Foot traffic Analytics: Burger King Vs. McDonald´s

Location and Footfall Analytics has changed the way retailers implement marketing and commercial strategies in the fast-food restaurant franchise business.

Friday, October 8, 2021

Understanding consumers’ mobility behavioral patterns are critical for all types of restaurants. Big Data tools and spatial data play a very important role in these analyses since they make it possible to measure the foot traffic and mobility patterns of any area or point of interest. By performing these studies, businesses understand and predict the performance of their stores, as well as estimate the competitors’ turnover or identify areas for expansion planning. (site selection)

By applying Big Data mining techniques, it’s possible to measure foot traffic at a specific venue, the combination of this information with spatial data, retailers can obtain valuable insights like dwell time inside their stores or visits count, helping them to measure the performance of their points of sale and other points of interest (POIs).

The correlation between the success, foot traffic, visits, and sales at fast-food restaurants has been studied and proven, so the development of this kind of research has become a priority in the site selection process and expansion modeling.

Case Study: McDonald’s Vs. Burger King, South Beach Miami, Florida, USA

At PREDIK Data-Driven we conducted a detailed study of two fast-food franchises in South Beach Miami: McDonald’s and Burger King, both located near Flamingo Park.

We analyzed the foot traffic in the venues and in the immediate surroundings, with the objective of understanding the behavioral patterns of the people who visited both restaurants. This analysis seeks to answer the following questions:

How are the visits distributed in each venue?

 

Something to keep in mind is that the resolution of this data lets us observe in which specific areas inside the restaurant the movement of people are scattered. This is a very useful insight when conceptualizing the infrastructure design and the internal architectural plans of each restaurant before implementing a site selection strategy.

This heatmap shows the concentration of people inside the restaurants that Burger King and McDonald’s operate around Flamingo Park.

  
Which of the restaurants is the most visited?

Percentage distribution of visits registered between 12/19/2020 and 01/22/2021:  




By analyzing the footfall inside both restaurants using the established period to time, we identified that 68% chose to visit McDonald´s, while the remaining 32% preferred Burger King, which has a correlation with the location of the restaurants and their popularity.

It’s also possible to understand the evolution of footfall over time, which can be very useful to identify behavioral patterns of both actual and potential customers.



Identifying customers’ patterns: Which weekdays are the busiest ones

One of the most useful applications of location analytics is that it provides a better understanding of consumer behavior patterns, and offers valuable insights to design marketing campaigns and commercial strategies.



By performing an in-depth analysis of the data, we determined the percentage of the visit per hour. This analysis is very useful to understand the performance of the restaurants in the busiest hours of the day.



What’s the foot traffic mobility pattern like in the surrounding areas of the restaurants?

Although visits are correlated with the performance of any retail venue, they are not the only factor for success. Another fundamental aspect that should be analyzed is the location environment since it allows to identify the competitors restaurants and understand their performance: estimate the number of visits each one gets, gather insights about the potential customers that move around, and how they behave.

Heatmap showing foot traffic around both restaurants:



The environment analysis provides us with a more general picture of the surrounding areas and the behavior of the people moving around them. This data, combined with other factors, provides in-depth insights when it comes to predicting the revenue of any retail store.

What other insights can be obtained by analyzing footfall at a point of sale?

Understand which customers both restaurants share



By analyzing data over a given period of time at a specific location, such as a restaurant, it’s possible to estimate the percentage amount of consumers that visited both franchises.

These solutions benefit any type of business, an example of this is another case study that was conducted to compare two of the most popular supermarkets in the city of Guadalajara Jalisco, Mexico, the findings were more than interesting. Read more about this case: “Walmart Vs. Soriana: Consumer Foot traffic Analysis"

Customer Analytics

With his analysis, it’s possible to identify in which other places (stores, restaurants, shopping malls, residential areas, among others) the people who were at a point of interest also visited. Thus, Burger King and McDonald´s can know how their customers behave, since they can know where and how long they were before and after visiting their restaurants. This allows them to generate high-value insights to optimize the understanding of current consumers and search for new potential customers with similar behaviors.

Identifying ideal areas in expansion and site selection strategies

With mobility data, it is possible to clearly understand the behavior of the people who pass through a given area, understand what they are like, their tastes, preferences, socioeconomic level, and purchasing potential. Including an in-depth analysis of the commercial establishments in the area in question, becomes a crucial factor in determining the best locations for the opening of new stores.

What is the revenue potential of my competitor?

Through machine learning models, it’s possible to predict the revenue and visits of a competitors´ store. With these models, Burger King could get to estimate the revenue of its competitor McDonald´s in a specific week, month, or year. These models can also be used, for instance, to predict the potential success of an outlet that is about to open. This is ideal to complement feasibility studies for new stores in expansion plans.


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