When I describe Digital Mortar to folks, the most common reaction I get is: “Can you really do this?”
Depending on their level of experience in the field, that question has one of two meetings. If they haven’t used existing in-store customer tracking solutions, the question generally means: is the technology practical and is it actually OK to use it (i.e. does it violate privacy policies)? If they have experience with existing in-store customer tracking solutions what they mean is: “does your stuff actually work as opposed to the garbage I’ve been using?”
I’m going to tackle the first question today (is the technology practical and legal) and leave the second for next time.
Is the Technology Practical?
Yes. As my post last week made clear, the various technologies for in-store customer tracking have challenges. Data quality is a real problem. There are issues with positional accuracy, visitorization, journey tracking, and even basic reliability. This is still cutting or even bleeding-edge technology. It’s like digital analytics circa 2005 not digital analytics 2017. But the technologies work. They can be deployed at scale and for a reasonable cost. The data they provide needs careful cleaning and processing. But so does almost any data set. If chosen appropriately and implemented well, the technologies provide data that is immediately valuable and can drive true continuous improvement in stores.
How Hard is it to Deploy In-Store Tracking?
Unfortunately, the in-store customer tracking technologies that don’t take at least some physical in-store installation (Wi-Fi Access Point based measurement and piggybacking off of existing security cameras) are also the least useful. Wi-Fi measurement is practical for arenas, airports, malls and other very large spaces with good Wi-Fi opt-in rates. For stores, it just doesn’t work well enough to support serious measurement. Security cameras can give you inaccurate, zone based counts and not much else. Good in-store measurement will require you install either measurement focused cameras or passive sniffers. Of the two, sniffers are lot easier. You need a lot less of them. The placement is easier. The power and cabling requirements are lower. And they are quite a bit cheaper.
Either way, you should expect that it will take a few weeks to plan out the deployment for a new store layout. This will also involve coordination with your installation partner. Typically, the installation is done over one or two evenings. No special closing is required. With sniffers, the impact on the store environment is minimal. The devices are about the size of a deck of playing cards, can be painted to match the environment and any necessary wiring is usually hidden.
After a couple week shake down, you’ll have useable measurement and a plan you can roll out to other stores. Subsequent stores with the same or similar layout can be done as quickly as your installation partner will schedule them. And the post-install shake-down period is less.
So if you’re planning a Pilot project, here’s the timeline we use at Digital Mortar:
- Select Store Targets: We typically recommend 3 stores in a Pilot – one test and two control stores with similar layout and market.
- Select Initial Store
- Design Implementation for the Initial Store
- Train Installation Partner
- Do initial 1 store installation
- Test the initial installation and tune plan if necessary
- Rollout to additional stores
- Provide initial reporting
- Targeted analysis to develop store testing plan
- Run initial test(s)
- Analyze control vs. test
- Assess findings and make optimization recommendations
- Evaluate pilot program
This kind of Pilot timeline gets you live, production data early in Month 2 with initial store findings not long after. And it gets you real experience with the type of analysis, testing and continuous improvement cycle that make for effective business use.
Is it Ok to Use Location Analytics?
And, of course, in the EU the tracking guidelines are significantly more restrictive.
Bottom line? In-store customer tracking and location analytics is ready for prime-time. The technologies work. They can be deployed reasonably and provide genuinely useful data. Deployment is non-trivial but is far from back-breaking. And the proper uses of the data are understood and widely accepted.
In my next post, I’ll take up the analytic problems that have crippled existing solutions and explain how we’ve solved them.