessperson tries to look at customers as producers of recurring income. A one-and-done customer relationship is usually a losing proposition. Let’s say it costs you $200 to acquire a new credit card customer for your financial institution. If that new cardholder makes some purchases on the card, pays you $100 in interest and then cancels the card, you’ve just lost $100.
However, if that customer pays you $100 in interest on his card balance every year for 10 years, you’ve earned $1,000. To be true to financial principles, the present value of that $1,000 is more like $614, if your cost of capital is 10%. The lifetime value of the customer is $614. You’re $414 ahead. If you can hang on to him for 20 years, you’re up to $851 in lifetime value. By the way, if you can move that customer up to $150 a year in recurring revenue, you’re going to see a 20-year lifetime value of $1,277.
Earning that lifetime value involves retaining the customer. Customers have no shortage of chances and reasons to defect to the competition. Can you really hang onto someone for 20 years? Yes, you can. However, this requires knowing the customer, what he values and what will keep him loyal. This a matter of data.
The Role of Data in Maximizing Customer Lifetime Value
Hold ‘em or fold ‘em? Should you invest in a customer or ignore her? Making the right decision should be based on understanding the customer’s potential lifetime value. The higher the lifetime value, the more investment the customer deserves – because the investment will (in theory) improve customer retention and reduce attrition.
One data point you need to know is the raw dollar potential of the customer. This is a modeling exercise based on historical patterns and educated guesses about the future. It requires up-to-date demographics on the customer to answer key questions. For instance, would you invest extensively in building an enduring relationship with a 90-year-old customer? You might, if the value is right.
Another necessary data point revolves around estimated future activities. How much money will a customer spend next year? If he signs up for investment services, will the earnings from his investment account be worth the cost of signing him up? Figuring this out is an analytics challenge.
Customer Retention Analytics – How It Works
The hold ‘em or fold ‘em decisions around customer retention are not new. Before the age of advanced customer retention analytics, businesses decided how to treat customers using coarse-grained data - like zip codes - to determine whether someone was worth the money to retain.
Zip codes are actually not a bad way to figure out which customers are best. If a prospective customer can afford a house in a nice zip code, maybe she would be a good risk for a new credit card. However, in today’s hyper-competitive financial services marketplace, the analysis must be far more sophisticated.
Hold ‘Em? Who Is the Most Valuable to Retain?
Not every customer prospect is the same. Two people can look quite similar on the surface, but present totally divergent lifetime value potentials. Customer A lives in a $300,000 house in the same zip code as Customer B, who also owns a $300,000 home. They each have $5,000 balances on their credit cards. Are they both worth investing in to retain?
Customer retention analytics lets you go deeper to understand the pros and cons of retaining Customers A and B. For example, you could run analytical processes to see who has paid more interest on their cards, matched against their annual spending patterns. It may turn out that Customer A has paid a lot of interest because he made one huge purchase a year ago while Customer B spends the same amount each month. This might indicate that Customer B is a better candidate for long-term retention investment. But wait... Customer B has too much debt. He may not keep up with the spending. Maybe he’s not such a good investment after all and can churn.
Fold ‘Em - Who Should Be Let Go?
Some customers need the cold shoulder. That may not sound nice, but it’s a very salient fact in business. Spending on retaining a losing customer is a waste of time and resources. Customer retention analytics can give you insights into who is worth dropping (or ignoring.)
For example, you can use analytics to determine who is more or less engaged with your financial brand. You could measure their revenue against factors like emails-opened and ads-clicked-on to see if they’re interested in working with you. You might discover that a customer is small now, but has a lot of future potential.
Moving Ahead with Customer Retention Analytics
It sounds easy when we talk about one customer versus another. In real life, you’re going to need to run many different kinds of customer retention analytics processes and predictive modeling on millions of customers and prospects. This is a large-scale data science, data management and analytics challenge. We have worked with many clients on this challenge. If you think your business could benefit from the implementation of customer retention analytics, let’s talk.