Location analytics in pharmacies: CLICKS Vs. Dis-Chem

Pharmacy franchise owners need to apply location and foot traffic analytics techniques to identify consumer behavior patterns, thereby maximizing sales and generating more efficient expansion strategies.

Case Study: Location analytics around CLICKS and Dis-Chem in Cape Town, South Africa.

At PREDIK Data-Driven we conducted a study of the mobility around two retail pharmacy franchises in the city of Cape Town, South Africa: CLICKS  and Dis-Chem, both share a similar size and share infrastructure with other medical centers, such as: Intercare and Medicross, so it is also possible to know a little more about the consumers and mobility patterns that visit these establishments.

In this case study we analyze the mobility and pedestrian traffic around both establishments by making a polygon of approximately 800 m2, in order to outline and understand the behavioral patterns of consumers who visit or have registered movement outside and inside the drawn geofence of both franchises. This analysis aims to answer the following questions:

What is the foot traffic mobility pattern around the two establishments?

Although visits are related to the performance of any retail location, they are not the only key factor for success. Another fundamental aspect to be analyzed is the environment of the pharmacies, as it allows for comparisons and estimates of the number of visits, revenues, strategic and operational movements between establishments.

By gathering information on the potential customers of the competition, it is possible to carry out a more detailed benchmarking and generate strategies to capture the customers of the competition.

This environmental analysis provides us with a more general picture of the surrounding areas and the mobility patterns of people moving around. This data, combined with other factors, provides deep insight into predicting the revenues of any retail establishment.

How are the visits distributed?

In this type of analysis, geofences are established with which the records found within them are measured.

In which of the geofences the highest mobility was identified during January 2021?

When analyzing the foot traffic within the geofences during the established period of time, we identified that 51% chose to visit Dis-Chem, while the remaining 49% preferred CLICKS, this has a correlation with the location of the establishments and the preference of consumers when it comes to choosing a pharmacy, or in this case a health center.

How was the evolution of visits in both pharmacies?

With this, it is also possible to observe the evolution of visits over time, which can be very useful to identify mobile customer behavior patterns and market trends in high and low seasons.

Identify consumer behavior: Which days of the week were the most visited?

One of the most interesting applications of location analytics is that it allows us 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 establishments.

Which were the most visited hours during the period of time analyzed?

This analysis is very useful to understand how the stores perform during the busiest times of the day.

Understanding which customers visit both pharmacies

By analyzing data over a given period of time at a specific location, such as a pharmacy, it is possible to estimate the percentage distribution of consumers who visited both establishments.

These solutions benefit any type of business, an example of this is another case study that was conducted to compare two shopping malls in the city of Cape Town, South Africa: Vangate Mall, which contains distinguished brands such as  Tekkie TownCell CStudio 88Rocomamas, Nando’sJimmy’s Killer Prawns, among others, and Maynard Mall, another shopping mall made up of renowned brands like  ShopriteClicks PharmacyRageJumboFixItTotal Sports.

Customer analytics

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 pharmacy.

Identify the most suitable areas to establish new points of sale

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 turnover potential of my competitor or of a point of sale?

Through machine learning models, it is possible to predict the turnover of a given point of sale. With these models, any business could get to know what is the potential turnover or visits of its competitor in a specific week, month, or year. These models can also be used to predict the potential of a new point of sale to be opened. This is ideal to complement feasibility studies of new points of sale with expansion plans.

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