Leveraging Predictive Modeling of Customer Data for Personalized Marketing Campaigns

March 5, 2017

Gap Inc. wanted to turn unused customer purchasing data into building blocks of highly effective personalized marketing campaigns to increase sales and improve customer retention.

Before the data lake: Gap did not have detailed understanding of customers’ purchase behaviors, how they interacted on the brand’s website, and how they responded to marketing and promotional activities. Accessing data for analysis was difficult, as it was scattered in multiple, siloed databases throughout the company, resulting in millions of customer records and purchasing data going unused.

With the data lake: Gap was able to ingest, transform and map the data so that the output datasets could be used for creating predictive models. The company now had access to a single dataset to feed a suite of algorithms that created a more personalized customer experience on the retailer’s e-commerce site. 

Results: Gap was able to uncover customer behaviors and trends that could be used to develop and implement highly targeted, personalized digital marketing campaigns to increase sales.

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