Location analysis

Dynamic tool for continuous supply chain optimization

A new reality

The retail scene is changing. Consumers shop differently nowadays, and one factor of this is the integration of online and offline shopping. Only stores at the best locations and with the right product ranges will allow you survive, provided that you anticipate your service area's needs properly.

Current retail questions

  • How can I find the best locations for my stores?
  • How can I adjust the product range or the formula of a store to the area and the customers?
  • How do my stores perform, taking into account their environment?
  • How do I make my stores more profitable?

Our solutions and results

Veneficus collects a huge amount of information on stores' surrounding areas. For example, travel times, demographics, competition, opening times, passers-by, traffic, et cetera. By linking these external data sources to internal sales data, the following analyses are possible:

Potential revenue of existing stores

The potential revenue is calculated for each individual store. This combined with the actual revenue results in a particular efficiency score for each store. Using this information, you can then make the right decisions and take the right actions.

Potential revenue of new locations

We calculate the sales potential for each possible new location. This analysis is ideal for picking the perfect spot for a new store.

Product range optimization per store

Not every store location is the same. The product range needs be optimized depending on the audience. This results in happier customers, less inventory, less square footage needed, and more profit.

Compare stores

Our solution sorts locations that are similar into groups. This makes them easily comparable, which helps you make the right decisions in terms of policy, campaigns, product range, and pricing.

How does it work?

Statistical algorithms establish the relationships between many external variables (including environmental variables)
and the turnover sales per store per product category. By understanding these relationships, the model is then able to estimate
the sales potential of existing and new locations.

The model determines the potential for each product category in each store. The difference between these figures and the actual turnover indicates where commercial opportunities await!

Results and examples

The final result is the ability to manage your stores based on data and facts. This results in higher sales and lower costs. Below are two examples of dashboards.

Example: insight into your entire chain

  • locations with a high potential
  • locations with an average potential
  • locations with a low potential

Example: the potential of a new location

If you enter addresses, the model will calculate the potential of these new locations. We can also make a list of ideal coverage in terms of stores for certain areas: the Netherlands, the Benelux region, Europe, et cetera.

Are you curious about what we could do for you?

Contact us