Tag Archives: customer experience

Customer Strategy for Retail – Using Analytics and Customer Journey Tracking

I’ve detailed five different ways that in-store customer journey tracking drives store improvement: from optimizing store merchandising to improving in-store digital experiences and tuning omni-channel visits. All are important and each can drive measurable ROI. But in-store customer journey also tracking has broad implications at the strategic level of your organization.  Everyone wants to be more customer focused. I hear that all the time. Over and over. I even agree. And if you’re delivering a physical experience to customers without adequate measurement, you’re not just delivering a sub-optimal experience, you’re missing out on an opportunity to drive customer-centric thinking deeper into your enterprise.

In organizations that take customer focus seriously, the key question isn’t what will maximize sales. It’s what does the customer like/want. Getting an organization to think that way isn’t easy and it’s not even always clear that it’s the right thing to do. I’ve seen plenty of cases where operations and sales people just roll their eyes at a customer-centric proposal – sure that the bottom-line impact will be unsustainable. I tend to shy away from absolutes. The world is a complex place and not every problem demands absolute customer focus regardless of cost. But I do know this; unless you take that customer question to heart, your customer journey exercises will fail. You really do have to care about the customer’s experience and you have to get used to thinking about it that way.

Analytics in general and in-store measurement tracking in particular is a powerful tool for driving customer-centricity. Customer experience issues aren’t captured in traditional ERP data. They don’t show up in our BI reports on product sales by SKU. They aren’t illuminated by marketing studies. To bring customer experience into focus in the organization, you need a set of tools that help the organization map, track, and study real customer experiences.

In physical measurement, store tracking systems aren’t the only tool in your customer experience toolkit (just as digital analytics tools aren’t the only tools in the digital world). Voice of Customer data, in particular, is a critical part of building customer-centric thinking and fueling both strategy and continuous improvement. For years now I’ve championed the integration of VoC data with behavioral data so that decision-makers can see and balance the trade-offs between hard goals (sales optimization) and soft goals (experience, branding, satisfaction). That’s every bit as true in physical retail as it is in eCommerce with the additional requirement that Voice of Employee becomes almost equally important.

You can’t craft and hone an effective customer journey strategy on the back of a one-time customer journey mapping consulting engagement. That doesn’t work. Part of real customer-centricity is realizing that the work of understanding and optimizing customer journeys never ends. It’s a continuous process that requires tools and organizational commitment.

But by bringing real-measurement of the in-store customer experience to your enterprise, you drive a whole new set of customer-centric questions and a fundamentally different approach to staying customer-focused into the enterprise. I spent the last few years prior to Digital Mortar helping drive enterprise digital transformation. It’s hard. But customer measurement is both a hammer and wedge into the organization; it’s one of the most effective tools around to drive organizational transformation.

Use it.

Questions you can Answer

  • What types of customer shopping experiences are there in the store?
  • How do those experiences change in nature or distribution by store type and region?
  • How do my traditional customer segments map to in-store behaviors?
  • How do loyal customer visits in-store differ from casual or non-loyal visits?
  • Are there customers who aren’t well served by the store layout?
  • Are we finding the right type of sales associate and is there incentive structure encouraging both sales and customer satisfaction?
  • Have we setup the store and store operations to minimize customer frustration?

To find out how Digital Mortar can help you improve your in-store experiences and drive transformation, drop us a line.

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.

 

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!

Join me for what I hope will be a really challenging webinar (hosted by the 4A’s) on improving customer experience with analytics. Feb. 15th at 1pm EST.

What You Will Learn

  • How behavioral segmentation creates a framework for continuous improvement
  • How you can most effectively use VoC to enhance a segmentation framework
  • How you can get around the common limitations of in-line VoC, such as sample bias and survey fatigue
  • What changes in the organization are required to really operationalize this type of process

Engineering the Digital Journey

Near the end of my last post (describing the concept of analytics across the enterprise), I argued that full spectrum analytics would  provide “a common understanding throughout the enterprise of who your customers are, what journeys they have, which journeys are easy and which a struggle for each type of customer, detailed and constantly improving profiles of those audiences and those journeys and the decision-making and attitudes that drive them, and a rich understanding of how initiatives and changes at every level of the enterprise have succeeded, failed, or changed those journeys over time.”

By my count, that admittedly too long sentence contains the word journey four times and clearly puts understanding the customer journey at the heart of analytics understanding in the enterprise.

I think that’s right.

If you think about what senior decision-makers in an organization should get from analytics, nothing seems more important than a good understanding of customers and their journeys. That same understanding is powerful and important at every level of the organization. And by creating that shared understanding, the enterprise gains something almost priceless – the ability to converse consistently and intelligently, top-to-bottom, about why programs are being implemented and what they are expected to accomplish.

This focus on the journey isn’t particularly new. It’s been almost five years since I began describing Two-Tiered Segmentation as fundamental to digital; it’s a topic I’ve returned to repeatedly and it’s the central theme of my book. In a Two-Tiered Segmentation, you segment along two dimensions: who visitors are and what they are trying to accomplish in a visit. It’s this second piece – the visit intent segmentation – that begins to capture and describe customer journey.

But if Two-Tiered Segmentation is the start of a measurement framework for customer journey, it isn’t a complete solution. It’s too digitally focused and too rooted in displayed behaviors – meaning it’s defined solely by the functionality provided by the enterprise not by the journeys your customers might actually want to take. It’s also designed to capture the points in a journey – not necessarily to lay out the broader journey in a maximally intelligible fashion.

Traditional journey mapping works from the other end of the spectrum. Starting with customers and using higher-level interview techniques, it’s designed to capture the basic things customers want to accomplish and then map those into more detailed potential touchpoints. It’s exploratory and specifically geared toward identifying gaps in functionality where customers CAN’T do the things they want or can’t do them in the channels they’d prefer.

While traditional journey mapping may feel like the right solution to creating enterprise-wide journey maps, it, too, has some problems. Because the techniques used to create journey maps are very high-level, they provide virtually no ability to segment the audience. This leads to a “one-size-fits-all” mentality that simply isn’t correct. In the real world, different audiences have significantly different journey styles, preferences and maps, and it’s only through behavioral analysis that enough detail can be exhumed about those segments to create accurate maps.

Similarly, this high-level journey mapping leads to a “golden-path” mentality that belies real world experience. When you talk to people in the abstract, it’s perfectly possible to create the ideal path to completion for any given task. But in the real world, customers will always surprise you. They start paths in odd places, go in unexpected directions, and choose channels that may not seem ideal. That doesn’t mean you can’t service them appropriately. It does mean that if you try to force every customer into a rigid “best” path you’ll likely create many bad experiences. This myth of the golden path is something we’ve seen repeatedly in traditional web analytics and it’s even more mistaken in omni-channel.

In an omni-channel world, the goal isn’t to create an ideal path to completion. It’s to understand where the customer is in their journey and adapt the immediate Touchpoint to maximize their experience. That’s a fundamentally different mindset – a network approach not a golden-path – and it’s one that isn’t well captured or supported by traditional journey mapping.

There’s one final aspect to traditional journey mapping that I find particularly troublesome – customer experience teams have traditionally approached journey mapping as a one-time, static exercise.

Mistake.

The biggest change digital brings to the enterprise is the move away from traditional project methodologies. This isn’t only an IT issue. It’s not (just) about Agile development vs. Waterfall. It’s about recognition that ALL projects in nearly all their constituent pieces, need to work in iterative fashion. You don’t build once and move on. You build, measure, tune, rebuild, measure, and so on.  Continuous improvement comes from iteration. And the implication is that analytics, design, testing, and, yes, development should all be setup to support continuous cycles of improvement.

In the well-designed digital organization, no project ever stops.

This goes for journey mapping too. Instead of one huge comprehensive journey map that never changes and covers every aspect of the enterprise, customer journeys need to be evolved iteratively as part of an experience factory approach. Yes, a high-level journey framework does need to exist to create the shared language and approach that the organization can use. But like branches on a tree, the journey map should constantly be evolved in increasingly fine-grained and detailed views of specific aspects of the journey. If you’ve commissioned a one-time customer experience journey mapping effort, congratulations; you’re already on the road to failure.

The right approach to journey mapping isn’t two-tiered segmentation or traditional customer experience maps; it’s a synthesis of the two that blends a high-level framework driven primarily by VoC and creative techniques with more detailed, measurement and channel-based approaches (like Two-Tiered Segmentation) that deliver highly segmented network-based views of the journey. The detailed approaches never stop developing, but even the high-level pieces should be continuously iterated. It’s not that you need to constantly re-work the whole framework; it’s that in a large enterprise, there are always new journeys, new content, and new opportunities evolving.

More than anything else, this need for continuous iteration is what’s changed in the world and it’s why digital is such a challenge to the large enterprise.

A great digital organization never stops measuring customer experience. It never stops designing customer experience. It never stops imagining customer experience.

That takes a factory, not a project.