Virtually every industry in the world relies on big data to some degree, including the printing industry. For years direct mail houses have been able to verify the success – or otherwise – of print campaigns by measuring how many people have responded to calls to action.
The advent of the QR code and image recognition applications like Blippar and Aurasma have enabled brand owners to generate even greater ROI from campaigns printed on product packs or in the pages of magazines and newspapers, providing a shot in the arm for packaging and magazine printers.
The one part of the printing industry that has struggled to get a slice of the big data action to date, however, is POS. Measuring the effectiveness of POS displays and other signage in order to determine ROI has been a costly and imprecise science.
Until now. At Fespa in June, EFI unveiled a new piece of technology capable of measuring engagement with signage, that it claimed would give early adopters a "licence to print money". The launch coincided with a raft of in-depth consumer engagement research and clever technological wizardry hitting the market.
Key considerations
But what do POS printers who want to sign up for the big data revolution need to know before embarking on this journey?
For an industry that accounts for millions of pounds’ worth of marketing spend each year, until recently the way that engagement with POS and signage was measured was incredibly rudimentary. One option included physically placing someone with a clipboard next to signage to note down the number of consumers who engaged with the sign, along with basic information about age and gender. This unsophisticated approach could set brand owners back around £1,000 a day.
Other more costly options include building a mock-up store in a warehouse to monitor customer interaction, or the creation of virtual reality stores. There are merits to both approaches, but some experts argue that because you’ve taken consumers out of a conventional store environment their behaviour changes.
You can also run in-store consumer surveys asking people whether or not they noticed a particular piece of POS or signage, but as POPAI’s Phil Day points out "what shoppers say they do and what they actually do are almost always massively different".
So to measure engagement effectively you have to do it in a real store environment. But the situation is complicated by the sheer amount of signage that consumers are bombarded with, says Day.
"When you walk around a superstore, you’ve got around 15,000 pieces of POS, so the time period between each new message is less than a second – message, after message, after message. If your brain had to cope with all that it would shut down, so what you do is you filter stuff out and you only get impacted by stuff that really catches your eye. In the 0.9 second it takes to switch from subconscious shopping to conscious shopping mode it’s no longer peripheral information – it’s front and centre. You’re looking at it, reading it, listening to it, tearing it, scratching it, smelling it. You’re doing whatever the thing is that it’s trying to get you to do. That’s engagement."
The only way that you can monitor this level of engagement effectively is through the use of cameras that detect where the eye is looking. This is what fuelled EFI’s development of SmartSign Analytics (SSA), says Mark McGowan, director of products for EFI’s Online Print Solutions.
"We starting looking at solutions that would help our customers generate business," recalls McGowan.
"When we looked at signage we realised that wherever marketers spend their advertising pounds – whether that be on the internet, on TV or on radio – they have access to analytics. Print signage is the only place that really didn’t have that option so eventually at some point this was going to be an issue for marketers who have to justify spending money in this area."
So SSA was born. The system detects people within viewing range of a particular sign thanks to a hidden camera embedded in the sign. Its eye-tracking tool detects which people are actually looking at the sign and determines how long they view it, what gender they are and it also ascribes an age range to each viewer. It then takes this raw data and, using proprietary algorithms, it produces analysis that can be ‘sliced and diced’ in a number of different ways. For example, you can determine whether or not the sign is generating cut-through with a certain demographic in a specific location. The information it gives users is invaluable, argues McGowan.
"The real cost of signage isn’t the £100,000 that a customer spent on getting the signs printed – it’s the sales that the sign failed to generate," says McGowan. "SSA has the potential to provide unprecedented insights on the best strategies and execution to drive retail sales."
To date he says that a lot of interest has been registered in SSA, with the system taken up by a number of printers in the US and the UK (unfortunately they declined to speak to PrintWeek for this article for fear of revealing to competitors that they had this type of capability).
The beauty of this particular product is that although it’s driven by complex algorithms, SSA has been specifically designed to make it easy for printers to deploy.
"One of the big things that has held printers back is that they haven’t been able to generate good data for their customers," says McGowan. "This system generates its own data so it doesn’t require the printer to do anything. You deploy it, it works and you get the analytics."
Other options
The good news for printers who are not prepared – or can’t afford – to splash out on this type of technology, is that there are other ways of gaining an insight into consumer engagement with signage.
POPAI recently undertook a major research project in which it looked at the effectiveness of in-store signage. Working with high-profile high-street retail partners POPAI fitted consumers with glasses equipped with a ‘ClipCam’ to capture 1,718 ‘shopper journeys’. During these journeys shoppers interacted with seven million POS displays and these interactions were then analysed to calculate a performance ratio for each item’s POS, together with its promotional message.
The findings of The grocery display effectiveness study (see boxout) make for fascinating reading, shedding new light on how consumers actually shop. For instance, female shoppers in-store for more than 60 minutes buy more from POS displays than male shoppers. Furthermore, 30-something shoppers respond more to gondola end displays than other age groups, whereas gondola side displays are least effective on shoppers aged 50-plus.
"It’s been the single best received piece of work that we’ve ever done," says POPAI’s Day. "This year I’ve had three or four retailers join POPAI and the biggest reason is they want to get their hands on this kind of data."
While POPAI has received an unparalleled response to its research, not everyone has latched on to the big data opportunity just yet, adds Day.
"Unfortunately, because of the pace of the industry and the paradox of brand versus retailer, I suspect that only the most innovative brands and retailers will take advantage of this and the others will continue to fumble around in the dark."
Going forward, Day hopes to build on this initial study to offer even greater levels of insight. One potentially exciting opportunity arises from coupling eye-tracking technology with other retail analytics products, such as shopper monitoring systems- those developed by Shopper Retail Insight (SRI) for example.
SRI uses the same analytics that are used for assessing the performance of professional footballers to monitor shopper journeys through a store to give brand owners and retailers fresh insight into consumer interaction with signage and fixtures, according to SRI director Charles Offer.
"Existing research techniques tell us what shoppers buy. What we don’t know is how shoppers behave when they are shopping. What they actually do and, just as importantly, what they don’t do."
Crucially, says Offer, SRI’s system operates in the real world and not the sample research world. "Consumers behave differently when they are being researched and traditional in-store methods are constrained by the limitations of how many shoppers it is possible to accompany."
SRI’s system doesn’t have such limitations. The ability to overlay this data with that generated by EFI’s technology – or the work undertaken by POPAI – plus other forms of financial and demographic data, would allow POS and signage printers a tremendous opportunity to stop money migrating away from print into digital channels by offering clients a greater level of insight than ever thought achievable.
As Day explains: "If someone is sitting on an X-million-pound print budget for in-store, the key questions they need to address is what do they put in-store, how much do they put and where do they put it, because it does differ throughout the store. For instance, the impact of a bus stop in the baby aisle is massively different from one on the beers, wines and spirits aisle. That’s the thing about supermarkets and that’s what a lot of retailers are waking up to and understand. It’s not about how many messages you put everywhere. It’s about the right message, hitting the right consumer, at the right time, in the right place."
And that’s exactly where big data and analytics can help.
Big data – in numbers
- 5.15% of men look at floor graphics versus 2.16% of women. Floor graphics and walk-around displays also work best for food/snack purchases
- The most effective POS message is ‘new plus value saving’
- Female shoppers who are in-store for over an hour tend to buy more from POS displays than men
- Although, overall men are more susceptible to in-store promotions (1.04% of men versus 0.95% of women)
- The maximum shopper impact of POS occurs on shopping missions of over an hour for women and 50-60 minutes for men
- Source: POPAI
