The main goal was to improve sales and customer satisfaction by sending personalized offers to end-users in a pool of over 3M customers.
Accordingly, our team defined the best strategy rendering the past customer sales and integrating commercial and marketing inputs. As a consequence, we were able to test different campaign strategies, as well as to predict their impact.
The customer affinity towards promotional offers, gathered from predictive modelling, was worked on by a robust, scalable optimization engine able to pursue different objectives, according to business demands.
This decision support tool routinely scrutinizes over 500M pairs of customer-stimulus, simulating campaign performance and ultimately arriving at an optimal 1 to 1 targeting and acquisition offer selection.
This agile scenario managed to successfully improve targeting and to tailor the end-user experience, which was manifested through an increase in overall coupon redemption.
Hence, not only did our approach reduce offer distribution and total campaign lead time, but it also brought value to the company and to its customers with these enhanced solutions.