Footfall counting for the retail sector

Introduction to retail footfall

What is footfall?

Footfall (also known as a people count or foot traffic) is the term given to the number of people passing through or past a building, location or site within a set period of time. In retail, it specifically refers to the number of potential customers passing through or in front of a store.

How footfall data is used

For retailers, leisure brands and other occupiers, understanding changes in footfall levels is critical in order to assess how a store is performing. This may include things like window displays, product selection, staff and management and the location of a store. However, footfall data is used by a wide variety of organisations such as commercial landlords, local councils and town planners and other businesses invested in the retail economy.

How footfall counting works

Footfall tracking or ‘people counting’ isn’t a new concept. Before the invention of electronic sensors, staff were required to stand in a location with a clicker, manually counting how many people passed by. This system was known to be inaccurate due to high rates of human error and was very resource-heavy. Technology provided the answer, and a raft of electronic sensors were developed. However, clickers are still used in some cases as a low-cost, short-term solution.

The Local Data Company has been at the forefront of people counting innovation. Through the application of technology, a key objective for us has been how we can improve the way footfall data is collected for the retail and leisure market. In 2016, we developed a forward-thinking sensor system in conjunction with UCL that used WiFi signals as a proxy for footfall.

Today, Local Data Company technology has evolved. Our latest sensor, released in 2020, uses harmless radar waves to detect people within a set field of vision. This new technology is highly accurate, stable and is able to count ground-truth numbers, rather than relying on proxies or extrapolation of smaller samples. We believe we have developed the most accurate sensor out there.

Local Data Company radar footfall sensor released in 2020


For more information on the latest Local Data Company footfall technology, click here. 

Using retail footfall insights to make more informed decisions

Footfall is a globally understood metric, relevant across a wide range of sectors, businesses and analysis. It can be used to benchmark and measure the performance of a number of variables, all of which contribute to the success or failure of a retail store, leisure unit, shopping centre or events such as Christmas markets and local campaigns.

Access to localised footfall trends supports decision making across finance, property, marketing and operations departments. It is used for a number of reasons, but above all, footfall data is the real-world context that binds together strategic activities.

Within retail, this metric is the key to quantifying store performance trends, such as sales volumes and revenue. For example, if sales are down one week, is that store underperforming? Does it need to be reviewed? Or are there external factors contributing to changes in sales that should be closely monitored? On top of this, footfall data can be used for both benchmarking and forecasting performance.

For examples of how people counting can help to understand how your store location is impacting success, read our blog on four key trends to measure.

Decision making

Footfall data is used to influence the following strategic decisions:

• Location strategy
• Leasing
• Site selection and acquisition
• Property development feasibility
• Site appraisals
• Rent reviews
• Marketing budget allocation
• Investment and regeneration planning
• Operational strategy (opening hours, stock levels and staffing)
  
  

Measurement

As well as supporting decision making, people counting methods are also commonly used to track and measure a wide range of initiatives.

• Tracking the success of local campaigns such as farmers markets and events
• Tracking the impact of regeneration or building work
• Marketing campaign tracking including advertising and window displays
• A/B store design
• Tracking the impact of competitor/anchor store opening and closures
• Redevelopment of a scheme or asset

Useful Insights from Footfall Trends

• Correlation of footfall to sales and conversion rates
• Using footfall data to understand why peaks and troughs in sales are happening
• Forecasting new site sales based on footfall
• Identifying busy/quiet times of day to inform operational planning
• Analysis of quality of footfall and demographic profile of the local catchment

Addressing the Issue of Falling Sales

One of the most common ways footfall data is used is to understand and forecast changes in sales volumes. When revenue is falling, footfall is often the first metric to look at for clues as to why this is happening.

External events

The first thing to consider is: what else could be impacting sales? Are in-store purchases falling, or the number of people passing by? Foot traffic can be affected by a range of things – some of which are unavoidable. This may include weather, closures of surrounding stores, events or building and development works. All of which can potentially block access to the unit or divert people away. Flipping this on its head, footfall data can also be used to forecast changes in sales caused by some of the external factors mentioned above. 

Understanding the correlation between changes in footfall and sales is valuable and can be utilised to make short, medium and long-term forecasts, which are not always linear. For example, a 5% reduction in passing foot traffic may cause a 10% dip in sales. Interpreting this information correctly, stores can plan and adapt. It’s an opportunity to implement initiatives such as marketing campaigns or new operational models.

Footfall quality

It’s also important to gauge quality over quantity. You may enjoy a high density of passing footfall, but if sales are underwhelming, something obviously isn’t right. For many retailers, a proportion of passing footfall is simply ‘noise’ and not relevant to their store.

Identifying this 'noise' from the pool of potential customers can help to distinguish between low and high-quality footfall. For example, a clothing store may open next door to a busy bus stop. Therefore it is necessary to separate the bustle of the bus stop from customers associated with the clothing store. The ability to capture footfall data within a specific field of vision allows you to make this differentiation.

If footfall flows change course, your operational model should too - a shift in passing opportunity may require a change in operational models. Knowing the busiest point of each day is critical to ensure staffing and stock levels are appropriate. For example, if you are a coffee and breakfast retailer, changes to footfall density from 7am – 11am would have a much bigger impact on the top line than that of a cocktail bar.

This is also an effective way to identify the impact of opening and closing activity of competitors. Let’s say, for instance, a competitor has opened down the road and is running a promotion for its first week. At the same time, your footfall dips and customers temporarily dry up. Being able to identify a correlation enables you to make an informed assumption –  that your customers love a promotion, and this is where they’re headed.

This data can also be used to identify when a retail cluster in a town centre moves, resulting in the prime pitch migrating over time. This can happen for a number of reasons including rising rental costs, surrounding opening and closure activity or significant changes to the pitch – such as an increase in vacancy, which can put new occupiers off opening in that area.

If your unit is no longer located in the best pitch for your brand, this can impact performance. Staying on top of changes to footfall can ensure that you don’t miss out on opportunities to be agile.

Finally, if there have been no changes to footfall but your business is still struggling, then the problem may be a little closer to home. If flows, trends and peaks remain the same, this could suggest the issue is in-store, rather than outside. If you are failing to convert footfall into customers then it’s time to assess the store layout, window display, product selection or staffing.

Determining How Well a Store is Performing

Like anything, it’s important to evaluate performance even when sales are strong. Footfall data can be used to compare conversion rates and sales volumes alongside stores with similar footfall levels. If store A is enjoying double that of store B’s footfall, but sales are only 10% higher, this could indicate a problem at store A. It may also suggest that store B, with lower sales volumes, is actually the better-performing site. Benchmarking is an effective way to help maximise the value of each and every site.

A common metric in retail is conversion – how many consumers are coming into a store vs the percentage making a purchase. Footfall data is often applied to a range of other data layers to provide forensic insight into every element of a store’s performance. Through this, retailers can assess relative strengths and weaknesses, including:

• Footfall
• Sales
• Rent
• Store size

Common Issues with Footfall Measurement

Footfall data can a powerful tool but it is important to be aware of potential pitfalls.

Avoiding Assumptive Measurement Techniques

Collecting accurate footfall data is notoriously complex and many technology companies have tried to combat this by using assumptive measurement techniques. This can include the collection of a small sample of footfall data, then using modelling to extrapolate the findings across a wider cohort. While this can be effective, the accuracy of these methodologies can be inconsistent due to what is known as ‘sampling bias’. This is when the sample is not fully representative and findings are limited. Another commonly used technique is to measure a proxy for footfall, like detecting signals from mobile phones. This is a robust method but is no substitute for collecting solid, ground-truth numbers.

Again, please see information on the new Local Data Company sensors.

Some companies use other assumptive techniques, but again, results vary. One of these is capturing images of feet, and then making ‘informed’ guesses on demographics by the size of feet and footwear. Unsurprisingly, this method is fraught with potential inaccuracies and basic assumptions – one shoe size or style does not fit all.

GDPR Compliance and Protection of Privacy

Ah, everyone’s favourite topic. But a really important one in this field. It is critical not to infringe on the general public’s sense of privacy. Tracking an individual’s activity and capturing personal information including images of identifiable faces always requires explicit consent. Not only is this a key ethical consideration, but thanks to the GDPR legislation launched in 2018, there is now a law against it. Beware of any sensor which collects personal information and always ensure that if consent is required, there is a robust system in place for obtaining it.

Accuracy and Granularity of Data

Historically, the biggest challenge with people counting has been accuracy. When the process first started with manual clickers, the process was rife with inaccuracies due to human error. Error-margins can go as high as 20% - an alarming figure if this data is being used for strategic planning, forecasting and critical decision making.

Valuable footfall analysis is all about detail. Knowing the daily footfall is great, but the real value comes in granular, hourly counts, which enable a superior level of actionable insight.