Tag Archives: customer analytics

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!]

Analytics for a (Good) Purpose

I imagine that anyone reading my posts can tell that I love doing analytics. I mean real, hands-on, getting your cuticles data-dirty analytics. But if I have a complaint about the analytics part of what I do, it’s that so often it’s for purposes that just aren’t gripping. There’s nothing wrong with selling more insurance, getting people to view higher-value ads, or cutting a few seconds off the time it takes to complete a process. Making commerce better is a perfectly good thing to do. Commerce matters to all of us. But if there’s nothing wrong with improving commerce, neither is it food for the soul. I’ve been re-reading Tobias Wolff’s wonderful novel “Old School”, and in it, one of the professors says something like this – “Essays? We could live without essays. The world would be a little poorer – like a world without chess – but stories…stories we can’t live without.”

That’s why I’ve always loved the rare occasions when we get to turn an analytics eye on a problem that means something more. Part of my team at EY got that chance a little more than a week back when we hosted an “Analytics Hackathon” for the Earthwatch Institute.

You can check out Earthwatch here at Earthwatch.org – it’s a very cool organization. I love everything about what they do and the way they approach it. I love the science part, which is fascinating. The nature part, which is just something I happen to enjoy – my daughters will attest that I am “crazy hiker guy”. And I love the approach, that assumes we are at our best when we do good not from ideology, which is often cold and artificial, but from passion. Even more, that worthwhile commitment comes from passion tempered by knowledge. We all realize that knowledge without passion achieves little. But passion without knowledge more often does harm than good in our complex society. Building that rare combination of passion for and knowledge of the natural world strikes me as what Earthwatch is all about, and I can’t think of a more rewarding mission.

So Earthwatch provided us six years of data on their expeditioners (folks who volunteer to take field trips to support their scientific endeavors), their donors, and the intersection of the two, and let us have at it for day. They asked three big questions: what can you tell us about donors and donor patterns, how do donors and expeditioners intersect, and are there things we should know to improve the marketing of expeditions to attract volunteers?

Earthwatch Image 1Great questions all, but a lot to ask of a five-hour day.

We pre-loaded their data into Tableau, and after a brief context-setting presentation from the Earthwatch folks, we split up into groups with each group drawing a single question. Each group produced a full-on dashboard and spent some time answering the questions.

One of the great challenges for many non-profits is the split between what you do and those who pay. In the traditional enterprise, good products and service make your customers happy and willing to pay. At Earthwatch, as with many a non-profit, their mission doesn’t directly serve their donors (those who pay). So the challenge (and the opportunity) is how to connect donors to the mission.

The mechanism for doing that at Earthwatch is the expedition. Hands on participation in an Earthwatch expedition is by far the best spur to giving they have. So one of our groups focused specifically on the relationship between expeditions and giving – and what they found was fascinating and unexpected. But it’s also fair to ask what other factors might drive giving – are there demographic, lifestage, or proclivities that can be used to direct social advertising, inform partnerships or target messaging?

Unfortunately, like many an enterprise (and not just non-profits), Earthwatch hasn’t done the greatest job building out their knowledge of their customers – in this case their donors. With only age, gender and zip code to work with (and that data obviously spotty with null values dominating each demographic category), the options for look-alike or advanced targeting are fairly minimal.

However, even with such thin gruel, there are findings to be had and analysis to be done. If you graph Earthwatch’s expeditioners by age, you get a big horseshoe-like graph. Lots of teenagers. Lots of seniors. Not much in-between. That’s no surprise and probably not changeable. Graph donors, and the left-hand side of the horse-shoe (the teenagers) go away. That’s no surprise either. You can’t squeeze much water from a rock. What is surprising is that the middle part of the graph doesn’t fill-in. Aren’t the parents of those teens natural donors? Your children’s connection to an activity ought to be a powerful motivator to giving. I think there’s potentially a missed strategic opportunity here.

There were two other points that emerged from simple graphs of donations by age and donation amount by age. Earthwatch gets lots of donations from seniors. But there’s a big spike right at sixty. And there’s a pretty significant spike in donation amount right around forty. Think about that. Forty and sixty are big inflection points. They are times when almost all of us step outside the lines for at least a short while and think about the shape and nature of our life. That’s a good time to think about an Earthwatch expedition or a donation, right? This is a case where there’s no need to target a broad demographic. The combination of some key interest variables and a big birthday might be enough. It’s at least worth testing. Targeted marketers know the importance of magic moments, and the finer-grained you can make them, the more efficient you can be. For a non-profit like Earthwatch with tiny marketing dollars, the tighter you can draw the boundaries around a magic-moment, the more likely you are to be able to use it effectively.

Thinking about that donor curve also makes plain how important both patience and a long-term strategy are to a non-profit like Earthwatch (and maybe to a lot of for-profits as well). Earthwatch has been around for a long time. That means some of their early expeditioners are retirees now. If you can keep track of people for twenty, thirty or forty years, you have an opportunity to re-ignite those connections. When they have teenagers themselves, they are the right audience to target for expeditions and donations.

This long term view seems hard. But it’s exactly what great schools and universities do. They know their 25 year old graduates aren’t giving them money. But if they can create mechanism to stay in touch till those graduates hit forty, fifty and sixty, that is worth a lot. Social media is, of course, a great way to do this. And facilitating social media connections with volunteers ought to be a long-term strategic goal for any non-profit that engages with young people.

And what about all those folks who took expeditions back in the 80’s and 90’s? Track them down on LinkedIn and Facebook – that’s what interns are for – and send them something to get them back in the fold!

In my recent posts, I’ve been arguing that analytics is under-used in strategy. Mostly, this type of analytics isn’t advanced modelling or big data stuff. It’s macroeconomics not microeconomics. Just looking at the shape of the donor and expeditioner curves can help inform strategic thinking.

From a more tactical standpoint, we also looked at the relationship between their new membership program and repeat giving. Earthwatch has bounced back and forth a bit on membership, but they currently are focused on it. We found that members tended to be smaller donors (their biggest donors weren’t always members). More interesting, however, was the impact of membership on donation pattern and stability. We tracked donors who gave in ’14 before the membership program and then became members in ’15. Did they give less or more? We didn’t have the time or the tools to do this analysis properly, but it looked as if membership, on average, tended to slightly depress average donation but increase frequency of giving resulting in a net positive. As I said, we didn’t have time to really prove this, but analytically, there’s a couple of key points here. If you’re a non-profit trying to assess the impact of something like membership, you need to make sure you break the problem down into analyzable segments. That means creating cohorts of previous donors and tracking their behavior (including whether their behavior tends to improve or deteriorate over time), tracking the impact on new donors and efforts, and, in most cases, using hold-outs and control groups to make sure you’re not fooling yourself about the numbers.

Going back to the shapes of curves, the team that looked into the relationship between giving and expeditions found something truly interesting. They linked the two tables (donors/expeditioners) to isolate just the population that had gone on an expedition and donated money. Then they created a calculated variable that tracked the difference between the donation date and the expedition date and laid it out on a chart (ain’t Tableau wonderful).

Earthwatch Image 2What they found was kind of a shock. I would have expected a curve kind of like a camel’s hump after the expeditions. Not much giving ahead of time, a short latency period after the expedition, then a sharp hump followed by a quick decline and a long slow descent as the halo from the trip gradually dispersed. Much of that is exactly what they found. There isn’t much of a latency period but the there is a sharp hump followed by the quick decline and slow descent. The shocker was on the other side of the curve. It turns out that lots of expeditioners (not the teens but the adults) are quite likely to give BEFORE they travel. The team tackling this called it a “Packing Boost” (this is one of those things that makes me proud – not only did they find something interesting but they did the extra work to attach a business useful name to the phenomenon – that’s good consulting). The pre-trip donation amounts were quite a bit smaller on average, but the number of donations was almost symmetrical.

I would never have expected that.

Apparently, when people are getting ready for an expedition they are also in the mood to make a donation. I can see that, but not only was it a surprise to me, it wasn’t received wisdom at Earthwatch either. Their donation solicitations are not at all focused on the pre-trip period.

That’s potentially a huge win and an easily testable addition to their solicitation marketing program.

The third team looked at the behavior of expeditioners. Their initial analysis focused on when people book an expedition versus the type of expedition. It turns out that there are some pretty distinct types of trip. Expeditions to Africa are usually booked a long time in advance. Expeditions in the US and places like Costa Rica are more typically booked 2-3 months in advance. There are seasonal impacts as well, with most expeditions getting booked in the spring (to take place over summer).

Actionable? You bet it is. If you’re programming the hero section of the website (which happens to have a rotating set of expeditions), knowing the time-horizons for each type of trip can help you get your web marketing right. There’s also a planning element to this. If your Africa expedition isn’t largely staffed six months out, you’re in trouble. But that trip to Costa Rica still has plenty of runway.

Finally, that team looked at the impact of discounts on cancellation behavior and which expeditions were most cancelled (important from a planning perspective). They, too, ran out of time and had some tool limitations but initial analysis seems to suggest that people are less likely to cancel trips when they’ve gotten a discount. Even more suggestive, it didn’t look like the amount of the discount was hugely significant. This might indicate that some discounting is economically beneficial – even it drives no initial lift.  It’s also possible that it’s no more than an artifact of self-selection, since the discounts may be offered to customer segments that are inherently less likely to cancel (previous expeditioners, for example). It’s an unexpected and potentially important finding but like any exploratory finding, it needs testing and controls to see if it’s real.

 

I’m pretty sure our five hours of time won’t change the world. Still, we had a lot of fun doing work we genuinely enjoy for an organization that truly matters. And there’s a chance we helped out a little. That’s good enough for me.

Are there some big takeaways about analytics from our one-day Hackathon? Most of them are things we all should know.

Earthwatch helped make the process more productive by coming to the table with three real and fairly concrete problems. We don’t always get as much from clients that are investing a lot of money. Knowing the questions you want to answer is the single most important step in any analysis.

Like a lot of organizations, Earthwatch hasn’t invested as much in data collection and data quality as is ideal. Limitations on the data place real boundaries on what you can do – not only with analysis but with the fruits of that analysis in targeting and personalization.

Being open to the unexpected is critical (and sometimes that’s easier for an outside consultant without a lot of preconceptions around the business). The team that started by focusing on the impact to donations after taking an expedition ended up talking much more about the impact to donations of planning for an expedition. It wasn’t that their initial hypothesis was wrong. People do donate after going on an expedition. But they had the imagination and sense to see that a more interesting hypothesis emerged from the data.

Tableau is a great tool for visualization and data exploration, but it can’t do everything. Problems like the cohort analysis of membership or the impact of cancellation really required statistical analysis tools with more horsepower and more data manipulation capabilities. Still, the ability to quickly explore a data set across many dimensions is wonderful and the utility of that ease in the right hands is hard to overestimate.

Finally, the biggest part of any analysis is the imagination to map the data to the business problem or opportunity. Strategic insights aren’t usually driven by fancy analysis. They are more often sparked by simple views and cuts of the data (line graphs or bar charts) that make obvious some fundamental fact about the business. Sometimes data can spark new insights; sometimes it’s just a confirmation (or refutation) of strategic thoughts or business intuitions that are already on the table. Either way, it makes for a better strategy and more confident decisions.

 

Finally, one last plug for Earthwatch. What they do is important and, often, very cool (check out that Barrier Reef diving expedition). Like our Hackathon, there’s nothing wrong and everything right with having fun doing something worthwhile. So even if you’re not coming up on forty or sixty, take a look!

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.