Segmented Initiative Analysis for Retail Location Performance
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Solution Overview
Problem
Retailers face challenges in determining which business locations would benefit from a tested initiative, as current methods only assess the average impact and lack the ability to identify specific demographic or environmental factors, leading to either widespread implementation or rejection of initiatives.
Innovation Solution
A system and method for segmented initiative analysis that collects performance data from test and control sites, identifies attributes strongly related to performance, and predicts the success of initiatives in other locations based on these attributes, allowing for targeted implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional software products (spreadsheet or statistical software) are used to analyze initiative data, then data collection and basic analysis can be performed, but the ability to segment results by location attributes and identify specific demographic or environmental factors is lost
Solution Approach 1:
The patent segments the business network into test sites and control sites, and further segments test sites by various attributes (location type, size, demographics, competition) to analyze initiative impact at different segments rather than as a whole. This allows identification of which location types benefit most from initiatives.
Solution Approach 2:
The system assigns different weights to various location attributes based on their relevance to initiative success. Different locations are evaluated with customized attribute sets and weighting schemes, allowing tailored assessment of initiative potential for each location type rather than uniform evaluation.
2Productivity
If only average impact of tested initiative is assessed, then overall performance change can be measured, but the ability to understand how impact varied among different types of stores and which non-tested stores would benefit is lost
Solution Approach 1:
The system divides the business network into test sites and control sites, and further segments test sites by attributes such as location type, store size, demographics, and competition. This segmentation enables analysis of initiative impact across different location types to identify which segments benefit most.
Solution Approach 2:
The system changes the parameters of analysis by introducing multiple location attributes (location type, size, demographics, competition) and their interactions. Instead of single average metrics, the system evaluates multiple parameters simultaneously to capture performance variation across different location types.
3Ease of operation
If a global decision is made to roll out initiative to all stores or eliminate it altogether, then implementation simplicity is maintained, but the ability to focus investments on locations most likely to succeed is lost
Solution Approach 1:
The system evaluates each location type with customized attribute sets and weighting schemes to determine initiative potential. Instead of uniform evaluation, each location receives an assessment tailored to its specific characteristics, enabling targeted implementation decisions.
Solution Approach 2:
The system changes from binary go/no-go decisions to a multi-parameter evaluation framework that considers location type, size, demographics, competition, and their interactions. This produces a nuanced prioritization list that guides selective rollout to maximize impact while minimizing resource waste.
Data Source
AI summary
A system, method, and article of manufacture is disclosed for analyzing a business initiative for a business network including business locations separated into control group sites and test sites that have implemented the business initiative for a predetermined test period. Each of the sites have an associated set of attributes reflecting various characteristics corresponding to the respective site, such as geographical location, size of business location, number of employees, etc. In one aspect of the invention, a process is performed that collects a performance value for each of the test and control group sites reflecting a level of performance of each respective sites during the test period. The performance of the test sites is then measured relative to the performance of the control sites over the same time period. The process may segment the performance values for each test site attribute to identify those attributes that have a greater impact on the performance values of the test sites than other attributes. Further, the process configures a model for predicting the performance values of the test sites using the identified attributes and determines whether the model accurately predicts these performance values. If so, the process applies the model to the non-tested sites to predict the performance values of these sites. Based on the predicted performance values, a user may select one or more of the sites to implement the business initiative.