Personalized Decision Trees from In-Store Behavior
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Solution Overview
Problem
Traditional methods for developing consumer decision trees are limited by their reliance on human interpretation or observation, providing a limited understanding of actual purchase behavior and lacking objectivity and precision, especially when aiming to refine marketing targeting to individual shoppers based on their specific behaviors and preferences.
Innovation Solution
A method and system utilizing multiple sensors, including cameras and mobile device data, to capture and analyze in-store shopper behavior, constructing personalized decision trees that reflect actual purchase decisions and paths, combining behavioral data with transactional and category layout information to create a holistic understanding of shopper preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods (household panel purchase data, ethnography, shop-alongs) are used to develop consumer decision trees, then the implementation is relatively simple and cost-effective, but the measurement precision and objectivity of actual purchase behavior is limited
Solution Approach 1:
The patent replaces traditional mechanical observation methods (human observers, shop-alongs) with automated sensor systems including cameras, weight sensors, and mobile device sensors to capture and analyze shopper behavior objectively, eliminating human interpretation bias and improving measurement precision
Solution Approach 2:
The patent introduces sensor technology as an intermediary between the shopper and the data collection process, using cameras, weight sensors, and mobile device sensors to indirectly capture behavior data without directly observing or interfering with the shopper's natural decision-making process
2Adaptability or versatility
If aggregated behavior data is used for marketing targeting, then the implementation is simpler and covers larger groups, but the adaptability to individual shopper preferences and behaviors is reduced
Solution Approach 1:
The patent segments aggregated behavior data into individual shopper profiles by tracking each shopper's unique decision paths, product interactions, and purchase patterns separately, enabling personalized marketing while maintaining the efficiency of automated data collection
Solution Approach 2:
The patent creates dynamic personalized decision trees that adapt to each shopper's evolving behavior patterns across multiple shopping trips, allowing the system to refine and update individual preferences over time rather than using static aggregated data
3Reliability
If human observers are used to study purchase decision processes, then the ease of operation is maintained, but the objectivity and precision of behavior capture is limited
Solution Approach 1:
The patent enables the system to automatically capture, process, and analyze behavior data without human intervention in the observation process, with sensors autonomously recording shopper movements, product interactions, and purchase decisions, ensuring both objectivity and operational efficiency
Data Source
AI summary
A method and system for determining the hierarchical purchase decision process of a shopper in front of a product category in a retail store. Shopping consideration and the decision path the shopper can be obtained by combining behavior data with the category layout and transaction data. The hierarchical decision process can be determined based on observed actual in-store purchase behavior using a set of video cameras and processor implemented instructions for extracting the sequence and timing of the shopper's decision process. The hierarchical decision process, obtained by the clustering of shopper behavior data over multiple shopping trips, can not only identify the sequence of the decision, but can also quantify the volume of shopping trips at each level in the decision process and the amount of time spent by the shopper for making each decision. The decisions of the shopper for each product group can be captured down to the product attribute level. Such in-depth understanding can provide a picture of the relative importance of each product attribute in the purchase decision process, and can help retailers personalize targeted messaging to the shopper.