IP-based surveillance cameras are being deployed increasingly to help UK retailers achieve operational efficiencies.
This trend is confirmed in a key finding from a 2012 CCTV in Retail survey of 700 UK retailers conducted by the Centre for Retail Research. The Axis-commissioned study found that 58 percent of UK retailers plan to migrate from analog-based CCTV to a new network video system in order to integrate network cameras with business intelligence (BI) applications. BI application integration was the most significant reason for UK retailers to move to network video, or IP surveillance as it is sometimes called.
So what are these retail-specific BI applications? We were interested to see many of them were geared to capturing and understanding in-store customer behaviour better and flagging up areas for potential improvement.
Nearly all these applications only work well when retailers deploy network cameras capable of integrating seamlessly with existing BI applications through the use of open platform infrastructure and APIs. In addition, the ability to run an increasing range of video analytics software in the camera itself has definitely supported wider demand for a new generation of “intelligent” network cameras.
People counting
So let’s look at some of the BI applications that are being supported by network cameras in retail today. The most obvious one is people counting. People counting used to be carried out by standalone devices, but increasingly the surveillance camera which covers the entrance to a shop is being fitted with people-counting software to capture the image of the visitor as well as log his entry and exit.
The people-counting software produces a stream of metadata, which can be easily shared with stakeholders and cross-referenced with sales receipts for the same period. This sort of cross-referencing is a useful way of determining how efficient a given outlet is. If store visitors are rising, but sales at the tills are static or falling, this may point to a sub-optimal store layout, issues with stock volumes or some other management issue.
Dwell time
Intelligent network cameras can also hold dwell-time analytics software to help analyse which aisles and displays are working best to attract and retain shoppers. Cameras can also analyse dwell-time alongside images of customers actually picking up products and putting them in their baskets. Again it is valuable to cross-reference dwell-time data with customer activity — normally resulting in purchase. If lots of customers are stopping to look at an offer or display and then walking away, this is a fair indication that something in the offer on display does not work for them. Managers can use this intelligence to investigate further and make improvements.
Shopper demographics
It is also possible to use images of customers to analyse visitor demographics, specifically collecting age and gender profiles of customers. Network cameras can go further, through integration with facial recognition software, to determine engagement levels of specific groups with specific digital or point-of-sale displays. This software is increasingly used to establish the number of unique visitors to a store or display. The resulting facts and figures can, of course, give added leverage to buyers when they are cutting deals with suppliers wanting to display their goods in favourable locations.
To support this type of BI, store owners are increasingly deploying network cameras fitted with heat mapping analytics. Heat maps determine areas of most activity and highest footfall in the store. The maps can be used to help direct managers’ discussions about the need for better store design to reduce black spots where few customers venture, and also to stimulate customer flow through the store so they naturally travel next to some of the high-value displays. Heat maps can be cross-referenced with till receipt data to confirm the effectiveness of new store layouts.
Queue analytics
An integrated network video platform can generate real-time alerts when queues exceed pre-defined thresholds. These alerts should then trigger the opening of additional tills and the acceleration of stock replenishment cycles. Some smarter queue analytics software can also integrate with footfall data at store entrances so that additional tills can be opened before that fresh rush of new visitors reaches the checkouts.
Dashboards
The larger the retailer, the more likely it is that all of the data and intelligence that cameras generate will be analysed and cross-referenced centrally. Retail analytics dashboards like RetailNext take data feeds from multiple sources, allowing modern retailers to gain real-time access to key BI metrics. This centralized view is increasingly being distributed to key operational managers via smartphones and tablets.
It’s great to see network video technology finding uses in more and more non-security applications. This is a trend that we believe deserves attention as the video surveillance market continues to mature.
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In a previous role, we gave merchandising teams access, so they could remotely check that displays were working, combining this with footfall monitoring, you can also get stats on whether a display was drawing consumers in or if they were ignoring it. The technology was quite new when I did this, I’m sure the applications are much better now. Once a non security department started to use the CCTV, they often wanted more.
That’s great to know that now not only are the employees of whatever store I am shopping at looking at me but so are their algorithms… Ok joking aside its very interesting to me all the BI applications that are out there for these systems now. And if you think about it most places deploy some type of camera system to monitor the facility and if you can add functionality and gain information from something you were going to have in place anyway then I say kudos to you.
Incredible feedback, and as you say I’m sure it’s got even better by now!
You touch on something though, JonathonL; there will undoubtedly be people who find this to be a bit overbearing, so retailers should be careful when introducing this technology to be clear what it’s for. We happily use a Nectar card or Clubcard to tell our supermarkets what we’re buying in return for vouchers. What does a customer get for this kind of tech?
Ages ago I read an article on RFID tags in clothes that stayed active, you would go into the store and they would know what you buy, what you like and the sales people will be able to direct you to what you like- freaked people out!
@John Kosinadeboh – Thanks for sharing the great article, retiles need to identify customer behavior and CCTV seems to be helping them immensely.
@Dawn Holmes – I’m sure it might be quite interesting, and definitely we have some new applications and some good hardware equipment to support this.
@Jonathan Lipscomb – Yes not only employees and other customer, now CCTV is watching and studying your behavior. Did you ever imagine CCTVs to be that intelligent and complex?
@Robert Brown – CCTVs have come a long way from where it was some time back. VGA cameras and CRTs have been past. Now it’s all about intelligence and business support.
@Robert Brown – Yes retailers need to create public awareness to ensure their customers don’t stay away when they see a CCTV. Customers might not like being monitored closely.
holmesd – I think we should stay away from the customer. They need to have their freedom to do their shopping peacefully. The could be monitored closely to help them when they need someone.
Great discussion here about the social implications of retail analytics.
Did anyone notice that the article highlights these capabilities as being served only by IP cameras?
In fact, people counting (and other) algorithms are most cost-effectively run on multi-channel compute engines that have access to unadulterated HDTV signals, for example in HD DVRs or control-room servers.
Apart from the discourse of people taking it doubtfully, it is thoroughly good for retailers to use CCTV for some useful purposes other than security as well. Dwell time is a very useful thing. If people are stopping but not taking something, it means they were attracted but not enough attracted to purchase. Some small change in display could induce them. Similarly queue analytics could help solve rush issues in the short run.
@ JonathanL, You certainly rang the bell, and rightly so. People are not going to digest it as easily as it seems. I have seen enough roars in comments at another blog about using CCTV for retail purposes. Much of that was regarding the application of facial recognition to recorded images. Commenters there thought it to be too intrusive.
Good to know, thanks Todd.