Standortanalyse


Retail Site Selection can have a huge impact on business success

The site selection problem is one of the most fundamental problems for growing businesses and new entries. Opening up a new business location can either be a game changer if hitting the right spot, or your new business site could be doomed to fail if the location is not attracting enough customers. Hence, there is a high risk of making wrong investment decisions. There are many factors that influence this essential decision that it becomes one of the most challenging topic for every business.

The Problem of traditional location decisions

Besides geo-based factors like transportation accessibility, real estate prices and availability of qualified workers, also socio-demographic factors play a vital role in decision making. The latter group of factors determine the size and distribution of the potential customer base. With data on population, purchasing power and consumption habits demand forecasts can be generated.

Having so many variables that influence the revenue of a specific location, the traditional approach of evaluating a site by manual survey of the land and building a competition landscape becomes costly, time consuming and ultimately too complex.

Gaining the best return on your investment

Luckily, with the rise of Big Data Analytics there is no burden of incorporating a bunch of different factors at a time anymore. By leveraging available geo-based and socio-demographic data from the internet and integrating business data from existing stores, retail site selection becomes a lot more efficient and less risky.

Simulations can model predicted market shares in different isochrones given existing competitors, substitution products and pricing. Moreover, the cannibalization effects of the new store on existing facilities can be calculated by measuring the effects of overlapping catchment areas.

When incorporating Big Data and publicly available geo-spartial information, risk associated with the investment into a new store location can be minimized and new growth potential can be taped. This is especially valuable for retailers, delivery services and insurances.

What we do

As a leading data science company in retail, we excel on our experience with the largest food retailers in Germany and localization projects in other industries. In our analysis, we combine new technologies like Big Data and Machine Learning with GIS and risk models. Of course also individual preferences and corporate regulations are communicated beforehand and considered in the optimization.

As a result, we deliver a sales forecast for potential sites and visualize the findings in a map with colored isochrones to provide managerial insights for decision makers.

Check out our success stories below or talk to one of our Data Science experts.

The assortment must be individually adapted to the location too

To get the maximum out of your investment, we are also happy to consult you with category management analytics. We help you to set up the ideal assortment given the consumer class in the new store’s catchment area.