With geographical big data analyses to optimized branch network planning

E-commerce and online sales are increasingly putting pressure on traditional branch office concepts to deliver returns. In addition, the location of branches and the surrounding infrastructure increasingly influence branch sales. Companies must therefore master a balancing act between profitability and customer proximity, between optimization and expansion, between efficiency and presence.
With the help of geographic big data analyses, estimates of changes in the branch network can be made more precisely: What proportion of customers will be attracted by the opening of new branches in the surrounding area? What turnover can be expected at which location? In which surroundings is an additional branch worthwhile?
Added value through the combination of internal and external data
The digitization of map and environment data as well as the availability of many other data sources with geographical reference enable new analysis approaches for branch network planning. In addition to sociodemographic data, much higher-resolution data sets are now available. Movement profiles and frequency analyses show when and where people tend to be and can be crucial when choosing the right location. The combination of external geographic data with in-house digital resources provides new insights for location planning. For example, internal company data contains information on how and when services are used. The geographical environment can explain why certain locations are more or less profitable. The location and opening hours of various shops, restaurants or supermarkets in the surrounding can reveal valuable information and thus better explain the service sales of individual stores. 
Soft factors refine the accuracy
The reason for customer behaviour in a region or district is rooted in the surroundings. This assumption flows into the tactile advantage, a soft factor when choosing a location. It refers to the preferred location of a company in a milieu that is assessed as positive. Based on large quantities of data with a geographical reference and their linkage to internal company performance indicators, many of the location factors previously classified as soft become measurable in the end: the above-mentioned tactile advantage, nearby shopping and leisure facilities, rents, pedestrian frequencies and, in general, the activities that visitors tend to pursue in the surrounding area.
Data-driven branch network planning
This new perspective offers considerable advantages for the evaluation of sites. Sales volumes and customer interest for specific areas of the service portfolio can be evaluated in detail on the basis of internal business data and the performance of branches with additional geodata (e.g. weather, points of interest, demographics, market figures, customer activity, nearby branches, visitor flows or pedestrian frequencies).
Methods from current research are particularly well suited for the analysis: machine learning, artificial intelligence, pattern recognition, neural networks and predictive analytics are all mature for economic use. However, one should not trust these methods without additional individual consideration. The correct selection of data and the interpretation of the results by statistics experts are therefore essential. Finally, the algorithms must be trained specifically for the individual parameters in order to find answers to the questions actually asked.


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