“Knowledge is power.” – Francis Bacon

Shopper Simulation

Shopper simulation leverages retailer-specific purchasing behavior data to develop a tailored algorithm that predicts shopper behavior in new store concepts. Through simulation, we can test customer behavior in various scenarios and gain insights into how store layout, product offerings, or product placements can be optimized.

What makes it unique is that the retailer-specific algorithm adapts to the individual store conditions.

USP

Our approach combines AI expertise with retail knowledge and is characterized by an algorithm based on real customer behavioral data.

To predict customer behavior and train the AI in a retailer-specific manner, we utilize real customer behavioral data from our Shoppermetrics database and validation studies.
Our retailer-specific algorithm adapts to the individual conditions of the premises and shopping behavior, offering high flexibility and adaptability to different locations and stores.
It is easy to apply and integrates visual results into the operational decision-making process.
This approach allows for more creative freedom in design and leads to improved store layouts.

Benefits

Individual results, as the algorithm is trained on real customer behavior using Light Gradient Boosting Machine.
Metrics-based assessment of the new store concept in digital twins without the need for renovation, providing more creative freedom in the design process.
Significant time and cost savings since no renovation is required.
High prediction accuracy.
A customer platform to independently conduct simulations.