Increase Revenue With Optimized Promotion Scheduling for Retail

Level-up your promotions with smarter scheduling thanks to augmented datasets
The retail industry has become hyper-competitive as sellers have started to compete not just with physical stores, but also virtual ones. In such a crowded landscape, one area where retailers can stand out and improve their bottom lines is by programming and launching promotions for their products. However, simply starting a promotion isn’t a guarantee that it will work.
The problem

A global retailer built a promotion optimization model but trained it exclusively using previous promotions and sales data. Although previous promotions have seen some success, the company has had trouble getting its ROI on recent promotions past 65% in terms of revenues per dollar spent. As a result, the retailer was seeing flagging sales at some of its most important stores, and couldn’t find the right promotions schedule to boost its new products.

The solution

To improve the impact of its promotions, the company created a new promotion scheduling model that added external data from Explorium’s Enrichment Catalog. With this new enhanced data, the company could identify a variety of key indicators, including: 

  • Usage of the word “Coupon” in search engine results based on specific products or search terms 
  • The percentage of married couples who are purchasing at a specific store
  • The number of retail stores in the same segment within a defined area, to understand the level of competition
  • The median income in a specific neighborhood, which can help identify the right price point for specific promotions and product types
The results:

Improved ROI from dynamic promotion scheduling

By connecting their datasets to Explorium, the retailer was able to boost their ROI on promotions by 14%, while managing a much more efficient promotion calendar that showed a 4% uptick in store revenues across their locations.

Schedule a call and learn how you can optimize your promotion schedule with better data.

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