South African retailers have access to more customer, sales and operational data than ever before. Yet many important decisions about where to open stores, how to optimise existing locations and how to respond to changing customer behaviour are still being made without a clear understanding of one critical factor: location.
The challenge is that retail has changed significantly. Consumers are more mobile, less loyal to individual stores and increasingly move between physical and digital shopping channels.
The traditional idea that a customer simply shops at the store closest to home no longer reflects how people behave.
A consumer may buy groceries near home, stop at a convenience store on the commute to work, visit a shopping centre over the weekend and use an app for additional purchases during the week.
At the same time, community shopping centres, smaller convenience stores and retail offerings at service stations are changing the competitive landscape.
According to Stuart Martin, geospatial industry lead at Esri South Africa, retailers need to look beyond traditional demographic and catchment-area models if they want to understand these changes.
Stuart Martin, geospatial industry lead at Esri South Africa. Image supplied
“Retailers are not short of data. The challenge is that the information often sits in separate systems and is analysed without enough context. When customer behaviour, store performance, movement patterns and the competitive landscape are viewed together geographically, a very different picture can emerge,” says Martin.
The traditional catchment area is changing
Historically, retailers could assess a potential site by looking at the demographics of the surrounding population, nearby competitors and the approximate drive time around the location.
These remain important considerations, but they no longer tell the full story.
Roads, railway lines and other physical barriers can influence where people shop, while working patterns, traffic flows and lifestyle changes can alter how consumers move through an area.
Digital shopping has added another layer of complexity.
Customers now move between online and physical channels, while factors such as collection points, delivery areas and app-based services are changing the role that physical stores play.
“A catchment area is no longer defined simply by a circle around a store. People shop near home, close to work, on their commute and online, and they may visit several competing locations in the same week. Retailers need to understand these real movement patterns if they want to make better decisions,” says Martin.
This has important implications for new store openings, but also for existing retail networks. A location that performed well several years ago may be operating in a very different environment today as neighbourhood demographics shift, new competitors enter the market and consumer movement patterns change.
Shopping centres face similar pressures. The right tenant mix can no longer be determined only by looking at broad demographic categories.
Generational differences, local preferences and changing expectations around convenience, entertainment, food, fitness, safety and the overall shopping experience all influence why consumers choose one destination over another.
Connecting the data retailers already have
Many retailers are already collecting information that can provide deeper insight into these changes. Loyalty programmes, for example, can reveal what customers buy, how much they spend, when they shop and which branches they visit.
Store performance data can highlight differences between locations, while field teams can capture information about the condition of stores, surrounding developments, parking areas and changes in the local environment.
Property data can also help businesses understand how neighbourhoods are evolving. Changes in property values, the profile of buyers and sellers, and increasing densification can all signal shifts in the future market around a store.
The opportunity lies in bringing these different sources of information together. Viewed separately, a sales report, customer database, property dataset or map may answer only one part of the question.
“The real value comes from combining different layers of information,” says Martin.
“Loyalty data may tell you what customers buy, mobility data can help show how they move, and operational data reveals how a store is performing. Location connects those datasets and helps turn them into decisions.”
Understanding where customers go before and after they visit
Mobility data is adding another dimension to retail analysis by helping businesses better understand movement patterns. Rather than only knowing that a customer visited a particular shopping area, retailers can start examining broader patterns, such as where visitors generally come from, how far they travel and whether they regularly visit competing or complementary destinations.
This can challenge long-held assumptions.
Two stores located the same distance from a customer may not compete equally. One may lie along a regular commuting route, while the other requires a significant detour.
A smaller convenience location may attract frequent visits because of ease of access, while a larger shopping destination may depend more heavily on weekend traffic.
Understanding these patterns can help retailers make more informed decisions about store placement, network planning and the role that different locations should play. It can also support decisions about marketing and customer engagement.
Rather than treating all customers within a particular radius in the same way, retailers can develop a better understanding of how different groups use an area and what may influence their shopping choices.
From hindsight to better forecasting
The next step is using these combined datasets not only to understand what has already happened, but to anticipate what may happen next.
Advances in artificial intelligence and data analytics are making it possible to process larger volumes of geographic and business information and identify patterns that may not be immediately visible through conventional reporting.
For retailers, this could help identify areas with future growth potential, understand how a changing neighbourhood could affect a store or assess whether an existing location may face increasing competitive pressure.
It can also help retailers avoid relying too heavily on historic performance. A store’s past results may not accurately reflect its future prospects if the population, property market, transport network, or competitive environment around it is changing.
The competitive advantage, therefore, may not come from having more data. Most large retailers already have significant amounts available. The advantage will increasingly come from connecting that information and understanding how it relates to the places where customers live, work, travel and shop.
For an industry where the success of a store can be influenced by what happens just a few kilometres away, understanding location is becoming an increasingly important part of understanding the customer.
