In the realm of shopper insights, decision trees serve as a vital tool for understanding the hierarchy of purchasing decisions made by consumers. These decision trees outline the ranking of various factors such as brand, flavor, size, and price, illuminating the key determinants that drive product choices. This information is used to inform strategy and planograms. Sometimes decision trees are built using market structure and historical purchase data, but we believe better, more insightful, and accurate results are produced from behavior-based decision trees.
Market structure decision trees are constructed by analyzing historical purchase data. These data-driven approaches track the interactions between different product attributes based on consumers’ past buying behaviors. For instance, if a consumer consistently purchases the same brand, flavor, or format within a specific category, market structure analysis identifies these attributes as influential factors. Consequently, market structure decision trees suggest that these attributes should be grouped together on store shelves, assuming they are strong substitutes.
While market structure decision trees do provide valuable insights into historical purchase patterns, they suffer from several limitations, in the context of planogram development and optimization. Their sole focus on past data fails to capture the ever-evolving nature of shopper behavior. As a result, they may not accurately represent current purchasing dynamics, or account for emerging trends. Secondly, market structures rely on the final purchase decision, overlooking the decision process that led to the final purchase. Thirdly, these trees do not consider the context-specific factors that influence purchasing decisions, such as shopping occasions. In certain categories, occasion can be extremely important in determining shopper behavior. Consequently, planograms based solely on market structure might not align with actual consumer preferences and may even impede the shopping experience.
Enter Behavior-Based Decision Trees
Behavior-based decision trees provide a more comprehensive and accurate understanding of consumer decision-making processes. Instead of relying solely on historical data, behavior-based decision trees incorporate real-time behavioral insights gathered through targeted experiments. By presenting shoppers with a virtual or physical shelf and analyzing their purchasing behavior, behavior-based decision trees uncover the intricate nuances of decision-making. This approach captures how shoppers navigate choices, make substitutions, and determine the order and importance of decisions.
Behavior-based decision trees offer several advantages over market structure.
- They reflect the most up-to-date consumer behaviors, accommodating changes in market dynamics, product availability, and inflationary pressures. This real-time perspective enables brands to adapt their strategies swiftly and stay ahead of the competition.
- Behavior-based decision trees account for individual shopper preferences and the order of decisions made during the shopping process. This knowledge allows for intuitive planograms, improving the overall shopping experience and enhancing opportunities for impulse purchases.
- Behavior-based decision trees consider context-specific factors like shopping occasions, ensuring that the assortment and layout cater to the specific needs and preferences of shoppers.
In the realm of shopper insights, behavior-based decision trees are a game-changer. By incorporating real-time behavioral data, these decision trees provide a more accurate and dynamic representation of consumer decision-making processes. They outshine market structure for planogram optimization by capturing the order of decisions, understanding walkaway points, and accounting for contextual factors like shopping occasions. As customer behavior continues to evolve rapidly, behavior-based decision trees empower brands and directors of shopper insights to make data-driven, shopper-centric decisions that resonate with the ever-changing preferences and needs of their target audience.
