Site decisions by brick-and-mortar stores and branch networks are critical to success. Targomo now helps these companies improve their location decisions by accurately predicting their sales figures thanks to Geo AI.
More than 80% of the success of individual retail stores depends on their location. Companies can now predict this success and forecast relevant sales metrics such as store revenue or guest count thanks to Geo AI by Targomo. The new solution is integrated with the TargomoLOOP location analytics platform and provides forecasts for every potential location in the sales area.
The Geo AI prediction is based on a bespoke prediction model built by Targomo’s data science team. It combines machine learning and geo algorithms with neighbourhood information and a company’s location data to develop and train a spatial predictive model. The result are reliable sales forecasts for any location and insights about success drivers that let the company understand what makes a location good for them.
Forecast accuracy of up to 80-90 %
“The success driver analysis is at the core of our Geo AI offering,” explains Henning Hollburg, Founder and Managing Director of Targomo. “We find out which environmental and competitive factors are critical to a brand’s success, and to what extend. With our analysis, a brick-and-mortar business finally knows how much of their sales are based on each location factor. This allows for reliable forecasting of sales metrics, where we usually achieve an accuracy of up to 80-90%.” (Read full interview with Henning)
In addition to the sales forecasting and success driver analysis, heatmaps make up the third pillar of the comprehensive Geo AI offering. Heatmaps visualize areas with untapped sales opportunities and let clients discover how much additional revenue their network could create by opening branches in any parts of the country. With these tools, companies can find easily geographic areas with highest potential to grow their business and their brand.