Tag Archives: camera analytics

A Year in Store Analytics

It’s been a little more than a year now for me in store analytics and with the time right after Christmas and the chance to see the industry’s latest at NRF 2018, it seems like a good time to reflect on what I’ve learned and where I think things are headed.

Let’s start with the big broad view…

The Current State of Stores

Given the retail apocalypse meme, it’s obvious that 2017 was a very tough year. But the sheer number of store closings masked other statistics – including fairly robust in-store spending growth – that tell a different story. There’s no doubt that stores saddled with a lot of bad real-estate and muddied brands got pounded in 2017. I’ve written before that one of the unique economic aspects of online from a marketplace standpoint is the absence of friction. That lack of friction makes it possible for one player (you know who) to dominate in a way that could never have happened in physical retail. At the same time, digital has greatly reduced overall retail friction. And that reduction means that shoppers are not inclined to shop at bad stores just to achieve geographic convenience. So the unsatisfying end of the store market is getting absolutely crushed – and frankly – nothing is going to save it. Digital has created a world that is very unforgiving to bad experience.

On the other hand, if you can exceed that threshold, it seems pretty clear that there is a legitimate and very significant role for physical stores. And then the key question becomes, can you use analytics to make stores an asset.

So let’s talk about…

The Current State of In-Store Customer Analytics

It’s pretty rough out there. A lot of companies have experimented with in-store shopper measurement using a variety of technologies. Mostly, those efforts haven’t been successful and I think there are two reasons for that. First, this type of store analytics is new and most of the stores trying it don’t have dedicated analytics teams who can use the data. IT led projects are great for getting the infrastructure in the store, but without dedicated analytics the business value isn’t going to materialize. I saw that same pattern for years in web analytics before the digital analytics function was standardized and (nearly always) located on the business side. Second, the products most stores are using just suck. I really do feel for any analyst trying to use the deeply flawed, highly aggregated data that gets produced and presented by most of the “solutions” out there. They don’t give analysts enough access to the data to be able to clean it, and they don’t to a very good job cleaning it themselves. And even when the data is acceptable, the depth of reporting and analytics isn’t.

So when I talk to company’s that have invested in existing non Digital Mortar store analytics solutions, what I mostly hear is a litany of complaints and failure. We tried it, but it was too expensive. We didn’t see the value. It didn’t work very well.

I get it. The bottom line is that for analytics to be useful, the data has to be reasonably accurate, the analytics platform has to provide reasonable access to the data and you must have resources who can use it. Oh – and you have to be willing to make changes and actually use the data.

There’s a lot of maturing to do across all of these dimensions. It’s really just this simple. If you are serious about analytics, you have to invest in it. Dollars and organizational capital. Dollars to put the right technology in place and get the people to run it. Organizational capital to push people into actually using data to drive decisions and aggressively test.

Which brings me to….

What to invest in

Our DM1 platform obviously. But that’s just one part of bigger set of analytics decisions. I wrote pretty deeply before the holidays on the various data collection technologies in play. Based on what I saw at NRF, not that much has changed. I did see some improvement in the camera side of the house. Time of Flight cameras are  interesting and there are at least a couple of camera systems now that are beginning to do the all-important work of shopper stitching across zones. For small footprint stores there are some viable options in the camera world worth considering. I even saw a couple of face recognition systems that might make point-to-point implementations for analytics practical. Those systems are mostly focused on security though – and integration with analytics is going to be work.

I haven’t written much about mobile measurement, but geo-location within mobile apps is – to quote the Lenox mortgage guy – the biggest no-brainer in the history of earth. It’s not a complete sample. It’s not even a good sample. But it’s ridiculously easy to drop code into your mobile app to geo-locate within the store. And we can take that tracking data and run it into DM1 – giving you detailed, powerful analytics on one of the most important shopper segments you have. It costs very little. There’s no store side infrastructure or physical implementation – and the data is accurate, omni-joinable and super powerful. Small segment nirvana.

The overall data collection technology decision isn’t simple or straightforward for anyone. We’ve actually been working with Capgemini to integrate multiple technologies into their Innovation Center so that we can run workshops to help companies get a hands-on feel for each and – I hope – help folks make the right decision for their stores.

People is the biggest thing. People is the most expensive thing. People is the most important thing. It doesn’t matter how much analytic technology you bring to the table – people are the key to making it work. The vast majority of stores just don’t have store-side teams that understand behavioral data. You can try to create that or you can expand the brief of your digital or omni-channel teams and re-christen them behavioral analytics teams. I like option number two. Why not take advantage of the analytics smarts you actually have? The data, as I’ve said many times before, is eerily similar. We’ve been working hard to beef up partnerships and our own professional services to help too. But while you can use consultants to get a serious analytic effort off the ground, over time you need to own it. And that means deciding where it lives in your organization and how it fits in.

Which I know sounds a lot like…

Everything old is new again

I make no bones about the fact that I dived into store measurement because I thought the lessons of digital analytics mostly applied. In the year sense, I’ve found that to be truer than I knew and maybe even truer than I’d like. Many of the challenges I see in store analytics are the ones we spent more than decade in digital analytics gradually solving. Bad data quality and insufficient attention to making it right. IT organizations focused on collection not use. A focus on site/store measurements instead of shopper measurement.

Some of the problems are common to any analytic effort of any sort. An over-willingness to invest in technology not people (yeah – I know – I’m a technology vendor now I shouldn’t be saying this!). A lack of willingness to change operational patterns to be driven by analytics and measurement and a corresponding challenge actually using analytics. Far too many people willing to talk the talk but unable or unwilling to walk the walk necessary to do analytics and to use it. These are hard problems and it’s only select companies that will ever solve them.

Through it all I see no reason to change the core beliefs that drove me to start Digital Mortar. Shopper analytics is critical to doing retail well. In a time of disruption and innovation, it can drive massive competitive advantage if an organization is willing to embrace it seriously. But that’s not easy. It takes organizational commitment, some guts, good tools and real smarts.

Digital Mortar can provide a genuinely good tool. We can help with the smarts. Guts and commitment? That’s up to you!

The State of Store Tracking Technology

The perfect store tracking data collection would be costless, lossless, highly-accurate, would require no effort to deploy, would track every customer journey with high-precision, would differentiate associates and shoppers and provide shopper demographics along with easy opt-out and a minimal creep factor.

We’re not in a perfect world.

In my last post, I summarized in-store data collection systems across the dimensions that I think matter when it comes to choosing a technology: population coverage, positional accuracy, journey tracking, demographics, privacy, associate data collection and separation, ease of implementation and cost. At the top of this post, I summarized how each technology fared by dimension.

In-store tracking technologies rated

As you can see, no technology wins every category, so you have to think about what matters most for your business and measurement needs.

Here’s our thinking about when to use each technology for store tracking:

Camera: Video systems provide accurate tracking for the entire population along with shopper demographics. On the con-side, they are hard to deploy, very expensive, provide sub-standard journey measurement and no opt-out mechanism. From our perspective, camera makes the most sense in very small foot-print stores or integrated into a broader store measurement system where camera is being used exclusively for total counting and demographics.

WiFi: If only WiFi tracking worked better what a wonderful world it would be. It’s nearly costless and there’s almost no effort to deploy. It can differentiate shoppers and Associates and it provides an opt-out mechanism. Unfortunately, it doesn’t provide the accuracy necessary to useful measurement in most retail situations. If you’re an airport or an arena or a resort, you should seriously consider WiFi tracking. But for most stores, the problems are too severe to work around. With store WiFi, you lose tracking on your iPhone shoppers and you get less coverage on all devices. Worse, the location accuracy isn’t good enough to place shoppers in a reasonable store location. It’s easy to fool yourself about this. It’s free. It’s easy. What could go wrong? But keep two things in mind. First, bad data is worse than no data. Making decisions on bad data is a surefire way to screw up. Second, most of the cost of analytics is people not technology. When you give your people bad tools and bad data, they spend most of their time trying to compensate. It just isn’t worth it.

Passive Sniffer (iViu): There’s a lot to like with this system and that’s why they are – by far – our most common go to solution in traditional store settings. iViu devices provide full journey measurement with good enough accuracy. They cover most of the population and what they miss doesn’t feel significantly biased. The devices are inexpensive and easy to install, so full-fleet measurement is possible and PoC’s can be done very inexpensively. They do a great job letting us differentiate and measure Associates and they provide a reasonable opt-out mechanism for shoppers. Even if this technology doesn’t win in most categories, it provides “good-enough” performance in almost every category.

Combining Solutions

This isn’t necessarily an all or nothing proposition. You can integrate these technologies in ways that (sorta) give you the best of both worlds. We often recommend camera-on-entry, for example, even when we’re deploying an iViu solution. Why? Well, camera-on-entry is cheap enough to deploy, it provides demographics, and it provides a pretty accurate total count. We can use that total to understand how much of the population we’re missing with electronic detection and, if the situation warrants it, we can true-up the numbers based on the measured difference.

In addition, we see real value in camera-based display tracking. Without a very fine-grained RFID mesh, electronic systems simply can’t do display interaction tracking. Where that’s critical, camera is the right point solution. In fact, that’s part of what we demoed at the Capgemini Applied Innovation Exchange last week. We used iViu devices for the overall journey measurement and Intel cameras for display interaction measurement.

Similarly, in large public spaces we sometimes recommend a mix of WiFi and iViu or camera. WiFi provides the in-place full journey measurement that would be too expensive to get at any other way. But by deploying camera at choke-points or iViu in places where we need more accurate positional data, we can significantly improve overall collection and measurement without incurring unreasonable costs.

Summing Up

In a very real sense, we have no dog in this hunt. Or perhaps it’s more accurate to say we back every dog in this hunt We don’t make hardware. We don’t make more money on one system than another. We just want the easiest, best path to getting the data we need to drive advanced analytics. Both camera systems and WiFi have the potential to be better store tracking solutions with improvements in accuracy and cost. We follow technology developments closely and we’re always hoping for better, cheaper, faster solutions. And there are times right now when using existing WiFi or deploying cameras is the right way to go. But in most retail situations, we think the iViu solution is the right choice.

And the fact that their data flows seamlessly into DM1 in both batch and – with Version 2 – real-time modes? From your perspective, that should be a big plus.

Open data systems are a huge advantage when it comes to planning out your data collection strategy. And finding the right measurement software to drive your analytics is – when you get right down to it – the decision that really matters.

And the good news? That’s the easiest decision you’ll ever have to make. Because there’s really nothing else out there that’s even remotely competitive to DM1.