Imagine having the power to rapidly predict the future of your planogram (POG) arrangement success in stores. That’s exactly what our cutting-edge prediction model does. Our platform is continuously fed with fresh sales behavior data, ensuring that its depth is ever-growing and it is continuously updated with current behavior trends to provide you with the best recommendations for today. Arrangement AI gives you the ability to fine-tune your recommendations or verify the strength of your current strategy.
Say goodbye to endless meetings and spreadsheet analysis. In less than 10 days, our platform provides you with clear, concise, and data-backed insights into which planogram will yield the highest sales lift.
Harness the power of advanced algorithms combined with a massive data lake of 2-million shopping trips to predict your best planogram sales lift.
For a fraction of the cost of in-store methods, Arrangement AI will arm you with the data needed to win with your Merchant.
Quickly compare multiple POG ideas to identify which are most likely to deliver the highest category and brand sales.
Generate new POG ideas
Rapid arrangement
Assess category drivers
Arrangement AI FAQs
Frequently Asked Questions
Arrangement AI is a cutting-edge prediction model based on InContext’s exclusive data lake of more than 2 million virtual shopping studies.
Arrangement AI is used by shopper insights, category management and brand management professionals who are looking for a way to quickly gain additional insights from their current shopper behavior studies.
All categories available have an R2 score of 0.9 or higher. This means that 90% of variations in the dependent variables are predictable from the independent variable. The average predictive accuracy across all categories, subcategories, or segments is 80%. We are recommending that a minimum predictive accuracy of 70% for a given category, subcategory, or segment be used as a threshold for use. This means that 70% of the time the model accurately predicts data will be significantly higher, lower, or flat when the design is tested with real shoppers.
No, it is not meant to be used as a replacement, but as a supplemental for use when time is a factor. The ability to analyze a category is dependent on having sufficient data from virtual testing. The list of categories available today is not inclusive of every category InContext has ever tested as some categories only have one study or have too much inconsistency in variables to produce an accurate prediction. Furthermore, category assortment, pricing, etc. evolve over time so continuing to test virtually helps provide the predictive model with new information necessary to making accurate predictions.
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