I still get offers for diapers. My child is a teenager. Five years ago I became a loyal buyer of a particular brand of yogurt. Yet for the past three years, I consistently receive offers on brands of yogurt that I don’t buy anymore. Some marketer somewhere missed the boat, and is working with old data, very old data.
Big Marketing decisioning needs to be based not only on historical data, but also on data being collected frequently – In some cases, via constant streams from any sources – It’s responsive to change. Learning from and adapting to changing behaviors and market conditions happen at both the back end and front end of decision processes.
At the back end, the broad range of data being analyzed and the emphasis on transactions means that predictive data elements are frequently refreshed. Many of our customers are generally collecting daily customer-level response data from transactions and generating new propensity scores for customers on a frequent basis. In addition, automation is used to update predictive models every 90 days to factor in changes in aggregate consumer behavior.
At the front end, companies use analytic learning loops to rapidly measure how customers are responding and to adjust decision strategies where necessary while campaigns are still underway. One retailer, for instance, has used this process to refine a lapsed-product offer strategy that is now driving unprecedented response rates.
Learning is swift since there’s no need to wait for all of the data to come in to determine which strategies are working and which aren’t. By comparing early actual results on KPI metrics against simulated results, marketers can extrapolate longer-term outcomes. With the ability to modify business rules without the need for IT assistance, they can change decision strategies in minutes.
For more information on this topic, check out our Insights paper (registration required).