Predictive analytics brings science to retail marketing. Along with personalization and customer retention, marketing campaign return on investment (ROI) is one of the most popular uses of predictive analytics, particularly in expensive channels, such as digital advertising, print direct mail, and enhanced customer service.

Predictive analytics increases ROI by increasing the odds of targeting the customers who will respond to your campaign. If you can accurately predict who will buy and spend the most as a result of your campaign, you avoid wasting campaign budget on the wrong customers and campaign revenue lift is higher per targeted customer. This can be proven with testing. And test results, in turn, can be used to refine the factors and models that will increase predictive accuracy. It’s a virtuous ROI cycle based on facts.

CASE STUDY

Our client, a retailer of specialty apparel and gear uses predictive analytics to remove customer groups expected to spend, on average, less than their direct mail margin requirements. Tests with control groups were used to find the minimum predicted average spend per customer required for the mailer to have the most impact on revenue lift – in this case above $4.00. Now, actual campaign results are fed back into the models to zero in on the individual customers where the mailer itself drives the sale.

To learn more about Predictive Analytics in Retail, read the related articles on Personalization and Customer Retention.

 

Peter Moloney is CEO of Loyalty Builders, a cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.

 

 

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