Global car service network Planning Optimization

One of the biggests car service brand was trying to grow faster and strategically. They wanted to know how to optimize their retail networks for its workshops chain. Locatium talks to them about it and make it by proposing our solution based on Omnichannel retail Network Optimization and customizing the solution to adapt it to the specific needs of them. Here you can know how we did it.


This is a global german brand and well known because of its home appliances products. In its new expansion wave, they were looking for to make smart investments in its retail network. This international chain of workshops asks Locatium the following questions:

  • Where to open new workshops in the three countries?
  • What type of workshop concept should open in each location?
  • What are the catchment areas of each of the existing workshops?
  • What is the level of cannibalization?
  • What workshops are overperforming and underperforming?

Tell us the particularities and needs your project needs to be able to implement a plan specially designed for you.

Project development and the final score

The first step was to count with our huge repository of data-based our machine learning model algorithm on:

  • Tracking of billions of mobile devices (which we use to do dwelling analysis, home location identification, visitation analytics, etc.)
  • Tracking of billions of vehicles (which we use to do traffic on the road network, vehicle home location identification, etc.)
    Consumer Data (purchasing power, consumer profiles by category, etc.)
  • Advanced Demographics (gender, income, education, nationality, etc.)
  • Real Estate insights.
  • Credit Card insights.
  • Social Network Insights.
  • Points of interest, including the location of competitors.

After, we match our data sets with our client’s sales data and put them together on our model algorithm. At the end of the process, we were able to tell them:

  • How many new workshops they should open in the three countries they wanted to grow.
  • What was the concept of new workshops they should open in each locatium found by our machine learning model.
  • What were the catchment areas of each existing workshop, and what was the level of cannibalization between them.
  • Finally, what workshops were overperforming and underperforming.

Finally, our client obtained an exact retail predictive model to optimize their business decision-making, saving investment, and knowing exactly where to be oriented to their customers. 

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