In our digitally driven world, understanding shopper behavior is essential for brands to effectively merchandise and thrive at the shelf. Luckily, shopper research has evolved significantly over the years, markedly propelled by the advent of predictive analytics and AI. In the retail space, artificial intelligence (AI) is expected to have a financial impact on retail of over $9 trillion by 2029. While it’s leveraged in many different facets of retail, the power of predictive AI is already starting to revolutionize how products get arranged on store shelves.
What is the difference between generative AI and predictive AI?
Generative AI, such as ChatGPT, is a sort of search engine technology that leverages algorithms and large datasets to create content, interpret human language, and can generate outcomes autonomously based on learned patterns and information. In the retail sector, using generative AI is a frequently employed technique to quickly answer customer questions with online chatbots, or to generate personalized marketing content. This method relies on complex algorithms that process vast information to mimic human interaction.
Predictive AI offers businesses a glimpse into the future, enabling them to anticipate outcomes with greater accuracy. By analyzing vast amounts of data, including past purchase history, browsing patterns, demographic information, and social media interactions, predictive analytics can forecast future trends and consumer preferences. This invaluable foresight empowers businesses to make informed decisions, optimize marketing strategies, and enhance the overall shopping experience.
Why AI for shopper research?
While generative AI has proven its worth in some aspects of retail planning, it exhibits notable limitations when forecasting customer behavior in store settings. The primary challenge lies in its inability to furnish highly precise and context-specific insights. Generative AI-powered models often lack the depth of data necessary to comprehend the intricate nuances of customer behaviors within specific retail environments.
As a result, many forward-thinking retailers are now exploring alternative approaches, like predictive AI. With the right machine learning models, brands and retailers can leverage AI’s ability to quickly learn how customers will react and behave to various planogram iterations. It means teams can test more concepts in less time, and be more iterative with learning plans.
How do I effectively apply AI at the shelf?
Effectively is the key word here. Outcomes from leveraging predictive analytics are only as good as the data that are fed in. For example, traditional shopper research methods often use data from past shopping trips to infer how planograms should be set. Inferences based on this previous shopping data would ideally be based on conditions that existed when those sales occurred. But, the context variables about adjacent products, placement on the shelves, number of facings, and other things, like size of brand block, aren’t captured. These data sources don’t have enough information in them to fully understand how the arrangement and assortment drive shoppers behavior.
Therefore, the data fed into machine learning models for shopper research has to be vast, accurate, and constantly updating and growing with new data.
At InContext, our data lake of over 2 million virtual shopping exercises has allowed us to create Arrangement AI, a tool to generate fast, impactful insights into arrangements at the shelf. InContext leverages digital twins of the retail environment, enabling teams to test various concepts in a risk-free space, iterate, and capture the shelf context to help understand how shoppers would behave in any given scenario with very high accuracy to what happens in real stores. This in turn provides our machine learning models to draw from a huge library stocked with real-world outcomes.
Predictive analytics represents a paradigm shift in the field of shopper research, offering businesses unprecedented insights into consumer behavior. By leveraging advanced data analytics techniques, brands can anticipate shopper preferences, optimize marketing strategies, and drive sustainable growth. However, success in predictive analytics requires a strategic approach and a commitment to data integrity. Before you jump headfirst into the choppy waters of AI, think about what, why, and how artificial intelligence can drive a competitive advantage for your brand in the marketplace.
