Location and foot traffic analytics are transforming the way retail expansion strategies and competitive advantages are defined in shopping centers.
Case Study: Blue Route Mall Vs. Table Bay Mall
At PREDIK Data-Driven we conducted a comparative study of two shopping malls in the city of Cape Town, South Africa: Blue Route Mall , which contains distinguished brands such as: Burger King, Absolute Pets, Dis-Chem, Foschini, House and Home among others, and Table Bay Mall, another shopping mall made up of renowned brands like Incredible Connection, Debonairs Pizza, Homelife, Brights, Cape Union Mart.
In this case study, we analyze the mobility and pedestrian foot traffic inside and outside both shopping malls, in order to understand the behavioral patterns of consumers visiting both franchises. This analysis aims to answer the following questions:
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 shopping malls.
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 shopping malls is the most visited?
Percentage distribution of visits recorded between December 2020 and January 2021:
By analyzing in-store mobility using the stated time period, we identified that 51% chose to visit Table Bay Mall, while the remaining 49% preferred Blue Route Mall, this has a correlation with the location of the stores and the preference of consumers when it comes to choosing a shopping mall.
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 shopping malls.
This analysis is very useful to know what is the performance of the shopping malls at peak and off-peak hours of the day.
What is the foot traffic mobility pattern around both shopping malls?
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 fast-food franchises specializing in fried chicken in Cape Town, one of the wealthiest suburbs in South Africa: KFC and Nando’s. Read the full article here
What other insights can be gained by analyzing footfall at shopping malls?
Understand which customers visited both stores
By analyzing data over a given period of time at a specific location, it is possible to estimate the percentage distribution of consumers who visited both stores.
Customer Analytics
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, Blue Route Mall and Table Bay Mall can analyze how their customers behave, since they can look where and how long they were before and after visiting the stores. 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 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, 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 stores expansion plans.
