Challenge: Footfall customers and their consumption habits are becoming increasingly spontaneous, while brand loyalty decreases. Hence, branch attractiveness and the surrounding vicinity are becoming more and more important for sales performance. Beyond this, e-commerce is putting traditional branch-based industries under pressure. Branches bind substantial amounts of capital, thus making the selection of the best locations for branch networks essential. Branch operators have to manage the trade-off between profitability and customer focus, optimization and expansion as well as efficiency and visibility.
Solution: Geospin offers precise revenue forecasts, allowing supermarkets, banks or other branch operators to evaluate any potential location.
Our advanced machine learning techniques shed new light on branch performance: we can finally make soft location factors and feeling effects tangible and transfer them to unknown areas. Additionally, Geospin allows for dynamic forecasting, enabling our customers to evaluate the effects of adaptions and expansions in their branch network. Thanks to our innovative pattern detection algorithms and a data collection of over 800 external geo-data sources, we can even forecast future revenue for areas without available market data.
The Geo Prediction Engine of Geospin analyzes relevant branches and customer behaviour in detail. This way, it can calculate the potential revenue for existing and future branch locations.
Our integration of over 800 environmental factors allows for a precise modelling of the static and dynamic vicinity of any branch location. Additionally, frequency analyses can show you when and where your customers will be.
Geospin’s machine learning software detects patterns in your data, that are missed by traditional approaches. The final evaluation is based on your performance factors along enriched by our large internal geo-database.
Dr. Christoph Gebele