Shopper Traffic Prediction Model for Retail Advertising
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Solution Overview
Problem
The advertising industry lacks an accurate and cost-effective metric to measure the effectiveness of in-store advertisements, making it difficult for marketers and retailers to optimize their advertising budgets and strategies.
Innovation Solution
A method is developed to predict shopper traffic at retail stores using actual shopper traffic data and market data, which involves inputting this data into a mathematical model executable on a computer to forecast traffic and impressions, allowing for the evaluation of in-store advertising effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional TV commercial ratings are used to measure advertising effectiveness, then reach and frequency metrics are available, but the metric becomes inaccurate due to viewers using digital video recorders to fast forward past advertisements
Solution Approach 1:
The patent introduces an intermediary measurement system consisting of sensors, cameras, and data processing equipment that indirectly measures advertising effectiveness by tracking shopper behavior, traffic patterns, and purchase data in-store, rather than directly measuring TV commercial viewership
Solution Approach 2:
The patent replaces the traditional mechanical rating system based on self-reported viewership with an automated electronic measurement system using sensors, cameras, and computer processing to objectively track and measure advertising impact on shopper behavior
2Productivity
If in-store advertising metrics are developed to measure effectiveness, then advertising efficiency can be improved, but the device and system complexity increases
Solution Approach 1:
The patent creates a multi-functional measurement system that simultaneously tracks shopper traffic, monitors product interactions, measures advertising exposure, and analyzes purchase data using a single integrated platform, reducing overall system complexity despite the variety of measurement capabilities
Solution Approach 2:
The measurement system automatically collects, processes, and analyzes data without requiring manual intervention, with sensors and cameras self-configuring and the computer system autonomously generating metrics and reports, reducing operational complexity
3Measurement precision
If comprehensive shopper traffic data collection is implemented, then measurement accuracy improves, but the cost of operation increases
Solution Approach 1:
The patent implements selective data collection that focuses on specific high-impact metrics such as shopper traffic counts, dwell time in advertising zones, and purchase conversion rates, rather than comprehensively tracking every shopper behavior, achieving sufficient accuracy at reduced cost
Solution Approach 2:
The system uses feedback loops where initial measurement data is analyzed to identify the most valuable metrics, then adjusts data collection intensity and scope accordingly, optimizing the balance between measurement precision and operational cost through iterative refinement
Data Source
AI summary
Mathematical models for predicting shopper traffic at a shopper region useful for developing inter alia a metric for measuring impressions to in-store advertising.