May 30, 2014
Published By : TechRepublic
When I was heading the marketing efforts of a financial institution years ago, we looked at how many customers were not actively using their credit cards. The dormancy level was nearly one third.
Dormancy matters when you start evaluating your customers in an effort to determine which are most profitable and which are dead weight. The income financial institutions derive when consumers use their credit cards comes in the form of interest on unpaid debt that is carried forward and that card users pay, but also in the form of Interchange income -- a portion of each charge that the underlying card issuer (e.g., MasterCard, Visa, etc.) pays the financial institution when its customers patronize the card. Interest and Interchange income can turn into big money.
Today's big data and analytics efforts bring welcome relief to banks, insurance companies, healthcare agencies, nonprofits, and other organizations that have habitually struggled with finding the most profitable customers and then selling to them. A new set of analytics reports can move these companies forward in connecting with their best customers.
Fractal Analytics talks about a banking user struggling in an industry where 40% of cardholders are inactive and 60% are unprofitable. The bank wanted to increase spending in its existing credit cardholder base, so it implemented a customer analytics framework that was targeted at improving first-hand understanding of these customers' needs. "Viewing our customers through this framework allowed us to appreciate the value of each customer," said the bank's vice president of customer marketing. "More importantly, it enabled us to design segment-based strategies to increase customer lifetime value."
Once the bank understood who its most profitable customers were, it developed a "genomic" understanding of how these customers spent their money and found that insurance and food expenses were among the leading "spend" categories. This enabled the bank to plan and target promotions built around these major spend areas. By doing so, the bank increased its value per customer while decreasing expenses on marketing campaigns, likely because the campaigns were better targeted.
Stories like this have encouraged companies to pursue genomic marketing that is propelled by big data analytics capable of profiling everything about a customer that an institution wants to know -- from his age, occupation, and amount of annual spend to how he appropriates that "spend" to his lifestyle needs and preferences. In analytics lingo, we call this a customer "genome" (i.e., the customer's unique and individual purchasing "imprint").
Genomic marketing abounds in many industry verticals today. We see it most often on large commercial websites like Amazon, which analyzes your recent buying behaviors and proactively recommends books or movies you might enjoy (and purchase), based on your prior buying patterns.
The good news for small and midsize companies competing in these markets is that cloud-based web information can be collected and analyzed as big data from internet activity. With the help of connecting application programming interfaces (APIs), this unstructured internet data can even be integrated with your systems of record data so you can get a complete look at your customers. This levels the analytics playing field for smaller companies when they go up against behemoth enterprises.
Operationally, companies still have to effect the necessary cultural changes to make their employees sales savvy and operationally competent. In banking, where tellers have historically been operationally oriented, some institutions have been hiring new employees out of the retail industry, where sales skills come with the employees. Now with the addition of consumer genome analytics, selling to profitable customers, understanding their needs, and filling those needs has gotten even easier.