Tag Archives: experience engineering

Digital Transformation of the Enterprise (with a side of Big Data)

Since I finished Measuring the Digital World and got back to regular blogging, I’ve been writing an extended series on the challenges of digital in the enterprise. Like many analysts, I’m often frustrated by the way our clients approach decision-making. So often, they lack any real understanding of the customer journey, any effective segmentation scheme, any real method for either doing or incorporating analytics into their decisioning, anything more than a superficial understanding of their customers, and anything more than the empty façade of a testing program. Is it any surprise that they aren’t very good at digital? This would be frustrating but understandable if companies simply didn’t invest in these capabilities. They aren’t magic, and no large enterprise can do these things without making a significant investment. But, in fact, many companies have invested plenty with very disappointing results. That’s maddening. I want to change that – and this series is an extended meditation on what it takes to do better and how large enterprises might truly gain competitive advantage in digital.

I hope that reading these posts is useful to people, but I know, too, that it’s hard to get the time. Heaven knows I struggle to read the stuff I’d like to. So I took advantage of the slow time over the holidays to do something that’s been on my wish list for about 2 years now – take some of the presentations I do and turn them into full online webinars. I started with a whole series that captures the core elements of this series – the challenge of digital transformation.

There are two versions of this video series. The first is a set of fairly short (2-4 minute) stories that walk through how enterprise decision-making gets done, what’s wrong with the way we do it, and how we can do better. It’s a ten(!) part series and meant to be tackled in order. It’s not really all that long…like I said, most of the videos are just 2-4 minutes long. I’ve also packaged up the whole story (except Part 10) in single video that runs just a little over 20 minutes. It’s shorter than viewing all 10 of the others, but you need a decent chunk of uninterrupted time to get at it. If you’re really pressed and only want to get the key themes without the story, you can just view Parts 8-10.

Here’s the video page that has all of these laid out in order:

Digital Transformation Video Series

Check it out and let me know what you think! To me it seems like a faster, better, and more enjoyable way to get the story about digital transformation and I’m hoping it’s very shareable as well. If you’re struggling to get analytics traction in your organization, these videos might be an easy thing to share with your CMO and digital channel leads to help drive real change.

I have to say I enjoyed doing these a lot and they aren’t really hard to do. They aren’t quite professional quality, but I think they are very listenable and I’ll keep working to make them better. In fact, I enjoyed doing the digital transformation ones so much that I knocked out another this last week – Big Data Explained.

This is one of my favorite presentations of all time – it’s rich in content and intellectually interesting. Big data is a subject that is obscured by hype, self-interest, and just plain ignorance; everyone talks about it but no one has a clear, cogent explanation of what it is and why it’s important. This presentation deconstructs the everyday explanation about big data (the 4Vs) and shows why it misses the mark. But it isn’t designed to merely expose the hype, it actually builds out a clear, straightforward and important explanation of why big data is real, why it challenges common IT and analytics paradigms, and how to understand whether a problem is a big data problem…or not. I’ve written about this before, but you can’t beat a video with supporting visuals for this particular topic. It’s less than fifteen minutes and, like the digital transformation series, it’s intended for a wide audience. If you have decision-makers who don’t get big data or are skeptical of the hype, they’ll appreciate this straightforward, clear, and no-nonsense explication of what it is.

You can get it on my video page or direct on Youtube

This is also a significant topic toward the end of Measuring the Digital World where I try to lay out a forward looking plan for digital analytics as a discipline.

I’m planning to do a steady stream of these videos throughout the year so I’d love thoughts/feedback if you have suggestions!

Next week I hope to have an update on my EY Counseling Family’s work in the 538 Academy Awards challenge. We’ve built our initial Hollywood culture models – it’s pretty cool stuff and I’m excited to share the results. Our model may not be as effective as some of the other challengers (TBD), but I think it’s definitely more fun.

Analytics with a Strategic Edge

The Role of Voice of Customer in Enterprise Analytics

The vast majority of analytics effort is expended on problems that are tactical in nature. That’s not necessarily wrong. Tactics gets a bad rap, sometimes, but the truth is that the vast majority of decisions we make in almost any context are tactical. The problem isn’t that too much analytics is weighted toward tactical issues, it’s really that strategic decisions don’t use analytics at all. The biggest, most important decisions in the digital enterprise nearly always lack a foundation in data or analysis.

I’ve always disliked the idea behind “HIPPOs” – with its Dilbertian assumption that executives are idiots. That isn’t (mostly) my experience at all. But analytics does suffer from what might be described as “virtue” syndrome – the idea that something (say taxes or abstinence) is good for everyone else but not necessarily for me. Just as creative folks tend to think that what they do can’t be driven by analytics, so too is there a perception that strategic decisions must inevitably be more imaginative and intuitive and less number-driven than many decisions further down in the enterprise.

This isn’t completely wrong though it probably short-sells those mid-level decisions. Building good creative takes…creativity. It can’t be churned out by machine. Ditto for strategic decisions. There is NEVER enough information to fully determine a complex strategic decision at the enterprise level.

This doesn’t mean that data isn’t useful or should not be a driver for strategic decisions (and for creative content too). Instinct only works when it’s deeply informed about reality. Nobody has instincts in the abstract. To make a good strategic decision, a decision-maker MUST have certain kinds of data to hand and without that data, there’s nothing on which intuition, knowledge and experience can operate.

What data does a digital decision-maker need for driving strategy?

Key audiences. Customer Journey. Drivers of decision. Competitive choices.

You need to know who your audiences are and what makes them distinct. You need (as described in the last post) to understand the different journeys those audiences take and what journeys they like to take. You need to understand why they make the choices they make – what drives them to choose one product or service or another. Things like demand elasticity, brand awareness, and drivers of choice at each journey stage are critical. And, of course, you need to understand when and why those choices might favor the competition.

None of this stuff will make a strategic decision for you. It won’t tell you how much to invest in digital. Whether or not to build a mobile app. Whether personalization will provide high returns.

But without fully understanding audience, journey, drivers of decision and competitive choices, how can ANY digital decision-maker possibly arrive at an informed strategy? They can’t. And, in fact, they don’t. Because for the vast majority of enteprises, none of this information is part-and-parcel of the information environment.

I’ve seen plenty of executive dashboards that are supposed to help people run their business. They don’t have any of this stuff. I’ve seen the “four personas” puffery that’s supposed to help decision-makers understand their audience. I’ve seen how limited is the exposure executives have to journey mapping and how little it is deployed on a day-to-day basis. Worst of all, I’ve seen how absolutely pathetic is the use of voice of customer (online and offline) to help decision-makers understand why customers make the choices they do.

Voice of customer as it exists today is almost exclusively concerned with measuring customer satisfaction. There’s nothing wrong with measuring NPS or satisfaction. But these measures tell you nothing that will help define a strategy. They are at best (and they are often deeply flawed here too) measures of scoreboard – whether or not you are succeeding in a strategy.

I’m sure that people will object that knowing whether or not a strategy is succeeding is important. It is. It’s even a core part of ongoing strategy development. However, when divorced from particular customer journeys, NPS is essentially meaningless and uninterpretable. And while it truly is critical to measure whether or not a strategy is succeeding, it’s even more important to have data to help shape that strategy in the first place.

Executives just don’t get that context from their analytics teams. At best, they get little pieces of it in dribs and drabs. It is never – as it ought to be – the constant ongoing lifeblood of decision-making.

I subtitled this post “The Role of Voice of Customer in Enterprise Analytics” because of all the different types of information that can help make strategic decisions better, VoC is by far the most important. A good VoC program collects information from every channel: online and offline surveys, call-center, site feedback, social media, etc. It provides a continuing, detailed and sliceable view of audience, journey distribution and (partly) success. It’s by far the best way to help decision-makers understand why customers are making the choices they are, whether those choices are evolving, and how those choices are playing out across the competitive set. In short, it answers the majority of the questions that ought to be on the minds of decision-makers crafting a digital strategy.

This is a very different sort of executive dashboard than we typically see. It’s a true customer insights dashboard. It’s also fundamentally different than almost ANY VoC dashboard we see at any level. The vast majority of VoC reporting doesn’t provide slice-and-dice by audience and use-case – a capability which is absolutely essential to useful VoC reporting. VoC reporting is almost never based on and tied into a journey model so that the customer insights data is immediately reflective of journey stage and actionable arena. And VoC reporting almost never includes a continuous focus on exploring customer decision-making and tying that into the performance of actual initiatives.

It isn’t just a matter of a dashboard. One of the most unique and powerful aspects of digital voice-of-customer is the flexibility it provides to rapidly, efficiently and at very little cost tackle new problems. VoC should be a core part of executive decision-making with a constant cadence of research, analysis, discussion and reporting driven by specific business questions. This open and continuing dialog where VoC is a tool for decision-making is critical to integrating analytics into decisioning. If senior folks aren’t asking for new VoC research on a constant basis, you aren’t doing it right. The single best indicator of a robust VoC program in digital is the speed with which it changes.

Sadly, what decision-makers mostly get right now (if they get anything at all) is a high-level, non-segmented view of audience demographics, an occasional glimpse into high-level decision-factors that is totally divorced from both segment and journey stage, and an overweening focus on a scoreboard metric like NPS.

It’s no wonder, given such thin gruel, that decision-makers aren’t using data for strategic decisions better. If our executives mostly aren’t Dilbertian, they aren’t miracle workers either. They can’t make wine out of information water. If we want analytics to support strategy – and I assume we all do – then building a completely different sort of VoC program is the single best place to start. It isn’t everything. There are other types of data (behavioral, benchmark, econometric, etc.) that can be hugely helpful in shaping digital strategies. But a good VoC program is a huge step forward – a step forward that, if well executed – has the power to immediately transform how the digital enterprise thinks and works.

 

This is probably my last post of the year – so see you in 2016! In the meantime, my book Measuring the Digital World is now available. Could be a great way to spend your holiday down time (ideally while your resting up from time on the slopes)! Have a great holiday…

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.