“Gain Theory offers a more integrated approach to econometrics moving from pure marketing optimisation to helping form a business strategy.” 


Gain Theory was commissioned by a major retailer to evaluate and optimize its personalized marketing efforts.


Gain Theory built models leveraging data by store, merchandise category and customer segment, allowing the retailer to optimize high value customer conversion for each category. Nested models were used to evaluate all business drivers, with deep dives into personalization efforts (e.g., e-mail, shopper, coupons and loyalty cards (i.e. how many points worked best by category?). Given the depth and complexity of data, machine learning was used to train models and improve validity. The validated algorithm was embedded into the retailer’s activation platform to optimize, in real-time, the best combination of activities and offers, and to maximize high value customer acquisition and return for each campaign.


The program delivered a 15% net sales gain through improved personalization (+$3.4B in sales / +0.9B in margin).


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