Retail is changing

 

…and so should the approach towards promotions in bricks-and-mortar retail.

 

Retailers need a system that will attract new customers and earn their loyalty, while at the same time increase their basket size and optimize discounts towards higher profits.

 

Modern machine learning algorithms are able to leverage the incredible amount of customer data retailers have. They can spot even the tiniest clues in customers’ behavior and recommend the right product at the right time for the right price.

 

Real environment tests with some of the major German and US grocers showed that AI is able to increase redemption rates more than 8 times, basket size by over 15.8% and profits by 36.4%.

 

All of that because AI doesn’t need to segment customers. It can work on 1:1 level, recommending products for individual customers for the price matching their true willingness-to-pay.

 

In this paper, we will examine 7 business cases supported by real world evidence. Have a look and consider how these use cases can be applied to your business.

 

 

Table of contents:

  1. Maximize offer redemptions & minimize discounts
  2. Increase basket size and incremental revenues
  3. Attract additional revenue from brands
  4. Increase customer loyalty
  5. Optimize for any channel
  6. Consider changing business goals
  7. Reveal strategic insights on customers

Revenue uplift and discounts saved:

Example figure 1: Revenue uplift (increase in targeted basket size) vs. percentage of average discount offered. This impact of individual promotions has been proven by rigorous A/B testing at all participating retailers.


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