Tag Archives: Voice of Customer

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.

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.