Tag Archives: retail analytics

Getting the Digital In-Store Experience Right

In creating Digital Mortar, we’ve made a huge leap from measuring and optimizing digital experiences to trying to do the same for store experiences. But we haven’t left digital entirely behind. It’s a huge part of Omni-Channel (of course), and you can hear more about that in this distilled down video. But it’s also, increasingly, a part of the store experience. And when digital experiences have made the leap from the web to the store, measurement has generally been left behind. That’s unacceptable.

Optimizing In-Store Digital Experiences

Digital is pervasive in our lives now and the in-store experience is increasingly digital. Integrated mobile apps, geo-fenced couponing, endless aisle digital supplementation, digital signage and integrated digital/product experiences have all gotten real interest and investment as retailers try to figure out the best ways to bring digital experience into the store. A lot – and I mean a LOT – of these experiences have failed. That doesn’t mean they’re bad ideas and it doesn’t mean they can’t succeed. I’ll say it again. Doing new stuff is hard.

But part of the reason the failure rate with these digital experiences is high is methodological. A friend of mine running retail analytics for his company describe it this way: “We spend a ton of money building something. Then we roll out these experiences, nobody measures them, nobody improves them, nobody figures out how they change operations or how operations needs to change. They just sit there. And after a while, they’re just gathering dust.”

I see plenty of in-store digital experiences that are gathering dust. Lumped in deserted areas of the store or with blue-screens of death. And I see plenty of digital signage that might as well be paper signage given how often it’s changed and customized.

If you’re rolling out a digital experience, you need to make sure that good digital measurement is incorporated. That might be a traditional measurement system like Adobe, but there are situations where tools like AppDynamics might be a better bet. Either way, it’s essential to measure the intra-tool experience.

If you’re investing in an in-store digital experience without baking in digital measurement, shame on you. And that measurement shouldn’t be the equivalent of so much App measurement (how often it’s opened). For hands-on digital experiences in the store, measuring success demands thoughtful and quite detailed data collection and analysis.

But what about the experience around the tool? How many customers might have engaged with it but didn’t. Of the customers who did, did it change the way they interacted with the store? Was it a draw or was it an impulse experience? Did using the experience increase total time in the store or did it cannibalize traditional browsing? Did users interact more or less with Staff? And was that a good thing?

If you can’t answer these types of questions when you deploy ANY type of digital experience in store, you can’t optimize the experience and you can’t really tell whether it’s working. That’s a prescription for failure.

Of course not all these types of digital experience use similar measurement techniques, Digital signage, for example, is a little bit different than hands-on digital experiences. Since digital signage has no interactions, traditional measurement techniques are worthless. You can’t put Adobe tags in digital signage (well, you can, it just isn’t useful in the traditional way). But you can still integrate the signage data into broader in-store customer journey tracking – in fact that’s really the only way to create a meaningful measurement context around the signage data and the only way to build a program of continuous improvement. Digital signage is sadly underutilized, under-localized, and under-optimized. All because it can’t really be measured.

Going digital in the store is part of the future retail has to embrace. Getting it right? That’s a job for analytics and in-store customer journey tracking.

Questions you can Answer

  • Are experiences used by a significant percentage of customers?
  • Are there some shopper types more or less likely to engage with a digital experience?
  • Are digital experiences additive to store engagement?
  • Do digital experiences increase or decrease staff interaction with customers?
  • Do digital experiences increase consideration time in a section?
  • Do digital experiences increase or decrease subsequent product consideration?
  • Are digital experiences a draw or an impulse?

The Strategic Uses of In-Store Customer Journey Measurement

Store layout, promotion and staff optimization are the immediate and obvious ways to use the core data from customer journey analytics. Together, they comprise the “you” part of the equation – optimizing your operational and marketing strategies. But the uses of in-store tracking don’t end there. There’s tremendous strategic value in being to understand customer journeys – a lesson we’ve learned over and over again in digital. When it comes to omni-channel, store and experience design, and the integration of new technologies to the store, you simply can’t do the job right without in-store journey measurement.

I cover the fundamentals of why the in-store journey matters and how to build in-store customer journey data in this new post on Digital Mortar.

 

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

The Uses of In-store Customer Journey Data – Store Marketing

I’m working my way through the broad uses of in-store customer journey optimization. I started with Store Layout and Merchandising optimization – which is really the foundational analytic capability that this type of data provides. Today, I’ll tackle a use that’s nearly as fundamental – optimizing in-store promotions. For those of you from the digital world, you can think of these two applications as parallel to site optimization and digital marketing optimization.

Promotion Planning

In-store promotion planning is one of those constant grinds in the life of retail analysis. You never stop planning promotions and you never get good enough. With PoS data, it’s pretty easy to measure the single most important aspect of a promotion – how much it sold. It can be a lot harder, however, to answer questions about why something worked or, as is often more salient, why something didn’t. In-store measurement can fill in the gaps around performance measurement AND help develop new promotion and display strategies.

With in-store journey measurement, you can track how and whether a promotion shifted behavior. Did a promotion steer visitors to a section? Did it keep them there longer? Did it drive key milestones like staff interaction or dressing room decisions? With only PoS data, you can easily misunderstand what drove a promotion’s apparent effectiveness. Almost as important, in-store journey measurement provides unique insight into how a promotion cannibalized shopping behaviors and generated new opportunities. When you change navigation patterns in the store, you ALWAYS cannibalize some behaviors and you nearly always disadvantage some sections/products. You also create new opportunities and traffic corridors that might present additional optimization or promotion opportunities. Understanding how cannibalization and redirection worked and whether or not their impact outweighed the promotion benefits is essential to developing sound long term strategies.

And it’s not all about the customer. In digital analytics, we didn’t have to worry much about compliance issues. What you pushed to the website is what was on the website. With dozens, hundreds or thousands of stores to manage, though, pushing content and making sure it’s consistent and correctly deployed is no joke. In-store customer journey measurement provides a strong behavioral compliance check. When a promotion drives specific patterns of behavior, it’s easy to see which stores are roughly following the pattern and which aren’t – given you near real-time feedback on potential compliance issues.

 

Questions you can Answer

  • Why did a promotion work better or worse than expected?
  • How did promotions localize and were there stores that didn’t “play along”?
  • How much opportunity did promotions have to influence shopping?
  • How successful were shoppers who were exposed to the promotion?
  • Did the promotion create new “impulse” opportunities?
  • Did the promotion cannibalize other areas/products and to what extent?
  • For a potential promotion, what are they placement areas that will drive exposure to the right shopping segments?
  • Were there stores that didn’t deploy or correctly implement a promotion?

Next up? A really powerful and oft-neglected aspect of customer journey measurement – staff optimization.

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.

 

Optimizing Omni-Channel with Analytics from the In-Store Customer Journey

I’m going to be co-hosting a webinar with my friend John Morrell at Datameer on Omni-Channel Analytics and using In-Store Customer Journey Data. It should be pretty cool stuff – and, of course, it’s free!

You can register here!

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!