Tag Archives: staff optimization

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:

Using In-Store Customer Journey Data: Associate Optimzation

If store layout/merchandising and promotion planning are the core applications for in-store customer journey measurement, staff optimization is their neglected and genius offspring. For most retail stores, labor costs are a huge part of overall operating expenses – typically around 15% of sales. And staff interactions are profoundly determinate of customer satisfaction. In countless analytic efforts around customer satisfaction and churn, the one constant driver of both is the quality of associate interactions. People matter.

The human factor is a huge part of the customer journey. Some in-store measurement solutions treat staff interactions the way digital solutions treat employee visits – as data to be culled out and discarded. The only thing worse is when they leave them in and don’t differentiate between customer and staff!

No part of the customer journey and no part of the store has a bigger impact on the journey, on the sale, and on the brand satisfaction than interactions with your sales associates. And, of course, labor costs are one of the biggest cost drivers at the store. So optimizing staff is critical on every front: revenue optimization, customer satisfaction and cost management. It’s rare that a single point of analysis drives across all three with so much impact, highlighting how important associate optimization really is.

With staff data integrated into customer journey measurement, you know how often associate interactions occurred, you know how long they lasted, and you know how often they resulted in sales. Some stores will already track at least some of this as part of their incentive programs, but customer journey data provides a true measure of opportunity and productivity. Some of these data points are straightforward, but there are interesting aspects to staffing data that go beyond basic conversion effectiveness. It’s possible, for example, to isolate the number and impact of cases where staff interactions should have happened but didn’t. It’s also possible to understand optimal contact strategies, answering questions like ‘how long should a customer be in a section before a contact becomes desirable or imperative? ‘  Even more interesting is the opportunity to bring sports-driven team and player metrics to bear on the problems of staffing. You can understand which associate combinations work best together, how valuable team cohesion is, and the value spread between a top associate and an average hire. This is all invaluable data when it comes time to plan out schedules and staffing levels and, when paired with weather data, can also be used to optimized staffing plans on a highly local basis.

Finally, there are deep opportunities to use this data to optimize broader aspects of staff optimization. By integrating Voice of Employee (VoE) data with associate effectiveness, you can hone in on the golden questions that will help you identify the best possible hires. Creating a measurement-driven, closed loop system to optimize associate hiring decisions isn’t what people generally think of when they evaluate in-store measurement. But it’s a unique and powerful use of the technology to drive competitive advantage.

 

Questions you can Answer

  • Are there days/times when a store is over/under staffed?
  • Are there better options of positioning staff?
  • What’s the best way to optimize staffing teams and placements?
  • How much does training impact staff performance?
  • What questions should I ask when I hire new staff to identify potential stars?
  • How successful is any given associate in converting opportunities?
  • What’s the right amount of dwell-time to allow a customer prior to an associate interaction?

To find out more about retail analytics and in-store customer journey tracking, check out my new company’s site: DigitalMortar.com

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.

 

What is In-Store Customer Journey Data?

Analytics professionals love data and technology. So it’s easy for us (and I use “us” because I completely self-identify in both the category of analytics professional and someone who loves data and technology) to get excited about new data sources and new measurement systems – sometimes without thinking too carefully about what they are for or whether they are really useful. When I first got interested in the technologies to track in-store customer journeys, I’ll admit that its newness was a big part of its appeal. But while newness can get you through a “first date”, it can’t – by its very nature – sustain a relationship. In the last few months, as I’ve worked on designing and building our initial product, I’ve had to put a lot of thought into how in-store measurement technology can be used, what will drive real value, and what’s just “for show”. In my last post, I described using the “PoS Test” (asking whether, for any given business question, in-store customer journey data worked better or differently than PoS data) to help choose the reports and analysis that fit this new technology. But I can see that in that post I put the cart somewhat before the horse, since I didn’t really describe in-store customer journey data and it’s likely applications. I’m going to rectify that now.

To measure the in-store customer journey you track customers as they move through your physical environment. The underlying data is really a set of way-points. Each point defines a moment in time when the customer was at a specific location. This is the core journey measurement data.

By aggregating those points and then mapping them to the actual store layout, you have data about how many people entered your store, where they went, and how long they spent near or around any store section. This mapping to the store is the point where concerns about accuracy crop up. After all, the waypoints themselves don’t have any meaning. It’s only when they are overlaid on top of the store that they become interesting. The more precisely you an place the customer with respect to the store, the more you can do with the data.

By tracking key waypoints along the journey (such as dressing rooms or registers), the basic journey data can be used to help build an in-store conversion funnel. Add Point-of-Sale data (and you’d be crazy not to) and you have the full conversion funnel at a product level and all the experience that went with it. For those coming from a digital world, this may feel like the complete journey. It has everything we measure in the digital world and can support all of the same analytic techniques – from funnel analysis to functional and real-estate optimization to behavioral segmentation. But in physical retail, there’s an additional, critical component: measuring staff interactions. It’s hard to overstate the importance of human interactions in physical retail; so if you want to really map the in-store customer journey you have to add in associate interactions. For any given customer journey, you’ll want to know whether, when, how long and with whom a customer interacted.

For most stores, this combination of journey waypoint data, store mapping, PoS data, and staff interaction data is the whole of customer journey data and it’s powerful. At Digital Mortar, though, we’re trying to build a comprehensive measurement backbone for the store that includes detailed digital experiences in store (mobile, digital signage, and specialized in-store experiences) AND a set of variables that encompass the background environment for a customer visit.

In-store digital experiences are a key part of a modern retail customer journey and if you can’t integrate them into your omni-channel picture of a customer you don’t have key ingredients of the experience. I also happen to believe that custom digital experiences will be a crucial differentiator in the evolution of retail experience.

What about the background environment – what does that mean? There’s a lot more environment in physical retail than there is in digital. Weather, for example, is a critical part of the background environment – impacting store traffic but also dramatically changing in-store journeys and purchase patterns. Other important environment variables include store promotions (local and national), advertising campaigns, mall traffic and promotions, road traffic, events, what digital signage was showing and even what music was playing during a customer visit. The more environment data you have, the better chance you have of understanding individual customer journeys and figuring out what shapes them in meaningful ways.

 

Summing Up

The in-store customer journey data begins with the waypoint data. That’s the core data that describes the actual customer experience in the store. To be useful, that data has to be mapped accurately to the store layout and the merchandise. You have to know what’s THERE! Integrating PoS data provides the key success metrics you need to understand what parts of the experience worked and to build full in-store funnels. Associate interactions data adds the human part of the experience and opens the door to meaningful staffing optimization. And the picture is completed by adding in digital interaction data and as much background data as you can get – particularly key facts about weather and promotions. Taken together, this data provides remarkable insight into the in-store funnel and customer experience. And to prove it, my next post will tackle the actual uses of this data and the business questions it can (and should) answer!