fast food chain Network Optimization in Latin America
Telepizza / PizzaHut is a well-known fast-food chain in the world. Because of this, the need to have an optimized network becomes evident. Locatium offered you its omnichannel optimization algorithm model based on geospatial data. The increase in revenues 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.
CHALLENGE
Choosing an adequate network for retailers represents a big challenge. Locatium’s solution is based on retail network optimization, enabling Retail and Real Estate organizations to use best-in-class location data to:
- Reduce their risk of site planning failures
- Make more accurate revenue predictions for existing and new sites
- Accelerate their time to insight
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 Telepizza / Pizza Hut chain was one of the brands that used our AI model for its global expansion. Pizza Hut, the owner of the Telepizza brand in Latin America, needed insights before investing in its next wave of expansion.
- Where to open new stores in Mexico, Colombia, Ecuador, and Chile?
- What are the catchment areas (home delivery and dine-in) for the current network?
- Capacity of each market: how many more stores to open in each country?
- What is the impact of using mobility data in addition to open data?
To make this happen, we based our machine learning model 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.
