16 Nov Behavior Prediction in the Retail Industry
In the retail industry, product is paramount, but customer experience runs closely behind. In recent years, retail executives have broadened the definition of customer experience beyond the traditional touchpoints. Retail leaders now focus on the many physical and sensory experiences customers have when they interact with a retailer’s brand, store, and online and social media presence.
Because of these many touchpoints and the volume of customer information generated by digital transformation and the IoT, retail is especially ripe for machine learning applications. Through recent innovations, machine learning is helping retailers turn customer data—shared with the user’s permission—into insight that drives personalized experiences with their brand. In one scenario, customers who order a meal using a retailer’s app have their food handed to them at the exact moment they walk into the store.
How is that level of precision possible? It is, in part, due to machine learning’s predictive capabilities combined with canny location intelligence. By analyzing millions of data points based on customer behavior and location data tracked through the app, ML algorithms can make accurate predictions about when that customer will arrive—without the privacy-infringing practice of individual location tracking.
“AI is a data-driven game, hands down,” explains Esri’s Sud Menon. But “predictions will be accurate only if the training data used to teach the AI prediction model is truly representative of the target cases being classified or predicted. “If I had to put it in one term,” Menon adds, “AI is basically about decision-making—smarter decision-making.”
Forward-thinking retailers are finding ways to tap into the data they need to be able to predict—with high levels of accuracy—what customers are going to demand; when they want it; by what channel; and most importantly, where they want it available. By combining location intelligence and artificial intelligence, companies can bridge the traditional gap between supply chain forecasting and actual consumer demand.
Today’s merchandise planning spans the entire complex network in dynamic iterations that reflect real-time trends. Predictive demand sensing backed by location intelligence and AI gives companies the edge to compete and build customer trust. Companies apply this innovative approach to deliver higher customer satisfaction, gain a competitive advantage, and achieve higher brand value.
The customer experience can be enhanced through machine learning in other ways. Cities are already putting GIS and ML to work directing drivers to the nearest open parking spaces in crowded urban areas. Large retailers can provide in-app deals as a customer moves through a store, targeting incentives to their precise location and behavior/purchasing history.
“By combining location intelligence and artificial intelligence, companies can bridge the traditional gap between supply chain forecasting and actual consumer demand.”
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