Why footfall isn't the end-all. What to do about the retail data gap.

In our experience, we’ve found that most retailers are familiar with the idea of monitoring in-store footfall, conversion rates, and some of the proceeding business optimizations (if you’re not, here’s a short introduction on the subject). However, often we find people are unaware of the limitations of these metrics, and don’t know how monitoring the number of people that pass a given store, would be better suited to helping them achieve their goals.

The basics: in-store footfall & in-store conversion rate

In the quest for store optimization, simply looking at sales data almost never gives the full picture of a store's performance. This is because sales data alone doesn’t contain information on the store's opportunity to sell over any given period, which is largely determined by the number of people who actually enter the store, the footfall. No people… no sales.

For example, imagine you have two identical stores in different locations and, on Saturday last week, they reported the same takings. Looking at this figure alone you could be inclined to believe that the stores were performing similarly. However, if I then tell you that one of the stores had five times the footfall it could be sensible to think the store with less footfall was performing better: for each person who walked in, more sales were made. It has a better conversion rate.

Of course, there may also be other factors at play affecting conversion that should be considered and accounted for. A location may for instance have visitors more likely to just browse, naturally lowering conversion. That being said, monitoring footfall and conversion remains key in understanding store performance across portfolios big or small, and retailers rely on these metrics to make incredibly important operational decisions. Check out the following article if you're interested in learning more about how footfall is used to improve store performance.

What Next? Passing traffic footfall & street-to-store conversion rate

We often speak to clients aiming to achieve goals that aren’t best reached by looking at footfall and conversion rates alone. The main case we encounter is when the client is looking to drive higher footfall. For example, they’re in the position that once people enter a store, they are converting at a satisfactory rate, but lower in-store footfall is limiting the number of sales that can be made.

A retailer may take any number of actions to drive more footfall to a store, such as running local marketing initiatives, adjusting window displays, or adjusting their strategy for choosing future locations. So to measure the outcome of their efforts they’d look to monitor footfall… right? In actual fact, similar to our previous example, looking at footfall alone doesn’t contain any information about the opportunity that the store had to bring people in the first place. Which is primarily determined by the number of people who pass by your store. Again, no passers-by, no people in-store.

Say for example you ran a local marketing campaign to promote brand awareness for one of your low-footfall stores, while assessing the success of your campaign you see that in-store footfall didn’t increase during or after the campaign. You’d be likely to believe based on this information alone that the campaign was unsuccessful, but if street footfall was down in the area, due to say a prolonged period of bad weather, you might think differently. Drawing incorrect conclusions from this data is almost always costly, causing retailers to mistakenly disregard successful campaigns and unnecessarily invest extra capital. This is where monitoring street footfall in some capacity comes in.

We say the ratio of the number of people passing a store to the number of people that enter forms the street-to-store conversion rate (or capture rate). When we inspect the street-to-store conversion rate before and after the campaign and we see an increase, that is for every person who walks past our store a higher proportion enter. We can now more accurately evaluate the success of our efforts, taking into account a range of external factors that culminate in lower footfall on the street.

This metric provides invaluable insight into how well your store converts passers-by into store visitors. Similarly to the in-store conversion rate, this metric, and its constituent parts, can be analyzed to provide a range of business optimizations and, as you add multiple stores into the equation, the visibility and utility it offers are unmatched.

How does it work and what does it really mean?

In terms of the collection of passing traffic data, some landlords will provide aggregate footfall data for a given area but again, this isn’t truly representative as you don't know how many people are passing by your specific store, the opportunity, and so can be grossly inaccurate. Instead, we recommend a collection method that captures the number of people passing by your specific store. Often we see solutions making use of wi-fi based systems which are known for their issues with accuracy and reliability. We explain this in more detail in the following article: The Main Challenges for Retailers in Fully Leveraging Footfall Data

We’ve found the most accurate and reliable method of counting the number of people passing a store to be a camera-based system powered by artificial intelligence, taking a similar approach to the in-store footfall counters we all know and love. Doing this, we’re able to audit each camera after installation ensuring its reliability and that it achieves a minimum of 95% accuracy.

For us though, the most exciting part of all this is that by combining the sales data, in-store footfall, and passing traffic, (as well as other important metrics) we’re able to create an easily digestible report that, at a glance, allows you to track and compare all of the most important metrics for your store portfolio.

Ultimately providing our clients with a unique, digital-style sales funnel spanning their physical stores.