Restaurant franchise owners need to apply location intelligence and foot traffic analytics techniques to identify consumer behavior patterns, in order to maximize sales and generate more efficient expansion strategies.
Case Study: Comparison of foot traffic around 100 Montaditos in Madrid and Valencia
At PREDIK Data-Driven we conducted a foot traffic analysis around two retail franchises of Cervecería 100 Montaditos, one in the city of Madrid located in C. del Ferrocarril, 10 208045 and another in Valencia located in Carrer del Comte d’Altea, 25, 46005.
In this case study we analyze the footfall and foot traffic traffic in the blocks where both establishments are located making a geofence of 1500 m2 approximately, in order to outline and understand the behavioral patterns of consumers who visit or have registered movement around both franchises. This analysis aims to answer the following questions:
How are visits distributed?
In this type of analysis, geofences are generated and all records within them are measured.
In September 2021, how did the number of visits evolve?
These analyses make it possible to observe the evolution of visits over time, which can be very useful to identify patterns of mobile customer behavior and market trends in peak and off-peak seasons.
Identify consumer behavior: Which days of the week were the most visited?
One of the most interesting applications of geomarketing is that it allows to know in detail by day, hour, month or year the behavior patterns of consumers, offering valuable knowledge to design marketing campaigns and commercial strategies based on the power hours of the franchises.
This analysis is very useful to know what is the performance of the premises at peak and off-peak hours of the day.
What is the foot traffic mobility pattern around both establishments?
Although foot traffic is related to the performance of any retail location, it is not the only key factor for success. Another fundamental aspect to be analyzed is the environment of the outlets, as it allows comparisons and estimation of the number of visits, revenues, strategic and operational movements of the competition.
By gathering information on the competition’s potential customers, it is possible to carry out a more detailed benchmarking and generate strategies to capture the competition’s customers.
This analysis of the environment provides us with a detailed picture of the surrounding areas and the mobility patterns of people moving through the area. This data, combined with other factors, provides deep insight into predicting the revenues of any retail establishment.
What other insights can be gained by analyzing footfall at a point of sale?
Understanding which customers visit both franchises
By analyzing data over a given period of time at a specific location, such as a restaurant chain, it is possible to estimate the percentage distribution of consumers who visited both franchises.
These insights can be shown in this study of two restaurant chains (McDonald’s and Burger King) specialized in the fast food business in a community of Madrid known as Arganda del Rey. Read the full article here
Customer Traffic and Profiling
Another possible analysis is customer profiling, since it is possible to know in which other places (stores, restaurants, shopping malls, residential areas, among others) the people who visited an establishment were. Thus, both brands can know how their customers behave, and know where and how long they were inside and after visiting a franchise.
Identify potential areas for site selection and expansion strategies
With data from the Wealth Index Global, collected by Facebook, it is possible to estimate the socioeconomic levels of the inhabitants, their age and their profile. At the same time, by applying geospatial data analysis techniques, corporate buildings, residential areas, schools, etc. can be located, which makes it possible to clearly understand the behavior of the people who pass through a given area, their tastes, preferences, income level and purchasing potential.
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, these models, could help both franchises to estimate their competitor’s revenue 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 franchise expansion plans.
