New clients are often thrilled to tell us they have a loyalty program in place that’s stockpiling data on hundreds of thousands of customers. But they’re even more excited when they hear our response: There’s a lot we can do to improve the insights you gain from your data going forward. And we can do it quickly.
The key lies in being able to attach detailed household-level data to your customers’ transaction data, ideally including SKUs and buying frequency. Typically, loyalty programs track dollar amounts, tying them to a name and address only. But when that data is merged with our national household-level demographic and psychographic data, you can create an amazingly accurate portrait of who your most valuable customers are. With that information in hand, it’s easy to target new customers and build predictive models for future purchases.
We’ve also found that customers with the same demographic profile behave differently in different trade areas, or under different retail or restaurant banners. Knowing that, you can identify and quantify nuanced behavioral patterns within your customer universe, further refining your purchase models as well as plans for marketing to both existing and potential customers.
For example, if you know 75 percent of your customers who buy large flat-screen TVs also buy wall speakers, then you can customize marketing messages to that audience of potential buyers in a far more meaningful way.
By improving your loyalty program data, you can:
• Understand who buys X but not Y, and begin to market to your customers in powerfully personalized ways.
• Measure who your most valuable customers are and know exactly where to go to find more just like them.
• Define dollar decay by customer segment and define which customers should be marketed to as a convenience play.
Kick up the value of your loyalty program significantly by enhancing the data you have with more refined household-level data. You’ll be amazed at the difference predictive models can make for future business performance when they’re based on the buying patterns and preferences of your actual customers.