Tag Archives: customer analytics

A Year in Store Analytics

It’s been a little more than a year now for me in store analytics and with the time right after Christmas and the chance to see the industry’s latest at NRF 2018, it seems like a good time to reflect on what I’ve learned and where I think things are headed.

Let’s start with the big broad view…

The Current State of Stores

Given the retail apocalypse meme, it’s obvious that 2017 was a very tough year. But the sheer number of store closings masked other statistics – including fairly robust in-store spending growth – that tell a different story. There’s no doubt that stores saddled with a lot of bad real-estate and muddied brands got pounded in 2017. I’ve written before that one of the unique economic aspects of online from a marketplace standpoint is the absence of friction. That lack of friction makes it possible for one player (you know who) to dominate in a way that could never have happened in physical retail. At the same time, digital has greatly reduced overall retail friction. And that reduction means that shoppers are not inclined to shop at bad stores just to achieve geographic convenience. So the unsatisfying end of the store market is getting absolutely crushed – and frankly – nothing is going to save it. Digital has created a world that is very unforgiving to bad experience.

On the other hand, if you can exceed that threshold, it seems pretty clear that there is a legitimate and very significant role for physical stores. And then the key question becomes, can you use analytics to make stores an asset.

So let’s talk about…

The Current State of In-Store Customer Analytics

It’s pretty rough out there. A lot of companies have experimented with in-store shopper measurement using a variety of technologies. Mostly, those efforts haven’t been successful and I think there are two reasons for that. First, this type of store analytics is new and most of the stores trying it don’t have dedicated analytics teams who can use the data. IT led projects are great for getting the infrastructure in the store, but without dedicated analytics the business value isn’t going to materialize. I saw that same pattern for years in web analytics before the digital analytics function was standardized and (nearly always) located on the business side. Second, the products most stores are using just suck. I really do feel for any analyst trying to use the deeply flawed, highly aggregated data that gets produced and presented by most of the “solutions” out there. They don’t give analysts enough access to the data to be able to clean it, and they don’t to a very good job cleaning it themselves. And even when the data is acceptable, the depth of reporting and analytics isn’t.

So when I talk to company’s that have invested in existing non Digital Mortar store analytics solutions, what I mostly hear is a litany of complaints and failure. We tried it, but it was too expensive. We didn’t see the value. It didn’t work very well.

I get it. The bottom line is that for analytics to be useful, the data has to be reasonably accurate, the analytics platform has to provide reasonable access to the data and you must have resources who can use it. Oh – and you have to be willing to make changes and actually use the data.

There’s a lot of maturing to do across all of these dimensions. It’s really just this simple. If you are serious about analytics, you have to invest in it. Dollars and organizational capital. Dollars to put the right technology in place and get the people to run it. Organizational capital to push people into actually using data to drive decisions and aggressively test.

Which brings me to….

What to invest in

Our DM1 platform obviously. But that’s just one part of bigger set of analytics decisions. I wrote pretty deeply before the holidays on the various data collection technologies in play. Based on what I saw at NRF, not that much has changed. I did see some improvement in the camera side of the house. Time of Flight cameras are  interesting and there are at least a couple of camera systems now that are beginning to do the all-important work of shopper stitching across zones. For small footprint stores there are some viable options in the camera world worth considering. I even saw a couple of face recognition systems that might make point-to-point implementations for analytics practical. Those systems are mostly focused on security though – and integration with analytics is going to be work.

I haven’t written much about mobile measurement, but geo-location within mobile apps is – to quote the Lenox mortgage guy – the biggest no-brainer in the history of earth. It’s not a complete sample. It’s not even a good sample. But it’s ridiculously easy to drop code into your mobile app to geo-locate within the store. And we can take that tracking data and run it into DM1 – giving you detailed, powerful analytics on one of the most important shopper segments you have. It costs very little. There’s no store side infrastructure or physical implementation – and the data is accurate, omni-joinable and super powerful. Small segment nirvana.

The overall data collection technology decision isn’t simple or straightforward for anyone. We’ve actually been working with Capgemini to integrate multiple technologies into their Innovation Center so that we can run workshops to help companies get a hands-on feel for each and – I hope – help folks make the right decision for their stores.

People is the biggest thing. People is the most expensive thing. People is the most important thing. It doesn’t matter how much analytic technology you bring to the table – people are the key to making it work. The vast majority of stores just don’t have store-side teams that understand behavioral data. You can try to create that or you can expand the brief of your digital or omni-channel teams and re-christen them behavioral analytics teams. I like option number two. Why not take advantage of the analytics smarts you actually have? The data, as I’ve said many times before, is eerily similar. We’ve been working hard to beef up partnerships and our own professional services to help too. But while you can use consultants to get a serious analytic effort off the ground, over time you need to own it. And that means deciding where it lives in your organization and how it fits in.

Which I know sounds a lot like…

Everything old is new again

I make no bones about the fact that I dived into store measurement because I thought the lessons of digital analytics mostly applied. In the year sense, I’ve found that to be truer than I knew and maybe even truer than I’d like. Many of the challenges I see in store analytics are the ones we spent more than decade in digital analytics gradually solving. Bad data quality and insufficient attention to making it right. IT organizations focused on collection not use. A focus on site/store measurements instead of shopper measurement.

Some of the problems are common to any analytic effort of any sort. An over-willingness to invest in technology not people (yeah – I know – I’m a technology vendor now I shouldn’t be saying this!). A lack of willingness to change operational patterns to be driven by analytics and measurement and a corresponding challenge actually using analytics. Far too many people willing to talk the talk but unable or unwilling to walk the walk necessary to do analytics and to use it. These are hard problems and it’s only select companies that will ever solve them.

Through it all I see no reason to change the core beliefs that drove me to start Digital Mortar. Shopper analytics is critical to doing retail well. In a time of disruption and innovation, it can drive massive competitive advantage if an organization is willing to embrace it seriously. But that’s not easy. It takes organizational commitment, some guts, good tools and real smarts.

Digital Mortar can provide a genuinely good tool. We can help with the smarts. Guts and commitment? That’s up to you!

Building a Career Around In-Store Measurement and Location Analytics

Over the nearly two decades I spent in digital analytics, I did a lot of selling. More than I ever wanted to. But during that time, I saw the process of selling digital analytics transformed. When I started, way back in the ‘90s, selling web analytics was evangelical. I had to convince potential clients that the Web mattered. Then I had to convince them that analytics mattered to the Web. If I got that far, I just to convince them that I was the right person to buy analytics from. But since there were only about five other people in the world doing it, that last part wasn’t so hard!

Over time, that changed. By 2005, most companies didn’t need to be convinced that the Web mattered. The role of analytics? That was still a hard sell. But by around 2012, selling digital analytics was no longer evangelical. Everyone accepted that analytics was a necessary part of digital. The only question, really, was how they would provision it.

I didn’t miss the evangelical sell. It’s a hard path. Most people are inherently conservative. Doing new stuff is risky. Most organizations are pretty poor at rewarding risk-taking. It’s great to suggest that analytics is powerful. That it will do the organization good. But for someone to take a risk on a new technology and process, there needs to be real upside. Think of it this way – just as VC’s expect out-sized returns when they invest hard-cash in risky startups, so do decision-makers who are willing to go outside the well-trodden path.

Well, with Digital Mortar, I’m right back in the evangelical world. I have to sell people on the value of in-store customer measurement and analytics – and often I have to do it within environments that are significantly disrupted and challenged. So here’s the question I ask myself – what’s in it for an influencer or a decision-maker?

I think that’s a surprisingly important question and one that doesn’t often get asked (or answered).

If you’re thinking about in-store measurement and analytics, here’s the personal questions I’d be asking if I were you (and my best guess at answers):

1. Is there a future career in this stuff?

There was a time when understanding how to create digital analytics tags was a really critical skill. That day has passed. Tagging is now a commodity skill often handled by offshore teams. In technology and analytics, in-demand skills come and go. And it’s critically important to keep building new skills. But which skills? Because there’s always lots of possible choices and most of them won’t end up being very important.

It’s pretty obvious that I believe location analytics has a big future or else I wouldn’t have started Digital Mortar. Here’s why I think this stuff matters.

I saw how compelling analytics became in the digital world. With increasing competition and interaction between digital and physical experiences, it’s just implausible to believe that we’ll continue in a world where online experiences are deeply quantified and physical experiences are a complete mystery. Every digital trend around customer centricity, experimentation and analytics is in-play in the physical world too – and all of them drive to the need for location analytics.

The thing is, measurement creates its own demand. Because once people understand that you can measure something, they WANT to know.

It will take a while. Change always does. But I have no doubt that in a few years, measurement of the physical customer journey will be well on its way to being the kind of table-stakes must-have that digital analytics is in the web world. That means new roles, new department, new jobs and new opportunities. Which brings me to…

2. Is there a real benefit to being an early adopter?

The people who got into digital analytics early carved out pretty admirable careers. Sure, they were a smart group, but in a new analytics domain, there is a real premium to early adoption. When that field starts to get traction, who gets to speak at Conferences? Who gets to write the books? It’s the early adopters. And if you’re the one speaking at conferences or writing the books, you get real opportunities to build a unique career. Being an early adopter of a technology that pans out is a huge win for your personal brand. It almost guarantees a set of terrific career options: leading a consultancy, having a cush job as an evangelist at a place like Google, getting recruited by a technology unicorn, or managing a large group at a premium company. All good stuff.

And by the way, it’s worth pointing out that this type of measurement isn’t limited to retail. You think resorts, arenas, and complex public spaces don’t need to understand the customer experience in their spaces? Location analytics won’t be in every industry. But it will be every WHERE.

But none of that stuff will happen unless you have some success.

3. Can I be successful right now?

How much success you need is easily exaggerated. Early adopters (and this is a good thing) are like fisherman. We mostly know how to tell a good story. But getting real success IS important. And fortunately, location analytics systems are good enough to do interesting measurement. The capture technologies have plenty of issues, but they work. And a platform like DM1 lets you do A LOT with the data. Best of all, if you’re already experienced with digital analytics, you know a bunch of what’s important about dealing with this data. That makes early adoption a little less frightening and a lot more likely to be successful.

There are real use-cases for this technology. Use-cases that have been hidden by the generally awful analytics capabilities of previously existing systems. This kind of measurement can identify and help solve line and queue management problems, answer questions around store and location design, resolve issues around staffing and associate optimization and feed better forecasting and allocation models, and drive powerful enhancements to customer CRM and personalization efforts.

4. How risky is it?

Middling. This stuff is still pretty new. But it’s starting to mature rapidly. The technology is getting better, the analytics software just got MUCH better, and the needs just keep growing. As with most analytics – the hard part is really organizational. Getting budget, getting authority, driving change – those are always the hardest tasks no matter how challenging things are on the data collection and analytics front. But no one’s ever seen this kind of data before. So the bar is incredibly low. When people have spent years living with hunch, intuition and door-counting as their sole metrics, you don’t have to provide world-beating analytics to look like a star.

5. Is it interesting (because no one wants to spend their life doing boring stuff)?

Yep. This stuff is deeply fascinating. Customer experience has long been one of the most interesting areas in analytics. People are great to study because their behavior is always complex. That makes the analytics a challenge. And because its people and behavior and the real-world, the problem set keeps morphing and changing. You’re not stuck analyzing the same thing for the next ten years.

Even better, identifying problematic customer behaviors is the table-set for actual business change. Once you’ve found a problem, you have to find a way to fix it. So the analytics drives directly into thinking about the business. I like that a lot. It means there’s a purpose to the measurement and the opportunity to brainstorm and design solutions not just analyze problems. If you enjoy doing digital analytics (or have always thought you might), this is an even richer and more complex set of analytic problems.

Yeah. It’s fun.

Which brings me to the bottom line. Risk is risk. A lot of businesses fail. A lot of technologies don’t take off. But I’m pretty confident that in-store journey measurement and location analytics will become a significant discipline in the next few years. If I’m right, there will be real dividends to being an early adopter. Both for the companies that do it and the people who drive it. And along the way there’s some fascinating analytics to be had and a whole bunch of really interesting stuff to learn. That doesn’t seem like such a bad deal.

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!

The Road Goes Ever On

“It’s a dangerous business,” says Bilbo Baggins, “going out your front door. You step onto the road, and if you don’t keep your feet, there’s no knowing where you might be swept off to.” Eighteen years ago I stepped outside my comfortable door and was swept out into a digital world that I – like everyone else – knew very little about. There were dragons in that wilderness, as there always are. Some we slew and some we ran away from. Some are out there still.

But though a road may go on and on, a person sometimes finds another path. In the last few months I’ve felt rather like young Mr. Baggins, visited by dwarfs and a wizard, and confronted with a map of a great unexplored wasteland, a forbiddingly guarded, lonely mountain, and a vast treasure.

It’s not so easy to give up adventuring.

Eighteen years ago when I first started thinking seriously about digital analytics, we were the poor step-child of analytics. Web analytics (as we called it back then) was pathetic. To call it analytics was a misnomer – the right word being some polyglot mash-up of hubris, false-advertising and ignorance. Perhaps “faligris” was the word we needed but didn’t have. We looked with envy at the sophisticated analytics done for mass media, retail, and direct mail.

Seriously.

My how times have changed.

I’m not going to go all Pollyanna on you. There’s still a lot not to like about the way we do digital analytics. But here’s the thing – I’m not sure there’s a field that does it better. Without really realizing it, digital has spawned a discipline of continuous improvement that includes a fairly sophisticated view of dashboarding and reporting, interesting segmentation, a decent set of techniques for specific analysis problems, and – probably most impressive – a real commitment to experimentation. Sure, most companies get a lot of this wrong. My extended discussion of the perils and problems of digital transformation isn’t (really!) just grumpiness. But the companies that do it well are truly outstanding. And even in the flawed general practice there is much to like.

The best of digital analytics these days has nothing to be ashamed of and much to be proud of.

That’s why, on the map of the digital analytics world, there are more gardens than wasteland, more cultivated field than dangerous mountain. Digital Analytics is well past the “trough of despair” in the hype-cycle – delivering tremendous value on a consistent basis. That’s a great place to be, but I’m looking for a little more adventure.

A little backstory may be in order here. Not too long ago, one of my larger clients asked us to take a look at a solution they’d tried to measure their customer’s IN STORE journeys. Their vendor wired up a sample store with a network of cameras to detect and measure in-store customer paths. It looked a lot like digital analytics. Or should I say it looked a lot like web analytics? Because in almost every respect, it reminded me of the measurement capture, reporting and analysis we did back in 1997. The data capture was expensive and broke frequently. The data was captured at the wrong level of granularity and there was no detail feed available. The reporting was right there with Webtrends 1.0. The analysis – literally – became a standing joke with our client.

It was pathetic.

Well, even from this mess, there was interesting information to be salvaged. You COULD do better reporting even on the sadly broken data being collected. But it got me thinking. Because this was a “leader” in the field.

So naturally, I checked out the rest of the field. What I found were the same type of engineering heavy, analysis tone-deaf companies that I remembered back in the old days of the web before people like Omniture and Google figured out how to do this kind of thing right. I found technology solutions desperately looking for actual business problems. I found expensive implementations that still managed to miss the really important data. I found engineers not analysts.

I found opportunity.

Because this data and these systems are very much like digital analytics. The lessons we’ve learned there about collection, KPIs, reporting, segmentation, analysis and testing all feel fresh, important and maybe even revolutionary. And this time, there’s a chance to provide an end-to-end solution that combines technology with the kind of reporting and analytics I’ve always dreamed about. There’s a chance to be the Omniture AND Semphonic of a really cool space.

I just couldn’t resist.

So I’m going to be leaving EY and, for that matter, digital analytics. I’ll miss both keenly. These past years in digital analytics have been the best and most rewarding of my professional life. I’m proud of the work I’ve done. Proud of the work we’ve done together. Proud of the discipline we’ve created. But I want to take that work and build something new from the ground up.

I’m going to build a startup dedicated to bringing the best of digital discipline and measurement to the physical world. Helping stores, malls, stadiums, banks, hospitals and who knows what else understand how to use customer behavior to actually optimize customer experience.

I want to make the best experiences in the real world every bit as seamless, personalized, and optimized as the best experiences in the digital world already are.

It’s a dangerous world out there in physical retail. They’re struggling and they don’t really know how to get better. If there really was a map, I’m pretty sure it would have a big X with “there be dragons” printed right above.

I can’t wait.

Welcome to Digital Mortar!

 

 

Competitive Advantage and Digital Transformation – Optimizing in Travel & Hospitality

In my last post I described a set of analytics projects that drive real competitive advantage in retail and eCommerce. These projects are meant to be the opening of the third and final stage of an analytically driven digital transformation. They are big, complex, important projects that make a real difference to the way the business works.

But I know folks outside retail (and they’re the majority of my client-base) get frustrated because so much of the analytics technology and conversation seems to reflect retail concerns. So in this post, I wanted to describe an alternative set of projects specifically for another industry (I picked Hospitality) and talk a little bit about some of the key analytics flashpoints in different industries. Every business is unique. There is no one right set of projects when you get to this phase of digital transformation, but there are analytics projects that are quite important to almost everyone in a given industry.

Here’s a fairly generic set of projects I’d typically attach to a presentation on digital transformation in hospitality.  You can see that about half the projects are the same as what I recommended for retail.

Digital Transformation Phase III Hospitality

Aggressive personalization is a core part of MOST good digital programs – almost regardless of industry. If you’re in health-care, financial services, retail, travel & hospitality, government or technology, then analytics-driven personalization should be a high priority. It’s actually a lot easier to say where personalization might not be near the top of the list: CPG and maybe manufacturing. In CPG, many web sites are too shallow and lack enough interesting content to make personalization effective. In fact, the Website itself is often pretty unimportant. CPG folks should probably be more worried about their marketing and social media analytics than personalization. Manufacturers might be on the same level, but a lot depends on the type of industry, how many products you have, how many audiences, and how much content. In every case, the more you have (product, audience, content), the more likely it is that personalization should be a strategic priority.

I also included Surprise-based Loyalty. Travel is actually the sector where I first developed these concepts. You can read a somewhat more detailed explanation in an article I recently published in the CIO Outlook for Travel & Hospitality. But there are quite a few reasons why hospitality, in particular, is a great place to build a surprise-based program. First, the hospitality industry has numerous opportunities to deliver surprise-based loyalty at little or no cost. That’s critical. Hospitality also has the requisite data to allow for powerful analytic targeting and has sufficient touches to make the concept powerful and workable. What’s more, most of the rewards programs in hospitality suffer from scale. Sure, a few global giants have the reach to make a traditional loyalty program appealing. But if you’re a boutique or mid-tier chain, your traditional loyalty program will never look particularly attractive. Surprise-based concepts get around all that. With no fixed cost, the ability to target and grow them organically, and real impact on loyalty, they deliver a fundamentally different kind of experience that doesn’t depend on scale and global reach.

My third project is another one that could appear almost everywhere: mobile optimization. For Hospitality, it’s particularly important to create a great mobile on-property experience and build out the mobile experience as the Hub for loyalty. Integration of mobile digital experiences with property systems enables a whole array of real experience difference makers – room selection, automatic upgrading, room bidding, expedited check-in, door control, service requests and, of course, plenty of surprise (and traditional) loyalty opportunities.

Why didn’t this show up in retail? Hey, it could. It might be sixth on my generic list. But many of the retailers I’m working with are struggling to figure out how to make mobile an important part of the experience. With all the beaconing and wifi we’ve seen, most opt-in systems simply don’t get enough adoption to make them worthwhile. I think it’s easier to drive adoption in hospitality. And adoption is critical to driving serious advantage.

When I talk about advanced Revenue Management I’m clearly hovering somewhere on the edge of what might reasonably be considered digital. There are lots of different ways to improve revenue management, but what I have in mind here are two specific types of analysis. The first is using digital view volume to feed demand signals into revenue management. This is a simple but effective technique for taking advantage of your digital data to improve your price planning. I also believe that in the zero-sum game that is room (and flight) planning, there are opportunities to use digital data collection from OTAs to reverse engineer competitor pricing strategies and then optimize your price curve to take advantage of that knowledge.

In retail, I talked about the growing importance of electronic signage and integrated digital experiences and optimizing the measurement of those (largely unmeasured) systems. In Hospitality, I’ve picked something that isn’t quite the same but falls in the same omni-channel category – optimizing the integration of on-property with digital. This cuts in both directions and overlaps with the analytics around mobile (obviously), personalization (obviously), surprise-loyalty (obviously) and revenue management. Revenue Management a little less obviously but most revenue management systems use time-based pricing not customer based pricing – often completely missing differentials in customer value from on-property behavior. Casino’s, of course, are the exception to this.

For resort properties, there are significant opportunities to integrate digital view behavior into on-property drives. But for almost every type of property there are ways to make the on-property experience better. Some of this is ridiculously easy. When I log into my hotel wifi, I almost always get the standard property page. No customization. No personalization. But I’m a heavy consumer of certain types of on-property experiences including some highly-profitable ones like late-night room service dinners. Do I ever get a dinner drive? A special offer? A loyalty treat? Nope. Pretty much never.

I put this digital/on-property integration high on the list mostly because when it comes to hospitality, the on-property experience is THE critical factor. I might love or hate the Website or even the App, but both are just little bumps on the great big behind that is the actual stay. If I can help make the stay experience better with digital, I’ve done something important.

So my top five projects for hospitality are:

  • Personalization
  • Surprise-based Loyalty
  • Digital Additions to Revenue Management
  • Mobile Experience and Loyalty Optimization
  • On-Property digital integration

As with retail, none are easy. Most involve complex integrations AND deep analytics to work well. But they form a powerful and powerfully related nexus of programs that drive real competitive advantage.

Of course, as I’ve tried to make clear, the selection of a top-five is utterly arbitrary. Every business will have its unique strategic priorities, market position, and brand. Those things matter. What’s more, the third phase of an analytics transformation is open-ended. There aren’t just five things. You don’t stop when (if ever) you’ve done these projects.

So it’s natural to ask what are some other commonly important projects that didn’t make the list (and weren’t already captured in the earlier two phases). Here, with some notes about industries, are some more things to chew on:

Digital Acquisition Optimization (Campaign-level): I’ve already covered both a campaign measurement framework and Mix/Attribution in the first two phases. But I haven’t been quite true to myself since I often tell clients to worry about optimizing your individual channels and campaigns first before you worry about attribution. There are more powerful analytic techniques for campaign-specific optimization than attribution – and many, many enterprises would be well-advised focus on those techniques as part of their overall digital transformation. I won’t say that every digital media buy I see sucks. But a lot do. This one isn’t specific to industry; it’s important to anyone dropping significant dime on digital marketing.

Right-Channeling Support:  This analytics project often makes my top-five list in financial services, technology, and health care (but it’s important in a lot of other places too). Not only is the call-center a significant cost for many an enterprise, it’s almost always a significant driver of churn and bad experience. That’s not always because call-centers are bad – it’s hard to do well. And these days, many people (I’m certainly in this bucket) flat out prefer digital servicing in most use-cases. So digital servicing is a big deal and it’s deeply analytic. Bridging digital and call is a huge analytics opportunity and one of the most important projects you can take in a digital transformation.

Digital Sales Support: If a field-sales force is a core part of your business, then digital analytics to support what they do is often in my top-five projects around transformation. Technology, Pharma, and certain areas of Financial Services (like Insurance and Wealth) all need to figure out how their digital assets play with their field sales force. Siloed approaches here are worse than silos in digital marketing attribution. You can NOT do this well unless you tackle it as an integrated effort with consistent measurement across the journey.

Content Attribution: When I was at the Digital Analytics Hub in Europe one of the most interesting parts of the discussion around transformation focused on the need for traditional companies to become, in effect, media companies. There’s nothing terribly original about this idea (not sure who’s it is), but it is terribly important and often it’s a huge stumbling block when it comes to transformation. Companies don’t build nearly enough content to be good at digital and they don’t measure it appropriately. Learning how to measure the content experience and how to take advantage of content are keys to effective digital transformation and anyone focused on building deeper sales cycles should think carefully about making content attribution a prominent part of their initial analytics plan.

Balancing Success:  One of the biggest failure points in digital transformation in my client-base involves situations where a digital property has several very important enterprise functions. Selling and generating leads, advertising and engagement, linear vs. direct consumption, building brands vs. generating revenue. These are all common examples. The problem is that most enterprises are wishy-washy when it comes to balancing these objectives. When I ask senior folks what they really want (or when I look at how people are measured), what I usually hear is both. That’s not helpful. There are analytic approaches to measuring the trade-offs in site real-estate and marketing between driving to multiple types of success. If you haven’t done the analytics work to figure this out and set appropriate incentives and performance measurements, you’re simply not going to be good at all – and perhaps any – of your core functions.

Well, I could go on of course. But I’m almost at four pages now – which I know is excessive. There are a lot of options. That’s why creating a strategic plan for analytics transformation isn’t trivial and it isn’t boilerplate. But as I pointed out in my introduction to the last post, this is the fun stuff.

In my next post, I hope to tackle those organizational issues I’ve been deferring for so long – but I may have one or two more detours up my sleeve!

[BTW – Early bird sign up for the U.S. version of the Digital Analytics Hub is coming up. If you’d like a promo code, just drop me a line!]