Retail Network Planning Optimization in AUSTRALIA
In this case we work with a brand that offers beauty treatments and products and wanted to optimize its retail network. Locatium offered its omnichannel optimization algorithm model based on geospatial data. The increase in revenue was just one consequence of all the work that went into it. Learn how we adapted our solution to the specific needs of our client.
The location of your store, without a doubt, determines its performance and profitability. This brand had this very clear, so they paid attention to their retail networks. This is how Locatium came out, optimizing their physical stores through the following specific points in their international expansion.
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
This is a brand combines lifestyle and beauty in a personalized way for each person and adapted to individual needs. The main objective of Australia’s leading beauty treatment network needs was to answer 3 core site questions:
- Where to open new clinics in the new market (United Kingdom)?
- What are the catchment areas (work and residence) for the current network in Australia?
- Are the stores too close or too separated from each other in Australia?
- What is the impact of using mobility data in addition to open data in the UK model?
For this happened we 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.
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.