Traditional segmentation often starts with characteristics. Demographics, or firmographics for businesses, are often descriptions of who they are like age, gender, race, location, industry or job, revenue or salary, etc. An easy approach, especially when third party data can fill in the blanks. But it’s less helpful for predicting individual needs.
To quote Clayton Christensen, creator of the jobs to be done framework:
“I’m 64 years old. I’m six feet eight inches tall. My shoe size is 16. My wife and I have sent all our children off to college. I drive a Honda minivan to work. I have a lot of characteristics, but none of them has caused me to go out and buy the New York Times this morning. My reason for buying the paper are much more specific. I might have bought it because I needed something to read on a plane or because I’m a basketball fan and it’s March Madness time. Marketers who collect demographic or psychographic information about me — and look for correlations with other buyer segments — are not going to capture those reasons.”
In depth research, talking with and listening to customers, reveals these underlying motivations, or “jobs” that your bank or credit union is being hired to do. Provide a place I can deposit my paycheck and sleep well at night knowing it’ll be there when the rent is due. Save me the headache and stress of managing and paying bills. Help me move into a different house when I get married, or newly single, or have kids, or they leave the house, or I retire.
Looking at the profitability of customers, and grouping them together can be a shortcut to finding similar patterns of usage and the underlying “jobs” they are solving.
Estimating Customer Lifetime Value (CLTV) can also provide clues that your prospect or customer is likely part of one of these segments before establishing that pattern of behavior with you.
Whether they are already a customer or are just opening their first account, knowing how much business they will do with you is a powerful tool for providing the differentiated services that serve as competitive moats for community FIs.
CLTV* can be defined in several ways. The most useful is the estimated future profit or net income a given customer will generate. We’ll cover different techniques and their pros and cons in future posts, but here is a simple made-up example.
A savings account depositor has an average balance of $7500 and keeps that balance for 1 year, then withdraws their money. At a net interest rate** of 3%, you would expect to generate ($7500 x 3%)/2 = $112.50 in revenue and the average cost to service an individual account is $50 per year. Your expected lifetime value of that customer is $112.50 - 50 = $62.50.
(divide the revenue by 2 with the other half of the interest rate spread is attributed to the borrower).
So in that example that customer is worth $62.50. Knowing that can help determine how much you should spend to bring that customer through the door.
It can also help point you in the right direction to try and drive up the profitability of your relationship with the customer. There are primarily 2 ways to do this:
Keep customers loyal, increasing how long they stay customers (have them keep their $7500 in that savings account longer than a year)
Growing their value/profitability as a customer, increasing the amount of business they do (deposit more, or get their next car loan from you, or open a checking account and use your debit card for day to day transactions)
But both of those efforts require a targeted and personalized approach.
Look at your existing CLTV as a precursor to personalization. It's a highly effective way to leverage the data at your fingertips.
By analyzing CLTV, you are forced to look at the granular drivers of your business. The results provide insights into usage habits, transaction frequency, and other key metrics. Within these patterns of usage, groupings of customers emerge. Often ones that defy intuition. Market leaders use these insights to develop personalized marketing and customer service strategies.
Understand your customers beyond basic observations for higher engagement, longer retention, a more effective and efficient organization.
This level of insight will drive more profitable customer relationships. We’ll dive into this topic in future posts. We are going to cover techniques from institution wide averages that can be done in a simple spreadsheet, to machine learning approaches that can route customers in real time to personalized offerings and experiences. All of this is available to your organization today.