Artifical intelligence enabled solutions, powered by machine learning, are poised to deliver unprecedented insights and create a new retail paradigm. By Sy Fahimi and Ryan Powell
The rise of e-commerce and the corresponding explosion of digital shopper data redefined retailers’ customer intelligence limitations, revolutionized shopper engagement practices, and changed customer expectations. Over time, as omni-channel retailing became pervasive, shoppers’ online behavior data became the catalyst for sweeping improvements to customer engagement across physical and digital touchpoints.
Yet the limitations of applying digital data in-store traditionally meant that online retail maintained an intelligence and personalization advantage. However, that paradigm is quickly shifting.
Breaking the Rules
Modern store systems are now capable of aggregating massive data sets from nearly every consumer touchpoint – digital or physical – and have emerged as powerful competitive assets. AI-enabled customer intelligence applications, built on machine learning, can evaluate trillions of data combinations – far more than any human or traditional enterprise system ever could – to deliver highly targeted recommendations to retailers, CPG manufacturers and customers alike. By removing human biases and limitations, physical retailers can do as Amazon does: optimize their strategy in real-time, based on complete data, to improve the shopper experience and deepen relationships with their customers.
The greatest benefit of AI-enabled applications is that they can identify trends or anomalies among those trillions of data points to predict future behavior faster and more accurately than any human operator. They’re able to project outcomes and guide marketing decisions based on any combination of potential inputs, such as a targeted discount for a certain product or the introduction of a private label alternative to a low-margin supplier item.
At the consumer level, rule-based algorithms are limited by their understanding and segmentation of historical data. While marketing campaigns using this foundation are today considered ‘personalized,’ they are not comprehensive and often result in plateaued redemption and incremental growth rates.
In contrast, AI-enabled personalized marketing solutions can anticipate consumer needs by analyzing trillions of combinations per household and identifying signals in historical data that predict future behavior. By giving the system the freedom to choose from an unrestricted range of offers that humans may not think to send, the system is able to ‘learn’ the best mix of offers for increasing redemption rates and supporting marketing goals.
No matter how intelligent the targeting, marketers need to ensure their messages are reaching consumers at exactly the right time and place. An AI-enabled system will know a customer is likely running low on pet food, and a new brand with healthier ingredients will deliver higher margins in the long run.
By sending a targeted offer through a mobile app, triggered by a geofence, marketers can engage with shoppers in-store, where they are much more likely to respond favorably than receiving a coupon in the mail or at the point of sale.
As one might imagine, the variability in a consumer’s willingness to redeem an offer is not just about the depth of the discount; it’s about the relationship of the customer to the product or brand in question. But that doesn’t mean the customer has to be brand loyal or have even bought the product before. In fact, they don’t even need to have ever considered a purchase from the category.
In a standard merchandising model, no one could predict when a customer who has never bought a sports drink, or any drink, would be willing to enter the category. But AI-enabled category management solutions assigned the outcome goal of increasing sports-drink sales can identify trends in behaviors for sports-drink consumers in other categories that make it clearer who to target and what it would take to convert them.
Today, physical retailing is all about creating a store environment built on rewarding, data-driven customer engagement strategies that lead to long-term loyalty. No longer are personalized marketing or curated assortments the exclusive domain of digital, nor is it any longer acceptable to create offers that supports business goals only to force feed them to unwilling shoppers.
There is now a fundamental mandate for stores to have a 360-degree understanding of their consumer and to know precisely how to act upon that information. Fortunately, with the arrival of AI, synthesizing and acting upon the insights customers share at every touchpoint becomes easy. And doing so will create a new relevancy and strength for physical stores in a data-driven digital economy.
As Senior Vice President of Product Strategy for Symphony Retail Ai’s customer intelligence division, Sy Fahimi is responsible for product strategy, direction and execution. Ryan Powell is vice president of merchandising and category management within the retail solutions division at Symphony Retail Ai. Powell is responsible for the product management, innovation, R&D and go-to-market strategy for game-changing solutions.