Foot Traffic Analytics: A 2023 Guide

Foot Traffic Analytics: A 2023 Guide

July 27, 2022

updated by Matt Felix Mar 03, 2023

Foot Traffic Analytics


The retail industry is facing challenges on a never before seen scale. As the rise of ecommerce and changes in consumer buying behavior increase the strain on brick and mortar retailers, there is a demand for solutions to help level the playing field. The retail industry though remains resilient, and challenges are being combated in ever more inventive ways. Increasingly, retail foot traffic trends show that businesses are turning to data to ensure a competitive advantage. This is done by leveraging in house data with external sources in order to maximize profits and minimize loss. Perhaps the strongest data signal available to retailers though, is foot traffic analytics.


Foot traffic data, otherwise known as mobility data or footfall data, shows how people interact with Points of Interest (POI’s) in the physical world. Using data collected from mobile devices, in store hardware and WiFi networks, retail businesses can make informed decisions on anything from stock planning to selecting the location of their next franchise. With margins tightening and the demand for efficient working practices higher than ever, foot traffic analysis has become the most reliable signal for the retail industry.


Find out more about the latest in retail trends here


What is Foot Traffic Analytics


So what signals are retailers looking for? Retailers primarily need data based evidence to inform their business practices. By making sense of mobility data by leveraging AI, foot traffic analytic businesses can provide retailers with a complete picture of consumer movement. Information most useful to retailers include:

  • How many people visited a store on a daily, weekly or monthly basis?
  • Where did those people come from before they visited a store? Where did they go after?
  • What is the demographic profile of that visitor?
  • What are the busiest times of day? Are some days busier than others?
  • How does my store or chain of stores compare with the competition? 

These questions are, of course, easily answered in the online world. Website analytic softwares can provide their users with a detailed understanding of the customer journey. They not only answer the questions outlined here, but also provide the framework to provide a tailored shopping experience to these customers based on the complete understanding.


Leveling the Playing Field


For physical world retailers though, insights of this level are historically not as fundamental to the sales process. Data usage has been limited to what was tracked internally, sales, stock levels and the like. For understandable reasons, these sources of data were not made publicly available. As a result businesses had only an understanding of their own levels of success, and were unable to benchmark against the retail ecosystem as a whole.


Foot traffic data goes a long way to provide these types of retailers with this holistic view of consumers, and ultimately give them information on how to increase foot traffic in a retail store. Analytics tools provide users with the same level of valuable insights afforded to their counterparts who serve solely online shoppers, allowing them the data needed to improve efficiency.


Where Does the Data Come From?


Insights of course come from foot traffic retail data, and there is no hard and fast rule as to where the data comes from. The raw foot traffic data comes from a number of sources, ranging from in store sensors and hardware to geolocation hits from mobile devices. Foot traffic analytics providers can focus on one of these data sources, or in some cases combine several, to give their customers more comprehensive insights.


Geohit to Visit


The most common data source is geolocation data from mobile devices. Companies are provided with raw location data, which is then processed extensively to attribute the data to a POI. This process is achieved by leveraging complex data science techniques, converting terabytes of data into clear, concise visualizations which can be used by retailers.


In the case of Olvin, data arrives anonymized, meaning no personal information is associated with an individual visit from a specific device. Then, external data sources are overlaid to the visits to attribute demographic information to the analysis. In addition to demographic information, Olvin looks at events, weather, and a number of other external data sources that affect consumer behavior, to provide customers with the most accurate insights available.

Find out more about Olvin’s Data here


In Store Solutions


On a more micro level, retail customer traffic data can be obtained from hardware and software inside stores. Historically, manual clicker counting was used to track customers entering a store, but just as cash registers and clocking in machines have seen updates with technology, advancements have led to more automated practices in collecting customer data.

Companies such as Sensormatic provide in store sensors which give retailers detailed information on customer levels, helping provide users with insights on peak periods of time. In addition, in store WiFi networks can track customers based on sign-ins to the service. While useful to individual stores or chains, these data sources offer only a partial view of foot traffic, as data can naturally only be obtained from businesses who use these services. However, when used in conjunction with data secured from mobile devices, they offer ground truth for validating the data output of complex AI solutions.


The proliferation of mobile devices ensures a large enough data set required to give the level of accuracy demanded by retail businesses, and is therefore the most popular data source of insights from foot traffic. However, by combining a number of different sources, analytics providers can offer physical world retail businesses a means to stay competitive with their online counterparts.


How Foot Traffic Analytics Can Help Retail Businesses


Accelerated by the COVID-19 pandemic and the lasting changes to consumer buying behavior seen since, online retailers have seen a rise in profits. Olvin’s data shows that retail foot traffic levels are yet to return to pre-pandemic levels, as consumers continue with online shopping habits. Although we predict an increase in customer traffic levels back towards what retailers are used to, businesses need to adapt.



Brick and mortar retailers are searching for new ways to ensure working practices are as efficient as possible, minimizing loss and maximizing profits. Micro adjustments to operations procedures can result in drastic financial changes over the course of a sales year for large retail brands, and foot traffic analytics can be a useful tool to inform these changes. Former COO of Nike, Eric Sprunk, has highlighted the importance of retail foot traffic data in an interview, stressing “We have to anticipate demand. We don’t have six months to do it. We have 30 minutes”. This pressing need for data based solutions has many retailers turning to insights based on retail foot traffic patterns.


Foot Traffic To Plan Your Operations


The first way in which foot traffic data can be used by retailers is in assortment planning. When combined with other retail analytics tools, foot traffic analytics has a huge impact on retail businesses profit margins, especially in the case of nationwide brands. Factors such as seasonality, local events and weather have enormous effects on sales and the foot traffic that drives those sales. If winter coats are sent to locations expecting heavy snow at one end of the country, and swimwear sent to a location in a city that showed an increase in foot traffic from out of town holiday makers, retailers can ensure maximum profits.

Another use of foot traffic data in the retail industry is analysis of store visits when staff planning. With increasingly tight margins, there is greater focus on minimizing overhead costs, and overstaffing a store or chain of stores can have enormous repercussions over the course of a business’s life. Similarly, if a location is understaffed, and potential customers are not getting the expected in store experience, retailers can experience lost revenue, and ultimately customer loyalty. Foot traffic analytics helps to mitigate these concerns, by providing a powerful data signal of visits to stores or trade areas.


Finally, foot traffic analytics can help retail businesses in supply chain management. Data can be used to demonstrate not only how much stock is needed at specific locations, but crucially why. Visits data to stores help retailers understand stock requirements on a macro level, but data on the demographics of visitors to a trade area, as well as knowledge of where they typically visit before and after the POI of interest provide more in depth analysis. When combined with proprietary sales and target consumer information, retailers can ensure the right stock is going to the right place.


The Power of Prediction


Analytics providers can help provide actionable insights for the future using the framework of historical visits. When combined with historical sales data, retailers can get a sense of trends, helping them with an understanding of when peak periods will be based on when they were in the past. This however, is not a full proof method. Variables that affect customer behavior are constantly changing, and using data from last year to dictate business practices for this one will only get you so far. For example, weather forecasts predict the chance of rain based on a number of different key factors, not just on the fact that it rained on this day last year. Seasonality says that it tends to rain at this time of year, but that isn’t the full story.


Just as with weather, foot traffic can be forecasted. Olvin uses sophisticated Machine Learning models to predict foot traffic to a POI. By processing a number of different data sources that constitute the variables that affect consumer trends, we can predict foot traffic levels up to three months in the future. This helps to better informs the decisions retail businesses make.

Find out more about Olvin’s Predictive Powers here


Considerations of Foot Traffic Analytics


Retail analytics derived from foot traffic data is an incredibly powerful tool to help physical world retailers close the distance between themselves and the online world. However, there are of course limitations to the approach. Firstly, they should be viewed as a weapon in the arsenal, not the entire military plan. By leveraging the power of foot traffic analytics as a data signal used in conjunction with other data tools, retail operations teams can furnish themselves with a more comprehensive strategy. Integrated into a data feed, foot traffic analytics can provide context for business decisions, as well as help inform planning practices.


Another crucial factor to consider is the accuracy of the data. The process of attributing data from mobile devices to visits to POI’s involves a number of different data sources in order to provide an accurate output. Mapping human mobility patterns from mobile devices alone is not enough. factors such as business opening times, the length of a visit and an accurate database of businesses are crucial to ensuring analytics maintain the level of accuracy demanded by retail businesses. Even if companies base their analysis on in store hardware and software trackers, which provides much more accurate visit analysis, this accuracy cannot be scaled across a whole trade area. Not all retail businesses share the same in store solutions, if they are present at all, so these providers cannot provide the full overview of foot traffic that retailers expect.


The Importance of Privacy


Finally, and perhaps most importantly, is the media attention on using location data as a tool for commercial enterprise. Some limitations to location data, both public regulations or private mandates, have caused analytics providers to rethink our practices in the past. Whilst public opinion on location intelligence remains a factor of consideration, Olvin can continue to point to a level of transparency that should mitigate perception of our usage of mobile device data. As data is given to us anonymized, Olvin has no record of personal information. Demographic insights are provided by external data sources, and when attributed to geohits from mobile devices, provide a macro level understanding of consumer behavior.

Read more about Olvin’s commitment to data accuracy and privacy here


How Your Business Can Get Started Today


Foot traffic analytics is a powerful tool in helping retailers deal with an ever changing landscape of consumer preference. By leveraging foot traffic data as part of comprehensive business operations planning strategies, retailers can ensure their resources are allocated correctly. Want to find out more about how your business can benefit? Get in touch with Olvin today.

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