Using Big Data to Understand Who is Most Likely to Respond to a Marketing Campaign

Big Data represented by interconneccted nodes showing faces

A retailer with one of Australia’s largest customer loyalty databases were spending excessive amounts of marketing budget on online and addressed mail marketing campaigns, to find return on investment (ROI) had stagnated, as they were targeting all loyalty customers equally.

Using big data, Geotech Information Services undertook analysis to determine not only who their highest value customers were, but also the characteristics of who would be most likely to respond to a marketing campaign.

This resulted in identifying the demographic attributes and spending behaviours of their highest value customers, and of the customers that were responding to the different marketing campaigns. It also revealed how a customers location relative to their closest store, as well as location relative to competitor stores, influenced their decision and propensity to respond to marketing material.

The result was a 9 segment customer matrix with recommendations for sampling for marketing campaigns, dependant on the segment the customer fell in to.

Therefore, it made sense to target those high value customers more conveniently located to a store more often, as there was proven higher ROI from these customers.

By having a more targeted marketing approach moving forward, this saved the company significant time and money, and resulted in a significant uplift in response rates and spend from their marketing campaigns, and ultimately ROI.

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