Accenture partnered with the retailer to design an AI-powered solution that would enable faster and better data collection and more precise modeling to optimize media spend. The first task was speeding up the existing data flow process, then aggregating and processing all the data from media channels, sales and spend that fed the measurement model. By customizing AIP+, Accenture’s pre-integrated AI services and capabilities, to do the data aggregation, we helped cut the existing process by 80% using automation to accelerate processing and validation.
With data flow addressed, the team looked next to alter the underlying model that produced the measurement. Previously, these models were hypothesis-driven, i.e., people would painstakingly hypothesize every possible interdependency between different channels. New machine learning was introduced to the process, helping to proactively identify those interdependencies between channels that potentially drive sales. With the new monthly cadence, the team could refresh the models every month, iterating from the previous month’s model instead of starting from scratch. By hosting deep-dive training sessions for employees on the modeling methodology, the team offered them transparency that earned buy-in and trust in the solution.