Foot Traffic analytics have revolutionized the way retailers implement expansion, commercial and operational strategies in self-service stores.
The correlation between location and foot traffic analytics, visits, sales, and the success of retail self-service restaurants have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion strategies.
Case Study: Bodega Aurrera vs. Soriana Express, Mexico City
What is the foot traffic mobility pattern around both self-service stores?
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 self-service stores, as it allows for comparisons and estimates of the number of visits, revenues, strategic and operational movements of the competition.
By gathering information on potential customers, it’s possible to carry out a more detailed benchmarking and generate strategies to capture the competitor’s clients.
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.
How are visits distributed in each establishment?
Through location intelligence, we identify the points of interest and apply a heat map to visualize the in-store mobility patterns of the clients, which allows us to visualize the customer journey, the dispersion of consumers, and the distribution of visits within both establishments.
This provides very useful information when conceptualizing the design of the infrastructure and internal architectural plans that make up each establishment so that leaders can implement strategies that improve the customer journey and implement more efficient expansion models while maximizing the shopping experience of consumers.
Which of the stores is the most visited?
Percentage distribution of visits registered between December 2020 January 2021:
When analyzing the in-store mobility at both establishments during the established time period, we identified that 51% chose to visit Soriana Express, while the remaining 49% preferred Bodega Aurrera, this has a correlation with the location of the establishments and the preference of consumers when it comes to choosing this self-service stores.
These analyses allow businesses to observe the evolution of visits over time, which can be very useful to identify patterns of foot traffic customer behavior and market trends in high and low traffic seasons.
Identify consumer behavior: Which days of the week are 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 stores.
This analysis is very useful to know what is the performance of the establishments at peak and off-peak hours of the day.
What other insights can be obtained by analyzing footfall at a point of sale?
Understand which customers both self-service stores share
By analyzing data over a given period of time at a specific location, such as a self-service store, it’s possible to estimate the percentage amount of consumers that visited both franchises.
With this analysis, it’s possible to infer in which other places (stores, restaurants, shopping malls, residential areas, among others) the people who were at a point of interest also visited. Thus, Bodega Aurrera and Soriana Express can analyze how their customers behave, since they can observe where and how long they were before and after visiting the establishments. 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’s possible to clearly understand the behavior of the people who pass through a given area, how they’re alike, their tastes, preferences, relative wealth index, 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 self-service 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´ restaurant, 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 self-service stores in expansion plans.