Tag Archives: store operations

An Easy Introduction to In-Store Measurement and Retail Analytics with DM1

My last post made the case that investing in store measurement and location analytics is a good move from a career perspective. The reward? Becoming a leader in a discipline that’s poised to grow dramatically. The risk? Ending up with a skill set that isn’t much in demand. For most people, though, risk/reward is only part of the equation. There are people who will expend the years and the effort to become a lawyer even without liking the law – simply on the basis of its economic return. I’m not a fan of that kind of thinking. To me, it undervalues human time and overvalues the impact of incremental prosperity. So my last and most important argument was simple: in-store measurement and location analytics is fun and interesting.

But there’s not a ton of ways you can figure out if in-store measurement is your cup of tea are there?

So I put together another video using our DM1 platform that’s designed to give folks a quick introduction to basic in-store measurement.

It’s a straightforward, short (3 minute) introduction to basic concepts in store-tracking with DM1 – using just the Store Layout tool.

The video walks through three core tasks for in-store measurement: understanding what customer’s do in-store, evaluating how well the store itself performed, and drilling into at least one aspect of performance drivers with a look at Associate interactions.

The first section walks through a series of basic metrics in store location analytics. Starting with where shoppers went, it shows increasingly sophisticated views that cover what drew shoppers into the store, how much time shoppers spend in different areas, and which parts of the store shoppers engaged with most often:

retail analytics: measuring store efficiency and conversion with DM1

The next section focuses on measures of store efficiency and conversion. It shows how you can track basic conversion metrics, analyze how proximity to the cash-wrap drives impulse conversion, and analyze unsuccessful visits in terms of exit and bounce points.

DM1 Layout Overview Video

Going from what to why is probably the hardest task in behavioral analytics. And in the 3rd section, I do a quick dive into a set of Associate metrics to show how they can help that journey along. Understanding where associates ARE relative to shoppers (this is where the geo-spatial element is critical), when and where Associates create lift, and whether your deployment of Associates is optimized for creating lift can be a powerful part of explaining shopper success.

retail analytics with dm1 - analyzing associate performance, STARs and lift with DM!

The whole video is super-quick (just 3 minutes in total) and unlike most of what I’ve done in the past, it doesn’t require audio. There’s a brief audio introduction (about 15 seconds) but for the rest, the screen annotations should give you a pretty good sense of what’s going on if you prefer to view videos in quiet mode.

I know you’re not going to learn in-store measurement in 3 minutes. And this is just a tiny fraction of the analytic capability in a product like DM1. It’s more of an amuse bouche – a little taste –  to see if you find something enjoyable and interesting.

I’m going to be working through a series of videos intended to serve that purpose (and also provide instructional content for new DM1 users). As part of that, I’m working on a broader overview right now that will show-off more of the tools available. Then I’m going to work on building a library of instructional vids for each part of DM1 – from configuring a store to creating and using metadata (like store events) to a deep-dive into funnel-analytics.

I’d love to hear what you think about this initial effort!

Check it out:

What is in-store customer journey data for?

In my last post, I described what in-store customer data is. But the really important question is this – what do you do with it? Not surprisingly, in-store customer movement data serves quite a range of needs that I’ll categorize broadly as store layout optimization, promotion planning and optimization, staff optimization, digital experience integration, omni-channel experience optimization, and customer experience optimization. I’ll talk about each in more detail, but you can think about it this way. Half of the utility of in-store customer journey measurement is focused on you – your store, your promotions and your staff. When you can measure the in-store customer journey better, you can optimize your marketing and operations more effectively. It’s that simple. The other half of the equation is about the customer. Mapping customer segments, finding gaps in the experience, figuring out how omni-channel journeys work. This kind of data may have immediate tactical implications but it’s real function is strategic. When you understand the customer experience better you can design better stores, better marketing campaigns, and better omni-channel strategies.

I’m going to cover each area in a short post, starting with the most basic and straightforward (store layout) and moving up into the increasingly strategic uses.

 

Store Layout and Merchandising Optimization

While bricks&mortar hasn’t had the kind of measurement and continuous improvement systems that drive digital, it has had a long, arduous and fruitful journey to maturity. Store analysts and manager know a lot. And while in-store customer journey measurement can fill in some pretty important gaps, you can do a lot of good store optimization based on a combination of well-understood best practices, basic door-counting, and PoS information. At a high-level, retailers understand how product placement drives sales, what the value of an end-cap/feature is, and how shelf placement matters. With PoS data, they also understand which products tend to be purchased together. So what’s missing? Quite a bit, actually, and some of it is pretty valuable. With customer journey data you can do true funnel analysis – not just at the store level (PoS/Door Counting) but at a detailed level within the store. You’ll see the opportunity each store area had, what customer segments made up that opportunity, and how well the section of the store is engaging customers and converting on the opportunity. Funnel analysis forever changed the way people optimized websites. It can do the same for the store. When you make a change, you can see how patterns of movement, shopping and segmentation all shift. You can isolate specific segments of customer (first time, regular, committed shopper, browser) and see how their product associations and navigation patterns differ. If this sounds like continuous improvement through testing…well, that’s exactly what it is.

Questions you can Answer

  • How well is each area and section of the store performing?
  • How do different customer segments use the store differently?
  • How effective are displays in engaging customers?
  • How did store layout changes impact opportunity and engagement?
  • Are there underutilized areas of the store?
  • Are store experiences capturing engagement and changing shopping patterns?
  • Are there unusual local patterns of engagement at a particular store?

Next up? Optimizing promotions and in-store marketing campaigns.