A retailer targets offers to increase redemption revenue by 230%
The Big Picture
A leading specialty retailer wanted to better understand its customer base to design more personalized and relevant pricing and promotions. Additionally, the company identified its localized store assortments as key drivers of incremental foot traffic and sales.
However, the company had huge volumes of data on its customers, products, transactions, demographics, and more, from different data sources, with each business unit working in silos to address their own priorities. This created situations where competing campaigns often resulted in low redemption rates.
To address its challenges, the company created a 360-degree customer view and developed over 50 customer markers around life stage, life segment, loyalty, purchase behavior, competition data, digital, and more. This was done for more than 60M households, using dynamic machine-learning algorithms.
The customer markers were used to understand purchase patterns by different stores and customer types to drive localized product assortment. Then, the company created retargeting campaigns across business units to reach out to customers with targeted offers. The design of the campaigns was driven by intelligence on customers, products, and offers. To further refine targeting criteria and drive campaign effectiveness, redemption patterns were analyzed and incorporated into the models.
As a result of the engagement, the company increased its redemption revenue by 230%. Powering and refining offers with data enabled the company to reduce its issuance of generic offers to customers from 23.2% down to 4.5%. The company rolled out over 400 retargeting campaigns across 14 business units.