Identifying mobility patterns and classifying consumers within a point of sale or areas of interest helps large retail fast fo measure foot traffic in and out of their stores while understanding the behavioral patterns of their consumers.
The correlation between location and footfall analytics, visits, sales, and the success of retail fast-food franchises 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: KFC Vs. Nando’s, Cape Town, South África
How are visits distributed in each establishment?
Through location analytics, 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 and January 2021:
By analyzing the mobility within both establishments during the established time period, we identified that 56% chose to visit Nando’s, while the remaining 44% preferred KFC, this has a correlation with the location of the stores and the preference of consumers when it comes to choosing a fast-food restaurant specialized on fried and grilled chicken.
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 location intelligence is that it allows gaining detailed knowledge of customers’ behavior patterns by day, hour, month, or year, offering valuable insights to design marketing campaigns and commercial strategies based on the power hours of the restaurants.
This analysis is very useful to know what is the performance of the establishments at peak and off-peak hours of the day.
What is the foot traffic mobility pattern around both fast-food restaurants?
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 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.
These solutions benefit any type of business, an example of this is another case study that was conducted to compare two retail supermarkets also in the city of Cape Town, Green Point South Africa: Woolworths Food and Shoprite. Read the full article here
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.
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, KFC and Nando’s can analyze how their customers behave, since they can look 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 restaurants.
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 restaurants in expansion plans.