In modern retail, an omnichannel approach is now absolutely critical for businesses who occupy both brick and mortar and digital storefronts. The way customers shop has developed rapidly alongside the advent of the internet, upending traditions and patterns that had remained virtually identical for centuries beforehand.
How often have you paused before buying something in store to check the reviews online, or to find out if the store you’re in is competitively priced? Equally, how many times have you used an online stock checker to make sure a store has a particular product before you go to buy it in person? We often fall into the trap of considering online retail the be-all-and-end-all, with brick and mortar increasingly a thing of the past; in fact, the future appears to be a mixture of both. Retailers who embrace an omnichannel future now are those that will reap the greatest benefits.
One such example of the nexus between digital and physical retail is footfall analysis. In this post, we’ll examine footfall analytics, how it works, why it matters and some prominent use cases.
What is retail footfall analytics?
Retail footfall analysis is the process of examining where customers are moving within a store or shopping mall and in what volume, then analyzing the results. In the past, a rudimentary form of retail footfall analysis could be conducted via manual data collection, such as with members of staff manually counting the number of customers in a certain part of the store. In modern times, things such as indoor positioning systems and digital geofences have removed this manual aspect of the process while making the data more reliable.
Once the data has been collected, it can be used to look at a variety of different metrics, such as the most popular locations in a store, the least busy, and common journeys users take throughout the store. This information can then be used to calculate everything from the most popular, enticing product ranges to the most successful promotions to areas of the store that are awkward to reach or unintuitively located and need re-locating.
How is retail footfall calculated?
As stated above, retail footfall has been measured by stores for decades. In some cases, stores to this day persist with the manual method, for example having employees use clickers to count the number of visitors over a set period of time or to a certain part of the store. There are, however, several downsides to this method of footfall data collection. Having employees count customers takes them away from their regular roles, and is a costly method of counting. Additionally, it’s prone to human error, and limits the scope of the data collected when compared to a fully fledged digital indoor mapping system.
Some stores may even provide kiosks or other systems to crowdsource the footfall information from the customers themselves, such as setting up two kiosks in different parts of the store that allow customers to press a button to give feedback on their experience, then comparing the usage of the two kiosks. However, while cheaper than the employee option, this data is likely to be even less reliable than that collected by staff, and is once again very limited in its analytical scope.
The arrival of IoT-adjacent technologies has revolutionized the process of collecting and analyzing retail footfall data. Cameras enabled with AI technology are able to record multiple parts of a store and count the number of customers entering and exiting. Infrared technology can project a beam across an entry point and count the number of interruptions in the beam to work out the number of visitors. However, while these methods are more robust and cost effective, they still have some limitations in terms of what insights they can provide.
Collecting data via an indoor positioning or navigation system powered via BLE beacons or BLE-enabled WiFi access points, however, offers far greater possibilities, due to the fact that these systems track a user as they move throughout the entire store, not just whether or not they walk through a certain point. By tracking the user in this manner, it’s possible to calculate more advanced metrics, such as most common routes through a store, possible customer footfall choke points, underutilized areas and dwell times.
Examples of how footfall analytics can improve footfall and revenue
Of course, calculating footfall is only the first step toward harnessing the data and using it to your advantage. There are few better ways to promote and increase retail footfall and, ultimately, revenue, than first understanding your store’s existing footfall patterns to a granular level.
Here’s some examples of the ways in which footfall analysis can help your retail business thrive:
Identify high interest products. As any retailer knows, price point is often king when it comes to buying decisions; a product can sit untouched on the shelves for months and then sell out in minutes after a relatively minor price adjustment. However, this is a very tricky call to make, and hitting that goldilocks point between demand and margin is absolutely critical to the success of any retailer.
In digital retail, information such as visits (and repeat visits) to a specific product page, website searches and price alert sign-ups are a good indicator of demand for a product, regardless of how many units are selling. If demand is high but sales are low, it can point to an uncompetitive price point holding the product back. But how can you glean similar insights for brick and mortar retail?
Well, combining footfall data with sales data for specific products, bins, aisles or product ranges can help unlock these insights. For example, if two aisles are receiving similar footfall and dwell times but one aisle is selling twice as many products as the other, it could suggest a pricing issue that, if rectified, could help deliver an increase in sales and average basket value.
Pinpoint availability and stock issues. Most modern retailers - whether online or brick and mortar - will have a robust stock system in place to alert them when certain products are running low or are entirely out of stock. A dynamic, proactive approach to stock control is another key tennent to successful retail, both in terms of ensuring products can be sold when the demand is there for them, and a positive customer experience (meaning an increased likelihood of repeat business).
Combining knowledge of which areas in a store are running low on stock with footfall data can reveal telling information about which products, in future, should be the highest priority to replenish or restock. It can also hint at which product lines should be expanded with related products or competing brands. Dwell time can show, for example, the difference between a customer seeing a product is out of stock and moving on quickly and a customer seeing a product is out of stock and spending time hunting nearby to find something similar they can pick up instead.
Identify highest footfall areas. It may seem obvious, but using footfall analysis to find the highest footfall areas within a brick and mortar retail environment can help reveal a whole host of valuable insights, including where best to position key POS displays and advertise key offers in order to ensure the most eyeballs on them.
Combining the footfall density information with dwell times and commonly used journey pathways can further help in this case. For example, if a store has a single entrance/ exit, that is likely to be one of the most congested footfall areas in the store. However, this data alone can be misleading, which is why indoor positioning system-powered footfall analytics is so much more powerful than a manual count in, count out system, for example.
In our example, though the entrance/ exit area will have the most concentrated footfall, customers may well be unengaged and unreceptive to displays and offers as they pass through this area - those entering the store will have something in mind that they want to look at first and be purposefully moving toward that area of the store, while those exiting will have already paid and have little to no interest in picking something else up and queuing to check out again.
Combining footfall data with dwell time and commonly used customer pathways can help pinpoint areas in the store and the user journey where customers are highly concentrated, spending a good deal of time, and the most engaged and open to offers.
Identify low footfall areas. It goes without saying that low footfall areas aren’t ideal for brick and mortar stores, suggesting underutilized space or difficult to reach areas.
If a store has noticeable discrepancies between their highest and lowest footfall areas, they could look to rearrange the store to help ensure a more even, less concentrated footfall pattern. Doing so should in theory help encourage customers to spend longer in-store and visit more areas, in turn increasing the average value of their baskets by virtue of them seeing a greater number of products, offers and POS displays.
If an indoor positioning and indoor mapping-powered footfall calculating system was rolled out across dozens or even hundreds of retail locations (as several major US retailers making use of Pointr’s revolutionary MapScale® have done) and a consistent pattern of low footfall, underutilized store space emerged, the retailer in question might consider more spacious in-store design to make better use of the space. It could also hint at a long-term possibility of reducing the average real estate size of each store, saving money on rental and leasing costs without compromising in-store revenue.