Big Data Analysis for Fast Food Chains (Case Study)
Predictive models and location analytics are two main tools offered by Big Data analysis. Fast food or QSR chains use them to improve their decision-making process in their expansion strategies. These tools are also crucial for a better market understanding and detection of new business opportunities.
We’ll dig into a real case where a client wanted to run a complete analysis for one of their key cities. They wanted to understand their locations’ performance and compare it with their competition.
They also needed to identify new locations to continue their expansion plan throughout the city.
Using internal and external data, we’re able to analyze:
- City areas with the highest consumers’ concentration (Indicating near sales points).
- City areas with the highest vehicular concentration (Indicating near sales points).
- Area coverage.
- Income and demographics analysis by area.
- Locations performance and forecast.
Note: Our client operates a chain of restaurant that offers three different services (“Restaurant,” “Pick and Go,” and “Express Service”).
You may also like to read: How to use location analytics in the fast food business?
Analyzing city areas with the highest consumers’ concentration
Using location analysis, we highlighted the city areas with the highest volume of users. This analysis helped them understand which of their “Pick and Go” locations are within those areas.
They also wanted to compare their physical distribution against the competition.
This analysis also showed them areas of high user concentration with no settled competitors. Our client was able spot three different location options for new openings.
Analyzing city areas with the highest vehicular concentrations
One of the client’s main interests was to identify new locations for their “Express” locations. They were looking for roads with high concentrations of vehicular traffic.
Our client found a street with potential: High vehicular concentration and no presence of competitors.
As part of its expansion strategy, the client aimed to cover the entire city with its different locations. Based on location analysis, they identified all the areas their brand didn’t fully cover.
Green areas are “fully covered” by the brand, yellow areas are “partially covered”, and the red areas are “not covered”. The red dots represent our client’s locations.
Income analysis by area
Unlike “Pick and Go” and “Express” locations, “Restaurants” target only “middle-class” users. Our client needed to consider the income level by area to open new restaurants.
The yellow areas represent median household income. Our client took these areas as reference to decide where to place their new “Restaurants”.
Locations performance and forecast
Through predictive models, we can analyze the performance of each location and generate a sales forecast.
Our client needed to anticipate results and identify underperforming locations. For this case, we generate a sales forecast for each of the three services (The graph shows the location analysis for “Restaurants”).
Powerful insights for intelligent decisions
As you can see, data science is crucial for a fast food chain. Our client was able to make complex decisions faster and easier based on reliable data.
In PREDIK-Data Driven, we have more than 14 years of experience helping top companies with Big Data.