The problem this bank faced was identifying the most likely prospects to shop in a particular merchant among those who normally did not shop at that merchant. Previously, the bank was focusing efforts on a broad group of customers who had previously shopped in that merchant or other merchants that they deemed to be similar. This heuristic (trial and error) method yielded mixed results as the marketing staff could not possibly look at the massive amounts of data available that might yield new customers; this was a manual process, after all.
The OneAnalytix "black box" predictive analytics identified micro-segments within the customer base that were highly promising new prospects based on a probabilistic calculation of likelihood of interest. This approach offered up to an 1100% improvement in spending over the baseline heuristics method for certain merchants. In all cases, results were substantially better using predictive analytics versus heuristics.
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Merchant Spending Increase Campaign
The problem this bank faced was identifying the most likely prospects to shop in a particular merchant among those who normally did not shop at that merchant. Previously, the bank was focusing efforts on a broad group of customers who had previously shopped in that merchant or other merchants that they deemed to be similar.
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Selling ad opportunities to package goods companies/agencies
The operator of a loyalty program that includes a diverse group of outlets - shopping malls, hotels, supermarkets, schools, sports, retail - wanted to maximize the opportunities to sell targeted product offers to those customers most likely to be interested in those offers.