Sample Booth Placement Using Conversion Lift Prediction
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Solution Overview
Problem
Decisions about setting up sample counters for providing free promotional items in stores are typically based on human intuition, which may not be optimal, leading to suboptimal location and timing choices.
Innovation Solution
A trained machine-learning model is used to predict incremental lift on item conversions caused by in-store sample booths, optimizing the placement and timing of sample counters based on user and location data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human intuition is used to decide sample counter placement, then the decision process is simple and quick, but the location and timing choices are suboptimal
Solution Approach 1:
The patent replaces human intuitive decision-making with an automated machine learning model that processes multiple data inputs (user behavior patterns, location characteristics, temporal information) to predict optimal sample counter placement. This substitution transforms subjective human judgment into an objective, data-driven automated system that continuously learns from actual conversion data to improve predictions.
Solution Approach 2:
The machine learning model acts as an intermediary between raw data (user signals, location data, temporal information) and final placement decisions. This intermediary layer processes and synthesizes multiple data sources into predictive scores, enabling complex decision-making without requiring direct human analysis of all input data.
2Productivity
If sample counters are placed at more locations, then promotional coverage increases, but resource consumption increases
Solution Approach 1:
The system changes the parameter of placement optimization by using predictive modeling to identify locations with the highest expected conversion lift. Instead of uniformly distributing sample counters across all locations, the system adjusts placement parameters based on predicted effectiveness, concentrating resources on locations where they will generate the most additional conversions.
Solution Approach 2:
The patent applies local quality by tailoring sample counter placement to specific location characteristics and user behaviors. Each location is evaluated individually based on its unique attributes (foot traffic patterns, user demographics, historical conversion data) to determine optimal placement, rather than applying a uniform strategy across all locations.
Data Source
AI summary
An online system uses a trained model to predict incremental sales caused by a sample counter for in-store free sampling of an item. Upon receiving signals related to in-store purchases of the item, the online system applies the trained model to output, based on the received signals, a ranked list of locations of a source and a ranked list of timeslots for placing the sample counter. The online system selects, from the ranked list of locations and the ranked list of timeslots, a location of the source and a timeslot for placing the sample counter, and generates a decision signal based on the selected location and the selected timeslot. The online system communicates, via the network to a device associated with the source, the decision signal prompting the source to place the sample counter for free sampling of the item at the selected location and during the selected timeslot.