AI can help harmonize data across diverse channels, across retailers and across sources including both structured and unstructured data. Just as Google can organize the world’s information into an efficiently searchable connected graph, it is also now possible to organize and triangulate all the information around consumers connecting individual household information, basket level scan data at point-of-sale, social sentiment, buying behavior across channels, day-in-the-life behavior, travel patterns and dwell time in various venues to gain deep understanding of the consumer path to purchase. The harmonized data graph becomes foundational to every analysis in Revenue Growth Management. And, all this is done within minutes versus weeks, at a time when speed to insights provides a competitive advantage. RGM incorporates AI technology and can sift through large amounts of data to add 360 degree consumer insights into the mix. Then, AI-enabled analysis can follow the consumer 24×7 to understand behavior by day of the week, time of day and by shopping occasion.

Predictive AI models are built on the harmonized data. These are very granular, micro-segmented models capable of large-scale analysis with tailored objectives and constraints at every level. These models learn from history and predict likely future outcomes. They can compute baseline and lift forecasts combining a diverse set of influence factors. They use deep learning to discern shopping behavior and complex interactions such as switching between brands within category, switching between channels and switching between shopping occasions. They identify complex cross effect patterns between trade and other actions in the market.

Sense and Respond

AI coupled with cloud, big data and agent-based computing technologies is capable of highly sophisticated sense and respond actions. Today, AI assistants can scan all channels, all markets, competitor actions, retailer actions and own actions to find pockets of opportunities and threats. Then predictive models can evaluate complex interactions and explore millions of possible scenarios to recommend best course of actions to specific people in specific roles owning each market and each account relationship. Modern trade levers can be exercised through personalized digital offers.

AI models continuously discern patterns and learn from experience. Event analysis in RGM takes a forward-looking perspective for learning about consumer behavior. AI models close the loop between plan, execution and results through recommend – act – measure – learn cycles of continuous learning. This inherent composition is extremely valuable in cycles of innovation. Revenue Growth Management teams can conduct carefully designed experiments in select markets and segments, observe the results and then roll out refined strategies across the business.

Have a sensational week!

“Thank you for the opportunity to attend the POI Spring Summit in Chicago, and speak on the panel. I learned a lot, met some great people, and really enjoyed myself!”
Craig Eaton, Director, Revenue Management, Ocean Spray