Case Study: Ecovis Data Analytics helps client save 17% annual costs through improved targeting and predicting customer response
A luxury fashion retailer optimised their marketing spend by using a machine learning model that predicts who is most likely to respond to catalogue sales campaigns with high accuracy.
How did we help?
Using data describing their customers’ purchasing habits, we were able to deliver a self-service machine learning solution which this company can use in advance of each marketing campaign to predict who is likely to make a purchase, thus allowing them to improve their targeting, save costs and reallocate their marketing budget smartly towards other initiatives.
- a highly accurate model resulting in saving 17% of annual catalogue mailing costs while losing fewer than 0.5% of buyers
- a range of options to tune how the model makes predictions to fit their needs
- Additionally we advised on uncovered data discrepancies, contributing to a process improvement programme to ensure that their data will be captured consistently and accurately.
The CEO of a luxury fashion retailer comments:
“Our catalogue marketing campaigns are hugely important for our business. With the model that Ecovis delivered, we are now able to select our catalogue audience according to their likelihood of responding. We expect this to return to us an annual net saving of approximately 17% of our annual catalogue mailing costs for minimal loss of demand. The data analytics team at Ecovis understood the scope of the work, were professional and patient, and delivered exactly the end product that we needed. This was a dream project for us, and I look forward to having their ongoing support.”
Reuben Barry, Director of Data Analytics, ECOVIS Wingrave Yeats, London, UK