Retail Promotional Optimisation & REVENUE GROWTH

Custom store promotions to increase customer acquisition

Trusted Data worked closely with a regional retail chain to drive revenue growth by identifying a unique approach to incentivising repeat customer visits, through dynamic promotional offers based upon predicting individual basket preferences

How we facilitated an 8.8% revenue increase for the client

Trusted Data were asked to support a top 3 national grocery retail group.

The client focused largely on the ‘value’ segment of the market with a diverse SKU portoflio (92,000 SKU’s) across over 140 national retail outlets.

Revenue had been stagnant for the previous 5 quarters and the client wanted to acquire a deeper understanding of how its’ pricing and marketing decisions affected consumer behaviour.

With more traditional marketing tactics and media spend failing to impact revenue growth, Trusted Data were engaged by the Chairman and CEO to find a solution.

Leveraging Machine Learning to improve forecast accuracy


The clients’ promotional tactics and marketing communications had resulted in a poor response to in-store promotions

Offer campaigns did not sufficiently align with product movement and store-level consumption dynamics

Aggregating consumer behaviour at a national level would not effectively consider local demand and consumer preferences. 

Effective promotional design therefore had to be generated dynamically at the store level and be responsive to customer behaviour and store-level product movements.


Computing sku revenue, risk and profitability metrics at each store.

Developing product association analytics to predict the probability of items selling together with a high level of accuracy.

Linking product association and movement, at the store level, with customer purchase data.

Model bespoke offers to customers at POS to encourage a repeat visit within 72 hours, based on their basket mix.

Anualised Revenue Growth


marketing spend reduced


Store inventory reduced


promo uptake rate