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The retail market size for artificial intelligence (AI) is expected to soar to USD 85.07 billion by 2032 over this year’s 9.36 billion, exhibiting a compound annual growth rate of a whopping 31.8%, according to Fortune Business Insights. AI technologies in retail are expanding rapidly, transforming and automating both online and offline retail operations, inventory management, product mix, demand forecasting—and customer engagement.
AI is making great strides in helping retailers adapt to changing consumer behaviors and market dynamics and shows no sign of slowing down. How is it working? Generative AI and predictive AI use historic data to predict future behavior, which is helping retailers have a starting place to stay competitive. When strategically paired with consumer feedback (critical to bring in the human element), brands are able to gather a holistic picture of shopper preferences—allowing retailers to plan ahead with a deep understanding of the quickly changing aspects of the marketplace.
Food Dive shares a little more about the two types of AI used in retail experiences. Generative AI is being used in retail for tasks like answering customer queries via chatbots and generating personalized marketing content. It relies on algorithms and large datasets to mimic human interaction but has limitations in predicting specific in-store behaviors. This is where the right shopper research comes in—not only to dig into in-store shopping behavior, but also to be able to react to the current marketplace happenings like impacts of short-term economic ups and downs and swiftly emerging new diet trends, or unexpected and unpredictable impacts like the recent pandemic had on shopping behavior.
Predictive AI offers foresight into future trends and consumer preferences by analyzing data such as past purchases, browsing patterns and social media interactions. This allows businesses to make informed decisions, optimize strategies and enhance the shopping experience. By merging this AI approach with current market research to understand what’s happening right now, companies have a powerful tool for innovation.
So what are some of the ways retailers are using AI, and how can we take advantage of merging the AI benefits with current consumer feedback for a holistic view? Some key areas where AI is transforming retailers include customer personalization and planogram optimization, as well as using in-store analytics to guide pricing and marketing strategies. Let’s take a look at these key areas, how AI is helping, and the role the right shopping research can play in getting it all right for the greatest success.
Retailers are increasingly using AI tools to create personalized shopping experiences by analyzing data from both online and in-store behaviors. Tools like volumetric tracking and spatial analysis can provide insights into customer movements and preferences within stores, optimizing layouts and product placements. Predictive AI enables brands to understand how customers will react to different shelf arrangements and optimize planograms. It can be used in real-time to enhance online shopping experiences through personalized recommendations and it can also predict customer churn, allowing proactive engagement strategies.
To make these AI recommendations more effective, input can be validated and amplified using online or in-store research with your target audience. For example, retailers can test proposed planograms or in-store designs in a virtual setting via an online study before implementing any changes—changes that have the potential to be rather costly so getting them right is essential. Test versus Control research approaches prior to national or large scale rollouts will validate, hone and expand the recommended personalizations coming from AI tools. Adding this element of shopper input can be done many different ways, such as through qualitative shop-alongs or in a more quantitative setting via in-store intercepts, or mobile in-the-moment shopper research.
Pricing research is vital to both existing products and new-to-market products in retail. AI enables dynamic pricing based on past consumer behavior, but it’s critical to always have a forward-thinking lens when determining pricing structures—which goes well beyond an individual product. AI should be used in conjunction with primary research to gain broader, context-specific insights, especially during these volatile economic and political times.
Depending on the end objective, market research pricing techniques such as Gabor Granger or even a straight-forward monadic evaluation can be utilized in conjunction with the data based on past behavior that AI can provide. It is important to also consider amplifying these techniques for a broader, more true-to-life understanding of how various in-store elements such as point of sale, pack design and shelf layout combine with pricing to impact consumer behavior. A choice-based design to examine human behavior (often in the form of a Conjoint analysis) can be an effective tool to land at the right price. At shelf, consumers are forced to make trade-offs, whether they realize they’re doing it or not, and that’s exactly what a choice-based design will do: force actual consumers to make trade-offs to provide concrete validation on proposed pricing models.
By developing synthetic personas of key target consumers, AI can help tailor promotions and offers to specific consumer segments and can definitely be used effectively as a starting point for retail marketing communications. For concrete validation, the combination of segmentation research (to allow retailers to get a true understanding of who their target consumers actually are) paired with messaging research (to more accurately understand how effective various campaigns will perform against the core target) should also be considered. Combining AI tools with traditional “tried-and-true” primary research techniques can improve the effectiveness of marketing strategies and allow retailers to realize a sizable ROI.
There is no doubt that AI represents a significant shift in the way that retailers understand how to best reach consumers, and is transforming the retail industry. The true potential of AI in retail is realized when paired with human insights and strategic market research, which can help ensure decisions are ultimately fueled by consumer empathy and real-time input. Retailers who blend AI with a keen understanding of current market trends and consumer behavior will be more agile in responding to rapid changes and sustaining a competitive advantage.
