While e-retailers attach strategic importance to analysing their website traffic data, shops seem much less inclined to do so for reasons that often have little to do with it, such as lack of time, lack of tools and a phygitalisation that is lagging a little behind! A 2018 Business Harvard Review analysis* reports that only 5% of retailers analyse their data to drive their business!
What e-retailer today would do without analyzing traffic by page, by item, by origin, and not link it to the conversion rate?
What seems obvious in e-commerce is not necessarily so on a website. And yet, analyzing a shop's footfall data can be just as useful for the site's commercial performance as it is for the customer's in-store experience.
So let's take a look at the main indicators that can be gathered by measuring footfall in a shop, and the impact that analyzing this data can have on management, performance and customer satisfaction!
Analyse customer footfall data to optimise retail sales performance
Before we can even talk about analysing footfall data, we obviously need to obtain this data using intelligent counting sensors. There are many such devices on the market: infrared counters, AI counters, 3D counting cameras, camera video analysis, etc.
It's important to choose your people counting device carefully, because it will provide you with reliable, accurate data that you can manipulate at your leisure to make the best possible decisions for your blind.
Analyse the conversion rate or exits without purchase in shops
One of the most common KPIs, for example, would be to measure the conversion rate in your shop. How many people went in, how many left without making a purchase, how many went through the checkout and how many people per receipt!
This is very useful information for assessing how many tills need to be opened depending on the day and time of the week (or other parameters), when to send out resources for cleaning, tidying up, etc., or quite simply for adjusting opening times and sales staff schedules.
And above all, data that can be compared with other shops so that you can act quickly if a site has a conversion rate that is really below the average for other sites.
We could even analyse the conversion rate as a function of the staff present to identify training needs or merchandising actions.
Analyse the average time spent in the shop
Analysing footfall data can also provide a very good overview of the average time spent in the shop (this information can be cross-referenced with the conversion rate and/or visitor profile). You'll know very quickly if you need to change anything about the welcome in your shop: customer traffic, circulation, reception, etc. An e-tailer always knows at what point in the customer journey the visitor has left the shop; why not analyse this in the shops too? This will enable you to take the necessary steps to retain customers and encourage them to buy!
Compare sales performance by zone
Depending on the sensor devices chosen, it will also be possible to analyse performance by zone and compare it with other shops. This can even be done using your video surveillance cameras!
The idea is to analyse which areas are the most frequented, at what time of day, and to cross-reference this information with the profile of visitors, the footfall rate, the average ticket on items in this area, etc.
You'll also have qualitative indicators that can alert you to the impact of a merchandising change or the introduction of a new shelf concept.
All in all, a wealth of information to help you manage your business with precision!
Analyse the results of marketing campaigns
Finally, analysing footfall in front of the shop window, in terms of the number of people entering, converting or leaving without making a purchase, is an additional indicator to help you improve your shop's performance. You can measure the commercial impact of a marketing campaign (do more people pass in front of my window?), the attractiveness of the window and, why not? renegotiate rents, particularly in shopping centres that commit to an average footfall for their spaces!
This is all data collected on footfall which, when analysed, will enable you to achieve a certain level of performance, or at any rate a "data driven" approach to management that is informed rather than "gut feeling".
All this information can be used for communication purposes to enhance the in-store customer experience!
Analyse footfall data to improve the in-store customer experience
Communicating waiting times at checkouts
Good, clear and controlled communication is often a source of customer satisfaction. The best example would be waiting at the checkout. With a few sensors in queues, it's easy to tell customers how long they've been waiting at the checkout, in real time.
For shops that have a single queue, basket abandonment on arrival at the checkout becomes very anecdotal, and customers are reassured. The same goes for waiting in the fitting room. No more abandoned items on the shelves close to the fitting rooms, which represent a potential loss of turnover and additional storage costs...
This information can also be analysed to provide customers with a comparison between waiting times at traditional checkouts and those at automatic checkouts.
In short, by communicating your waiting times, you will be able to reduce your queues while increasing the comfort, experience and satisfaction of your customers!
Communicating the regulatory gauge
Analysing attendance data also means being able to control your regulatory gauge. Whether you're in the middle of a pandemic, as we've seen, or in the middle of a sales period, or on the day a highly-anticipated product is launched, it will be very easy to communicate the number of people in the shop and stop people entering if the gauge is reached. The end of the pandemic does not have to mean the end of customer flow counting in your shop. Real-time analysis of footfall data in retail remains an asset for managers, and occupancy remains an important indicator for the customer experience.
Communicating shop occupancy forecasts
Finally, this "Retail Analytics" approach can also be used for forecasting! As well as being able to organise visitor reception (advisers, cleaning, checkouts, etc.) much more effectively according to the expected number of visitors, why not communicate this attendance forecast in the same way as is done in museums, in transport, on the road, etc.? This should make it possible to spread the flow over the week and still guarantee a high level of comfort and welcome for visitors!
And remember, if you're not yet ready to do this ... your customers are!
Finally, by simply looking at shop footfall, you can come up with around ten KPIs that are really useful for taking rapid action on sales performance and customer satisfaction. Of course, these KPIs are all the more relevant when they are cross-referenced with other data from checkouts, reception, access control or security. What's more, this data needs to be made easily accessible to shop managers thanks to simple APIs and pre-established reports that can be consulted in just two clicks! Studying conversion rates has a direct impact on sales, and counting systems pay for themselves very quickly.
Affluences can advise you on the best strategy to implement!
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