Schooling ‘Curmudgeon’ Denny Hatch on ‘New-School’ Marketing
Figure 2 (at right) shows the results of an even more stringent test using product purchase propensities. For approximately 1 million customers and thousands of SKUs, probabilities were calculated for individual customers to make a repeat purchase of particular SKUs. Probabilities were assigned to the many combinations of individual customers and particular SKUs. Again, the customer/SKU combinations were bucketed according to probability of purchase. The chart shows how closely the predictions matched actual purchasing behavior in the next month.
The conclusion from these charts is crystal clear: Proper analytics can predict which customers are likely to buy, when they will buy and what products they will purchase. Even cross-sell (selling a product the customer has not previously purchased) can be predicted with similar accuracy.
These results depend on using transaction data for individualized analysis, and on making relevant offers to individual customers based on that analysis. The whole analysis-campaign process can be automated, and scales to tens of millions of customers.
What the Curmudgeons Are Missing
There is still a place for the old school data the curmudgeons love, but its role is more supplemental than fundamental. Use it to enrich the messaging, but recognize that transaction data is more predictive. The new metrics, based on that transaction data, are predictive rather than just measurements of past behavior. That makes them actionable. Changes in a customer's Risk Score, for example, can be used to trigger a retention campaign message. Likely Buyer Score can tell a marketer when to send some expensive collateral. Purchase propensities inform what offer to make.
The customer relevance that both curmudgeons and new school marketers want won't come from product-centric analysis. If you want individualized marketing, you need to analyze at the individual level, not the segment level, and you need to describe or profile customers based on customer-level analytics.