Branch networks - Restructuring with the help of simulation analyses

Geomarketing analyses help to optimize branch networks economically. For commercial enterprises, the following questions primarily play a central role in branch network restructurings:

• How many branches are necessary?
• Where do these locations need to be situated in order to maximize the demand potential?
• How large does the capacity (square footage) of the locations need to be?
• Where do the customers of the respective locations come from?
• Which locations are cannibalizing themselves?
• Which locations can be closed without losing sales?
• What effects on neighboring branches can be expected from closing a branch? 

Simulation analyses answer such questions.

Example 1: Reorganization of branch networks

Simulation analyses help in reorganizing branch networks optimally, primarily in already highly saturated total markets. Cases show through experience that retail businesses can significantly increase their turnover by relocating branches to other locations.

Example 2: Calculation of cannibalization effects

Simulation analyses are highly suited to answering the important question of cannibalization effects on retail trade. The geomarketing software calculates the intersections of the catchment areas and the households or residents they contain.

Catchment area calculation - Precise target group-based determination of catchment areas

The determination of catchment areas has traditionally been extremely important in the retail sector.

An evolution of catchment area determination is emerging:

• The first stage of evolution is based on simply drawing a certain radius around the respective location.
• The second stage assumes routing, i.e. travel times or walking times, as the decisive criterion. Many retailers still use this method today.
• Geomarketing now has a third stage of development to offer. With this method, catchment areas can be calculated much more realistically. The calculations are based on the idea that there are many determining factors as to why a customer visits a given store. These are the so-called attractiveness factors. These vary depending on the target group and must be defined individually for each location.

Attractiveness factors of a location can be, for example:

• Size
• Design of the sales floor
• Portfolio
• Parking availability or accessibility to public transport near the store
• Points of interest and other "attraction magnets" in the surrounding area, etc.

If you are looking at the individually defined attractiveness factors in a spatial context, the methodology best suited to your needs is gravitational analysis. Gravity models are a modern geomarketing method for determining catchment areas more realistically and with target group precision. These models were developed based on Isaac Newton’s law of gravity.

Put simply, gravity analysis is based on the assumption that the attractiveness of a store is the key factor in determining whether a customer is attracted to the store and decides to visit it. You could also say: The more attractive the store, the further the customer is willing to travel. If retailers rely on catchment areas based solely on distance, they may be missing out on the full customer potential. Attractiveness factors must be included in the catchment area analyses.

In addition to distance, the gravity analysis also considers factors such as purchasing power, household structure, and the company's own customer data, as well as the essential attractiveness factors. This is useful, because it provides a more in-depth picture of a location's performance and sales potential.

Location-based sales forecasts using gravity models

Geomarketing analyses make location-based sales forecasts possible. These can be very precisely represented by means of complex analysis models, such as gravity analysis. For reliable sales forecasts, realistic catchment areas are essential as a basis of calculation. The basis for location-based sales forecasts are data on product range-specific purchasing power or overall purchasing power, resident or household numbers.

Location-based sales forecasts support retail businesses not only in deciding on whether or not a potential location will be profitable over the long term, they also assist in the further development of locations, such as whether sales floors should be expanded or the effect of a change of focus in the product range.

Category Management - Adjusting the product range to the surrounding environment

In order to secure the long-term success of a location, the retail industry is increasingly changing over to adjusting the product range to the peculiarities of the surrounding environment. To do so, the catchment area of the location is analyzed on the basis of sociodemographic figures. The households living in a given area are evaluated according to the following attributes:

• Dominant age group
• Dominant purchasing power
• Households with or without children
• Day and night populations
• Building structure (living spaces or commercial buildings)
• Personal affinities

Such analyses bring not only clarity as to why some articles are shelf warmers, they also provide insights into the products or packaging sizes that have the potential to sell well but which have yet to be introduced to the product range on offer.

Practical methods for retail location optimization

This is how retail companies use geomarketing and WebGIS and GIS-Software in location optimization. Explore our case studies and in-depth insights.