Data-driven advertising for a grocery and retail chain
The client, a chain of grocery and retail stores, wanted to improve their advertising ROI through AI/ML modeling. The client wanted to know which products and product combinations should be promoted in various channels in order to optimize additional sales from advertising.
We helped them:
* select which products and product combinations should be promoted in order to increase sales
* understand what products complement each other
* understand how package sizes and other variables affect sales.
Expected additional earnings from promotion were modeled for each product. The products were ranked against each other accordingly.
The outcome is a data-driven approach to advertising and assortment planning. The results were considered valuable from the sales monitoring perspective as they pointed out the commercial relationships between different products. The sales effects associated with, for example, package size and type were also highly useful information to the client.