Tag Archives: digital analytics

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.

Full Spectrum Analytics

Enterprises do analytics. They just don’t use analytics.

That’s the first, and for me the most frustrating, of the litany of failures I listed in my last post that drive digital incompetence in the enterprise. Most readers will assume I mean by this assertion that organizations spend time analyzing the data but then do nothing to act on the implications of that analysis. That’s true, but it’s only a small part of what I mean when I say the enterprises don’t use analytics. Nearly every enterprise that I work with or talk to has a digital analytics team ranging in size from modest to substantial. Some of these teams are very strong, some aren’t. But good or not-so-good, in almost every case, their efforts are focused on a very narrow range of analysis. Reporting on and attributing digital marketing, reporting on digital consumption, and conversion rate optimization around the funnel account for nearly all of the work these organizations produce.

Is that really all there is too digital analytics?

Though I’ve been struggling to find the right term (I’ve called it full-stack, full-spectrum and top-down analytics), the core idea is the same – every decision about digital at every level in the enterprise should be analytically driven. C-Level decision-makers who are deciding how much to invest in digital and what types of products or big-initiatives might bear fruit, senior leaders who are allocating budget and fleshing out major campaigns and initiatives, program managers who are prioritizing audiences, features and functionality, designers who are building content or campaign creative; every level and every decision should be supported and driven by data.

That simply isn’t the case at any enterprise I know. It isn’t even close to the case. Not even at the very best of the best. And the problem almost always begins at the top.

How do really senior decision-makers decide which products to invest in and how to carve up budgets? From a marketing perspective, there are organizations that efficiently use mix-modeling to support high-level decisions around marketing spend. That’s a good thing, but it’s a very small part of the equation. Senior decision-makers ought to have constantly before them a comprehensive and data-driven understanding of their customer types and customer journeys. They ought to understand which of those journeys they as a business perform well at and at which they lag behind. They ought to understand what audiences they don’t do well with, and what the keys to success for that audience are. They ought to have a deep understanding of how previous initiatives have impacted those audiences and journeys – which have been successful and which have failed.

This mostly just doesn’t exist.

Journey mapping in the organization is static, old-fashioned, non-segmented and mostly ignored. There’s no VoC surfaced to decision-makers except NPS – which is entirely useless for actually understanding your customers (instead of understanding what they think about you). There is no monitoring of journey success or failure – either overall or by audience. Where journey maps exist, they exist entirely independent of KPIs and measurement. There is no understanding of how initiatives have impacted either specific audiences or journeys. There is no interesting tracking of audiences in general, no detailed briefings about where the enterprise is failing, no deep-dives into potential target populations and what they care about. In short, C-Level decision-makers get almost no interesting or relevant data on which to base the types of decisions they actually need to make.

Given that complete absence of interesting data, what you typically get is the same old style of decision-making we’ve been at forever. Raise digital budgets by 10% because it sounds about right.  Invest in a mobile app because Gartner says mobile is the coming thing. Create a social media command center because company X has one. This isn’t transformation. It isn’t analytics. It isn’t right.

Things don’t get better as you descend the hierarchy of an organization. The senior leaders taking those high-level decisions and fleshing out programs and initiatives lack all of those same things the C-Level folks lack. They don’t get useful VoC, interesting and data-supported journey mapping, comprehensive segmented performance tracking, or interesting analysis of historical performance by initiative either. They need all that stuff too.

Worse, since they don’t have any of those things and aren’t basing their decisions on them, most initiatives are shaped without having a clear business purpose that will translate into decisions downstream around targeting, creative, functionality and, of course, measurement.

If you’re building a mobile app to have a mobile app, not because you need to improve key aspects of a universally understood and agreed upon set of customer journeys for specific audiences, how much less effective will all of the downstream decisions about that app be? From content development to campaign planning to measurement and testing, a huge number of enterprise digital initiatives are crippled from the get-go by the lack of a consistent and clear vision at the senior levels about what they are designed to accomplish.

That lack of vision is, of course, fueled by a gaping hole in enterprise measurement – the lack of a comprehensive, segmented customer journey framework that is the basis for performance measurement and customer research.

Yes, there are pockets in the enterprise where data is used. Digital campaigns do get attributed (sometimes) and optimized (sometimes). Funnels do get improved with CRO. But even these often ardent users of data work, almost always, without the big picture. They have no better framework or data around that big-picture than anyone else and, unlike their counterparts in the C-Suite, they tend to be focused almost entirely on channel level concerns. This leads, inevitably, to a host of sub-optimal but fully data-driven decisions based on a narrow view of the data, the customer, and the business function.

There are, too, vast swathes of the mid and low level digital enterprise where data is as foreign to day-to-day operations as Texas BBQ would be in Timbuktu. The agencies and internal teams that create campaigns, build content and develop tools live their lives gloriously unconstrained by data. They know almost nothing of the target audiences for which the content and campaigns are built, they have no historical tracking of creative or feature delivery correlated to journey or audience success, they get no VoC information about what those audiences lack, struggle with or make decisions using. They lack, in short, the basic data around which they might understand why they are building an experience, what it should consist of, and how it should address the specific target audiences. They generally have no idea, either, how what they build will be measured or which aspects of its usage will be chosen by the organization as Key Performance Indicators.

Take all this together and what it means is that even in the enterprise with a strong digital analytics department, the overwhelming majority of decisions about digital – including nearly all the most important choices – are made with little or no data.

This isn’t a worst-case picture. It’s almost a best-case picture. Most organizations aren’t even dimly aware of how much they lack when it comes to using data to drive digital decision-making.  Their view of digital analytics is framed by a set of preconceptions that limit its application to evaluating campaign performance or optimizing funnels.

That’s not full-spectrum analytics. It’s one little ray of light – and that a sickly, purplish hue – cast on an otherwise empty gray void. To transform the enterprise around digital – to be really good at digital with all the competitive advantage that implies – it takes analytics. But by analytics I don’t mean this pale, restricted version of digital analytics that claims for its territory nothing but a small set of choices around which marketing campaign to invest in. I mean, instead, a form of analytics that provides support for decision-makers of every type and at every level in the organization. An analytics that provides 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.

You can’t be great, or even very good, at digital without all this.

A flat-out majority of the enterprises I talk to these days are going on about transforming themselves with digital and all that implies for customer-centricity and agility. I’m pretty sure I know what they mean. They mean creating a siloed testing program and adding five people to their digital analytics team. They mean tracking NPS with their online surveys. They mean the sort of “agile” development that has lead the original creators of agile to abandon the term in despair. They mean creating a set of static journey maps which are used once by the web design team and which are never tied to any measurement. They mean, in short, to pursue the same old ways of doing business and of making decisions with a gloss of digital best practices that change almost nothing.

It’s all too easy to guess how transformative and effective these efforts will be.

Digital Transformation

With a full first draft of my book in the hands of the publishers, I’m hoping to get back to a more regular schedule of blogging. Frankly, I’m looking forward to it. It’s a lot less of a grind than the “everyday after work and all day on the weekends pace” that was needful for finishing “Measuring the Digital World”! I’ve also accumulated a fair number of ideas for things to talk about; some directly from the book and some from our ongoing practice.

The vast majority of “Measuring the Digital World” concerns topics I’ve blogged about many times: digital segmentation, functionalism, meta-data, voice-of-customer, and tracking user journeys. Essentially, the book proceeds by developing a framework for digital measurement that is independent of any particular tool, report or specific application. It’s an introduction not a bible, so it’s not like I covered tons of new ground.  But, as will happen any time you try to voice what you know, some new understandings did emerge. I spent most of a chapter trying to articulate how the impact of self-selection and site structure can be handled analytically; this isn’t new exactly, but some of the concepts I ended up using were. Sections on rolling your own experiments with analytics not testing, and the idea of use-case demand elasticity and how to measure it, introduced concepts that crystallized for me only as I wrote them down. I’m looking forward to exploring those topics further.

At the same time, we’ve been making significant strides in our digital analytics practice that I’m eager to talk about. Writing a book on digital analytics has forced me to take stock not only of what I know, but also of where we are in our profession and industry. I really don’t know if “Measuring the Digital World” is any good or not (right now, at least, I am heartily sick of it), but I do know it’s ambitious. Its goal is nothing less than to establish a substantive methodology for digital analytics. That’s been needed for a long time. Far too often, analysts don’t understand how measurement in digital actually works and are oblivious to the very real methodological challenges it presents. Their ignorance results in a great deal of bad analysis; bad analysis that is either ignored or, worse, is used by the enterprise.

Even if we fixed all the bad analysis, however, the state of digital analytics in the enterprise would still be disappointing. Perhaps even worse, the state of digital in the enterprise is equally bad. And that’s really what matters. The vast majority of companies I observe, talk to, and work with, aren’t doing digital very well. Most of the digital experiences I study are poorly integrated with offline experiences, lack any useful personalization, have terribly inefficient marketing, are poorly optimized by channel and – if at all complex – harbor major usability flaws.

This isn’t because enterprises don’t invest in digital. They do. They spend on teams, tools and vendors for content development and deployment, for analytics, for testing, and for marketing. They spend millions and millions of dollars on all of these things. They just don’t do it very well.

Why is that?

Well, what happens is this:

Enterprises do analytics. They just don’t use analytics.

Enterprises have A/B testing tools and teams and they run lots of tests. They just don’t learn anything.

Enterprises talk about making data-driven decisions. They don’t really do it. And the people who do the most talking are the worst offenders.

Everyone has gone agile. But somehow nothing is.

Everyone says they are focused on the customer. Nobody really listens to them.

It isn’t about doing analytics or testing or voice of customer. It’s about finding ways to integrate them into the organization’s decision-making. In other words, to do digital well demands a fundamental transformation in the enterprise. It can’t be done on a business as usual basis. You can add an analytics team, build an A/B testing team, spend millions on attribution tools, Hadoop platforms, and every other fancy technology for content management and analytics out there. You can buy a great CMS with all the personalization capabilities you could ever demand. And almost nothing will change.

Analytics, testing, VoC, agile, customer-focus…these are the things you MUST do if you are going to do digital well. It isn’t that people don’t understand what’s necessary. Everyone knows what it takes. It’s that, by and large, these things aren’t being done in ways that drive actual change.

Having the right methodology for digital analytics is a (small) part of that. It’s a way to do digital analytics well. And digital analytics truly is essential to delivering great digital experiences. You can’t be great – or even pretty good – without it. But that’s clearly not enough. To do digital well requires a deeper transformation; it’s a transformation that forces the enterprise to blend analytics and testing into their DNA, and to use both at every level and around every decision in the digital channel.

That’s hard. But that’s what we’re focusing on this year. Not just on doing analytics, but on digital transformation. We’re figuring out how to use our team, our methods, and our processes to drive change at the most fundamental level in the enterprise – to do digital differently: to make decisions differently, to work differently, to deliver differently and, of course, to measure differently.

As we work through delivering on digital transformation, I plan to write about that journey as well: to describe the huge problems in the way most enterprises actually do digital, to describe how analytics and testing can be integrated deep into the organization, to show how measurement can be used to change the way organizations actually think about and understand their customers, and to show how method and process can be blended to create real change. We want to drive change in the digital experience and, equally, change in the controlling enterprise, for it is from the latter that the former must come if we are to deliver sustained success.