Case Study

SERVICE STATION NETWORK PLANNING OPTIMIZATION IN UAE - 1

This brand is a multi-channel power and energy company with a much-loved network of service stations and fuel depots across the UAE. They were looking for an optimization of its retail networks and were expecting to get the best performance for each service station, but also they wanted to know the best locations for new service stations. Locatium offered them its solution based on the omnichannel of the Retail Network and customized the solution to adapt it to the specific needs of our client. Here you can know how we did it.

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CHALLENGE

This client is a leading provider of gas and oil in the UAE. In its new expansion wave, they needed insights before making investments. This big chain of service stations asks Locatium the following questions:

Where to open new service stations in the UAE? What retail/workshop partners should bring to the stations?
What are the catchment areas for the current network? (fuel, convenience, and workshop)
Capacity of each market: how many more stations to open in each emirate?
What is the impact of using mobility data in addition to internal client data?

PROJECT DEVELOPMENT AND THE FINAL SCORE

In the first place, we take into account our huge repository of data and based our machine learning model algorithm on:

Tracking of billions of mobile devices (which we use to do dwell 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.
Then, we match our data sets with the client’s sales data. At the end of the process, we were able to tell them:
How many new service stations they should open in UAE in strategic locations.
What kind of strategic retail or workshops partners they should bring to the service station to attract more visits and consumption.
What were the catchment areas in the current network that they had related to fuel, convenience, and workshop retail.
We also were able to tell them the capacity of each market, so they knew how many more stations to open in each emirate.
Finally, they could see through their eyes the impact of using mobility data in addition to internal client data.
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.