Supermarkets can apply location intelligence techniques and footfall analytics to understand consumer mobility patterns, generate efficient site selection strategies, understand the performance of their stores, and estimate competitor turnover.
The correlation between foot traffic visitation, sales, and the success of retail supermarkets 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.
Foot traffic analytics: Mercadona Vs. LIDL, Ciudad Real, Spain
In this case study, we analyze the mobility patterns and foot traffic inside and outside both supermarkets, 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 supermarket?
Through location analytics, the points of interest are identified and a heat map is applied 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 supermarkets is the most visited?
Percentage distribution of visits recorded in September 2021:
By analyzing in-store mobility using the stated time period, we identified that 64% chose to visit Mercadona, while the remaining 36% preferred LIDL, which correlates with store location and consumer preference when it comes to choosing groceries.
These analyses allow you 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 supermarkets.
This analysis is very useful to know what is the performance of the stores at peak and off-peak hours of the day.
What is the foot traffic mobility pattern around both supermarkets?
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 the competition’s potential customers, it’s 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 foot traffic at a retail outlet?
Understand which customers visit both supermarkets
By analyzing data over a given period of time at a specific location, such as a clothing store, it is possible to estimate the percentage distribution of consumers who visited both stores.
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 home improvement stores in the city of Dallas, USA, the findings were more than interesting. Read more about this case: “Competitor Analytics: Home Depot Vs. Lowe’s home improvement“
What are the benefits of foot traffic analytics and location intelligence?
These tools have revolutionized the way retailers implement expansion, commercial and operational strategies.
Through these analytics, businesses gain a detailed picture of their store performance, while predicting or estimating:
- Brand positioning
- Customer behavior
- Market trends
- Competitors revenue
- Expansion models (site selection)
By applying footfall analytics through spatial data mining, it is possible to collect valuable information such as:
- Quantity and classification of people visiting an establishment or area of interest
- Times and moments of the day where there are more visitors
- Dwell time
- Visitation count in-store and out-store
- Market potential of POS and POI’s
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, Mercadona and LIDL can analyze how their customers behave, since they can look where and how long they were before and after visiting the stores.
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, 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, Mercadona could get to estimate the revenue of its competitor LIDL in a specific week, month, or year. These models can also be used, for instance, to predict the potential success of a site selection strategy.
All these insights are generated by applying location intelligence and mobility analysis, if you are interested in knowing more about these insights, we conducted a POI characterization case study in Bangalore, India POI Analytics: Uses and Applications.