Tag Archives: digital segmentation

Measuring the Digital World – The Movie!

I’ve put together a short 20 minute video that’s a companion piece to Measuring the Digital World. It’s a guided tour through the core principles of digital analytics and a really nice introduction to the book and the field:

Measuring the Digital World : Introduction

Measuring the Digital World

An Introduction to Digital Analytics

The video introduces the unique challenges of measuring the digital world. It’s a world where none of our traditional measurement categories and concepts apply. And it doesn’t help that our tools mostly point us in the wrong direction – introducing measurement categories that are unhelpful or misleading. To measure the digital world, we need to understand customer experiences not Websites. That isn’t easy when all you know is what web pages people looked at!

But it’s precisely that leap – from consumption to intent – that underlies all digital measurement. The video borrows an example from the book (Conan the Librarian) to show how this works and why it can be powerful. This leads directly to the concepts of 2-Tiered segmentation that are central to MTDW and are the foundation of good digital measurement.

Of course, it’s not that easy. Not only is making the inference from consumption to intent hard, it’s constantly undermined by the nature of digital properties. Their limited real-estate and strong structural elements – designed to force visitors in particular directions – make it risky to assume that people viewed what they were most interested in.

This essential contradiction between the two most fundamental principles of digital analytics is what makes our discipline so hard and (also) so interesting.

Finally, the video introduces the big data story and the ways that digital data – and making the leap from consumption to intent – challenges many of our traditional IT paradigms (not to mention our supposedly purpose-built digital analytics toolkit).

Give it a look. Even if you’re an experience practitioner I think you’ll find parts of it illuminating. And if you’re new to the field or a consumer of digital reporting and analytics, I don’t think you could spend a more productive 20 minutes.

Afterward (when you want to order the book), here’s the link to it on Amazon!

Building Analytics Culture – One Decision at a Time

In my last post, I argued that much of what passes for “building culture” in corporate America is worthless. It’s all about talk. And whether that talk is about diversity, ethics or analytics, it’s equally arid. Because you don’t build culture by talking. You build culture though actions. By doing things right (or wrong if that’s the kind of culture you want). Not only are words not effective in building culture, they can be positively toxic. When words and actions don’t align, the dishonesty casts other – possibly more meaningful words – into disrepute. Think about which is worse – a culture where bribery is simply the accepted and normal way of getting things done (and is cheerfully acknowledged) and one where bribery is ubiquitous but is cloaked behind constant protestations of disinterest and honesty? If you’re not sure about your answer, take it down to a personal level and ask yourself the same question. Do we not like an honest villain better than a hypocrite? If hypocrisy is the compliment vice pays to virtue, it is a particularly nasty form of flattery.

What this means is that you can’t build an analytics culture by telling people to be data driven. You can’t build an analytics culture by touting the virtues of analysis. You can’t even build an analytics culture by hiring analysts. You build an analytics culture by making good (data-driven) decisions.

That’s the only way.

But how do you get an organization to make data-driven decisions? That’s the art of building culture. And in that last post, I laid out seven (a baker’s half-dozen?) tactics for building good decision-making habits: analytic reporting, analytics briefing sessions, hiring a C-Suite analytics advisor, creating measurement standards, building a rich meta-data system for campaigns and content, creating a rapid VoC capability and embracing a continuous improvement methodology like SPEED.

These aren’t just random parts of making analytic decisions. They are tactics that seem to me particularly effective in driving good habits in the organization and building the right kind of culture. But seven tactics doesn’t nearly exhaust my list. Here’s another set of techniques that are equally important in helping drive good decision-making in the organization (my original list wasn’t in any particular order so it’s not like the previous list had all the important stuff):

Yearly Agency Performance Measurement and Reviews

What it is: Having an independent annual analysis of your agency’s performance. This should include review of goals and metrics, consideration of the appropriateness of KPIs and analysis of variation in campaign performance along three dimensions (inside the campaign by element, over time, and across campaigns). This must not be done by the agency itself (duh!) or by the owners of the relationship.

Why it builds culture: Most agencies work by building strong personal relationships. There are times and ways that this can work in your favor, but from a cultural perspective it both limits and discourages analytic thinking. I see many enterprises where the agency is so strongly entrenched you literally cannot criticize them. Not only does the resulting marketing nearly always suck, but this drains the life out of an analytics culture. This is one of many ways in which building an analytic culture can conflict with other goals, but here I definitely believe analytics should win. You don’t need a too cozy relationship with your agency. You do need objective measurement of their performance.

 

Analytics Annotation / Collaboration Tool like Insight Rocket

What it is: A tool that provides a method for rich data annotation and the creation and distribution of analytic stories across the analytics team and into the organization. In Analytic Reporting, I argued for a focus on democratizing knowledge not data. Tools like Insight Rocket are a part of that strategy, since they provide a way to create and rapidly disseminate a layer of meaning on top of powerful data exploration tools like Tableau.

Why it builds culture: There aren’t that many places where technology makes much difference to culture, but there are a few. As some of my other suggestions make clear, you get better analytics culture the more you drive analytics across and into the organization (analytic reporting, C-Suite Advisor, SPEED, etc.). Tools like Insight Rocket have three virtues: they help disseminate analytics thinking not just data, they boost analytics collaboration making for better analytic teams, and they provide a repository of analytics which increases long-term leverage in the enterprise. Oh, here’s a fourth advantage, they force analysts to tell stories – meaning they have to engage with the business. That makes this piece of technology a really nice complement to my suggestion about a regular cadence of analytics briefings and a rare instance of technology deepening culture.

 

In-sourcing

What it is: Building analytics expertise internally instead of hiring it out and, most especially, instead of off-shoring it.

Why it builds culture: I’d be the last person to tell you that consulting shouldn’t have a role in the large enterprise. I’ve been a consultant for most of my working life. But we routinely advise our clients to change the way they think about consulting – to use it not as a replacement for an internal capability but as a bootstrap and supplement to that capability. If analytics is core to digital (and it is) and if digital is core to your business (which it probably is), then you need analytics to be part of your internal capability. Having strong, capable, influential on-shore employees who are analysts is absolutely necessary to analytics culture. I’ll add that while off-shoring, too, has a role, it’s a far more effective culture killer than normal consulting. Off-shoring creates a sharp divide between the analyst and the business that is fatal to good performance and good culture on EITHER side.

 

Learning-based Testing Plan

What it is: Testing plans that include significant focus on developing best design practices and resolving political issues instead of on micro-optimizations of the funnel.

Why it works: Testing is a way to make decisions. But as long as its primary use is to decide whether to show image A or image B or a button in this color or that color, it will never be used properly. To illustrate learning-based testing, I’ve used the example of video integration – testing different methods of on-page video integration, different lengths, different content types and different placements against each key segment and use-case to determine UI parameters for ALL future videos. When you test this way, you resolve hundreds of future questions and save endless future debate about what to do with this or that video. That’s learning based testing. It’s also about picking key places in the organization where political battles determine design – things like home page real-estate and the amount of advertising load on a page – and resolving them with testing; that’s learning based testing, too. Learning based testing builds culture in two ways. First, in and of itself, it drives analytic decision-making. Almost as important, it demonstrates the proper role of experimentation and should help set the table for decision-makers tests to ask for more interesting tests.

 

Control Groups

What it is: Use of control groups to measure effectiveness whenever new programs (operational or marketing) are implemented. Control groups use small population subsets chosen randomly from a target population who are given either no experience or a neutral (existing) experience instead. Nearly all tests feature a baseline control group as part of the test, but the use of control groups transcends A/B testing tools. Use of control groups common in traditional direct response marketing and can be used in a wide variety of on and offline contexts (most especially as I recently saw Elea Feit of Drexel hammer home at the DAA Symposium – as a much more effective approach to attribution).

Why it works: One of the real barriers to building culture is a classic problem in education. When you first teach students something, they almost invariably use it poorly. That can sour others on the value of the knowledge itself. When people in an organization first start using analytics, they are, quite inevitably, going to fall into the correlation trap. Correlation is not causation. But in many cases, it sure looks like it is and this leads to many, many bad decisions. How to prevent the most common error in analytics? Control groups. Control groups build culture because they get decision-makers thinking the right way about measurement and because they protect the organization from mistakes that will otherwise sour the culture on analytics.

 

Unified Success Framework

What it is: A standardized, pre-determined framework for content and campaign success measurement that includes definition of campaign types, description of key metrics for those types, and methods of comparing like campaigns on an apples-to-apples basis.

Why it works: You may not be able to make the horse drink, but leading it to water is a good start. A unified success framework puts rigor around success measurement – a critical part of building good analytics culture. On the producer side, it forces the analytics team to make real decisions about what matters and, one hopes, pushes them to prove that proxy measures (such as engagement) are real. On the consumer side, it prevents that most insidious destroyer of analytics culture, the post hoc success analysis. If you can pick your success after the game is over, you’ll always win.

 

The Enterprise VoC Dashboard

What it is: An enterprise-wide state-of-the-customer dashboard that provides a snapshot and trended look at how customer attitudes are evolving. It should include built in segmentation so that attitudinal views are ALWAYS shown sliced by key customer types with additional segmentation possible.

Why it works: There are so many good things going on here that it’s hard to enumerate them all. First, this type of dashboard is one of the best ways to distill customer-first thinking in the organization. You can’t think customer-first, until you know what the customer thinks. Second, this type of dashboard enforces a segmented view of the world. Segmentation is fundamental to critical thinking about digital problems and this sets the table for better questions and better answers in the organization. Third, opinion data is easier to absorb and use than behavioral data, making this type of dashboard particularly valuable for encouraging decision-makers to use analytics.

 

Two-Tiered Segmentation

What it is: A method that creates two-levels of segmentation in the digital channel. The first level is the traditional “who” someone is – whether in terms of persona or business relationship or key demographics. The second level captures “what” they are trying to accomplish. Each customer touch-point can be described in this type of segmentation as the intersection of who a visitor is and what their visit was for.

Why it works: Much like the VoC Dashboard, Two-Tiered Segmentation makes for dramatically better clarity around digital channel decision-making and evaluation of success. Questions like ‘Is our Website successful?’ get morphed into the much more tractable and analyzable question ‘Is our Website successful for this audience trying to do this task?’. That’s a much better question and big part of building analytics culture is getting people to ask better questions. This also happens to be the main topic of my book “Measuring the Digital World” and in it you can get a full description of both the power and the methods behind Two-Tiered Segmentation.

 

I have more, but I’m going to roll the rest into my next post on building an agile organization since they are all deeply related to the integration of capabilities in the organization. Still, that’s fifteen different tactics for building culture. None of which include mission statements, organizational alignment or C-Level support (okay, Walking the Walk is kind of that but not exactly and I didn’t include it in the fifteen) and none of which will take place in corporate retreats or all-hands conferences. That’s a good thing and makes me believe they might actually work.

Ask yourself this: is it possible to imagine an organization that does even half these things and doesn’t have a great analytics culture? I don’t think it is. Because culture just is the sum of the way your organization works and these are powerful drivers of good analytic thinking. You can imagine an organization that does these things and isn’t friendly, collaborative, responsible, flat, diverse, caring or even innovative. There are all kinds of culture, and good decision-making isn’t the only aspect of culture to care about*. But if you do these things, you will have an organization that makes consistently good decisions.

*Incidentally, if you want to build culture in any of these other ways, you have to think about similar approaches. Astronomers have a clever technique for seeing very faint objects called averted vision. The idea is that you look just to the side of the object if you want to get the most light-gathering power from your eyes. It’s the same with culture. You can’t tackle it head-on by talking about it. You have to build it just a little from the side!

SPEED: A Process for Continuous Improvement in Digital

Everyone always wants to get better. But without a formal process to drive performance, continuous improvement is more likely to be an empty platitude than a reality in the enterprise. Building that formal process isn’t trivial. Existing methodologies like Six Sigma illustrate the depth and the advantages of a true improvement process versus an ad hoc “let’s get better” attitude, but those methodologies (largely birthed in manufacturing) aren’t directly applicable to digital. In my last post, I laid out six grounding principles that underlie continuous improvement in digital. I’ll summarize them here as:

  • Small is measurable. Big changes (like website redesigns) alter too much to make optimization practical
  • Controlled Experiments are essential to measure any complex change
  • Continuous improvement will broadly target reduction in friction or improvement in segmentation
  • Acquisition and Experience (Content) are inter-related and inter-dependent
  • Audience, use-case, prequalification and target content all drive marketing performance
  • Most content changes shift behavior rather than drive clear positive or negative outcomes

Having guiding principles isn’t the same thing as having a method, but a real methodology can be fashioned from this sub-structure that will drive true continuous improvement. A full methodology needs a way to identify the right areas to work on and a process for improving those areas. At minimum, that process should include techniques for figuring out what to change and for evaluating the direction and impact of those changes. If you have that, you can drive continuous improvement.

I’ll start where I always start: segmentation. Specifically, 2-tiered segmentation. 2-tiered segmentation is a uniquely digital approach to segmentation that slices audiences by who they are (traditional segmentation) and what they are trying to accomplish (this is the second tier) in the digital channel. This matrixed segmentation scheme is the perfect table-set for continuous improvement. In fact, I don’t think it’s possible to drive continuous improvement without this type of segmentation. Real digital improvement is always relative to an audience and a use-case.

But segmentation on its own isn’t a method for continuous improvement. 2-tiered segmentation gives us a powerful framework for understanding where and why improvement might be focused, but it doesn’t tell us where to target improvements or what those improvements might be. To have a real method, we need that.

Here’s where pre-qualification comes in. One of the core principles is that acquisition and experience are inter-related and inter-dependent. This means that if you want to understand whether or not content is working (creating lift of some kind), then you have to understand the pre-existing state of the audience that consumes that content. Content with a 100% success rate may suck. Content with a 0% success rate may be outstanding. It all depends on the population you give them. Every single person in line at the DMV will stay there to get their license. That doesn’t mean the experience is a good one. It just means that the self-selected audience is determined to finish the process. We need that license! Similarly, if you direct garbage traffic to even the best content, it won’t perform at all. Acquisition and content are deeply interdependent. It’s impossible to measure the latter without understanding the former.

Fortunately, there’s a simple technique for measuring the quality of the audience sourced for any given content area that we call pre-qualification. To understand the pre-qualification level of an audience at a given content point, we use a very short (typically nor more than 3-4 questions) pop-up survey. The pre-qualification survey explores what use-case visitors are in, where they are in the buying cycle, and how committed they are to the brand. That’s it.

It may be simple, but pre-qualification is one of the most powerful tools in the digital analytics arsenal and it’s the key to a successful continuous improvement methodology.

First we segment. Then we measure pre-qualification. With these two pieces we can measure content performance by visitor type, use-case and visitor quality. That’s enough to establish which content and which marketing campaigns are truly underperforming.

How?

Hold the population, use-case and pre-qualification level constant and measure the effectiveness of content pieces and sequences in creating successful outcomes. You can’t effectively measure content performance unless you hold these three variables constant, but when you control for these three variables you open up the power of digital analytics.

We now have a way to target potential improvement areas – just pick the content with the worst performance in each cell (visitor type x visit type x qualification level).

But there is much more that we can do with these essential pieces in place. By evaluating whether content underperforms across all pre-qualification levels equally or is much worse for less qualified visitors, you can determine if the content problem is because of friction (see guiding principle #3).

Friction problems tend to impact less qualified visitors disproportionately. So if less qualified visitors within each visitor type perform even worse than expected after consuming a piece of content, then some type of friction is likely the culprit.

Further, by evaluating content performance across visitor type (within use-case and with pre-qualification held constant), you have strong clues as to whether or not there are personalization opportunities to drive segmentation improvement.

Finally, where content performs well for qualified audiences but receives a disproportionate share of unqualified visitors, you know that you have to go upstream to fix the marketing campaigns sourcing the visits and targeting the content.

Segment. Pre-Qualify. Evaluate by qualification for friction and acquisition, and by visitor type for personalization.

Step four is to explore what to change. How do you do that? Often, the best method is to ask. This is yet another area for targeted VoC, where you can explore what content people are looking for, how they make decisions, what they need to know, and how that differs by segment. A rich series of choice/decision questions should create the necessary material to craft alternative approaches to test.

You can also break up the content into discrete chunks (each with a specific meta-data purpose or role) and then create a controlled experiment that tests which content chunks are most important and deliver the most lift. This is a sub-process for testing within the larger continuous improvement process. Analytically, it should also be possible to do a form of conjoint analysis on either behavior or preferences captured in VoC.

Segment. Pre-Qualify. Evaluate. Explore.

Now you’re ready to decide on the next round of tests and experiments based on a formal process for finding where problems are, why they exist, and how they can be tackled.

Segment, Pre-Qualify. Evaluate. Explore. Decide.

SPEED.

Sure, it’s just another consulting acronym. But underneath that acronym is real method. Not squishy and not contentless. It’s a formal procedure for identifying where problems exist, what class of problems they are, what type of solution might be a fit (friction reduction or personalization), and what that solution might consist of. All wrapped together in a process that can be endlessly repeated to drive measurable, discrete improvement for every type of visitor and every type of visit across any digital channel. It’s also specifically designed to be responsive to the guiding principles enumerated above that define digital.

If you’re looking for a real continuous improvement process in digital, there’s SPEED and then there’s…

Well, as far as I know, that’s pretty much it.

 

Interested in knowing more about 2-Tiered Segmentation and Pre-Qualification, the key ingredients to SPEED? “Measuring the Digital World” provides the most detailed descriptions I’ve ever written of how to do both and is now available for pre-order on Amazon.

Continuous Improvement

Is it a Method or a Platitude?

What does it take to be good at digital? The ability to make good decisions, of course. If you run a pro football team and you make consistently good decisions about players and about coaches, and they, in turn, make consistently good decisions about preparation and plays, you’ll be successful. Most organizations aren’t setup to make good decisions in digital. They don’t have the right information to drive strategic decisions and they often lack the right processes to make good tactical decisions. I’ve highlighted four capabilities that must be knitted together to drive consistently good decisions in the digital realm: comprehensive customer journey mapping, analytics support at every level of the organization, aggressive controlled experimentation targeted to decision-support, and constant voice of customer research. For most organizations, none of these capabilities are well-baked and it’s rare that even a very good organization is excellent at more than two of these capabilities.

The Essentials for Digital Transformation
                          The Essentials for Digital Transformation

There’s a fifth spoke of this wheel, however, that isn’t so much a capability as an approach. That’s not so completely different from the others as it might seem. After all, almost every enterprise I see has a digital analytics department, a VoC capability, a customer journey map, and an A/B Testing team. In previous posts, I’ve highlighted how those capabilities are mis-used, mis-deployed or simply misunderstood. Which makes for a pretty big miss. So it’s very much true that a better approach underlies all of these capabilities. When I talk about continuous improvement, it’s not a capability at all. There’s no there, there. It’s just an approach. Yet it’s an approach that, taken seriously, can help weld these other four capabilities into a coherent whole.

The doctrine of continuous improvement is not new – in digital or elsewhere. It has a long and proven track record and it’s one of the few industry best practices with which I am in whole-hearted agreement. Too often, however, continuous improvement is treated as an empty platitude, not a method. It’s interpreted as a squishy injunction that we should always try to get better. Rah! Rah!

No.

Taken this way, it’s as contentless as interpreting evolutionary theory as survival of the fittest. Those most likely to survive are…those most likely to survive. It is the mechanism of natural selection coupled with genetic variation and mutation that gives content to evolutionary doctrine. In other words, without a process for deciding what’s fittest and a method of transmitting that fitness across generations, evolutionary theory would be a contentless tautology. The idea of continuous improvement, too, needs a method to be interesting. Everybody wants to get better all the time. There has to be a real process to make it interesting.

There are such processes, of course. Techniques like Six Sigma famously elaborate a specific method to drive continuous improvement in manufacturing processes. Unfortunately, Six Sigma isn’t directly transferable to digital analytics. We lack the critical optimization variable (defects) against which these methods work. Nor does it work to simply substitute a variable like conversion rate for defects because we lack the controlled environment necessary to believe that every customer should convert.

If Six Sigma doesn’t translate directly into digital analytics, that doesn’t mean we can’t learn from it and cadge some good ideas, though. Here are the core ideas that drive continuous improvement in digital, many of which are rooted in formal continuous improvement methodologies:

  1. It’s much easier to measure a single, specific change than a huge number of simultaneous changes. A website or mobile app is a complex set of interconnecting pieces. If you change your home page, for example, you change the dynamics of every use-case on the site. This may benefit some users and disadvantage others; it may improve one page’s performance and harm another’s. When you change an entire website at once, it’s incredibly difficult to isolate which elements improved and which didn’t. Only the holistic performance of the system can be measured on a before and after basis – and even that can be challenging if new functionality has been introduced. The more discrete and isolated a change, the easier it is to measure its true impact on the system.
  2. Where changes are specific and local, micro-conversion analytics can generally be used to assess improvement. Where changes are numerous or the impact non-local, then a controlled environment is necessary to measure improvement. A true controlled environment in digital is generally impossible but can be effectively replicated via controlled experimentation (such as A/B testing or hold-outs).
  3. Continuous improvement can be driven on a segmented or site-wide basis. Improvements that are site-wide are typically focused on reducing friction. Segmentation improvements are focused on optimizing the conversation with specific populations. Both types of improvement cycles must be addressed in any comprehensive program.
  4. Digital performance is driven by two different systems (acquisition of traffic and content performance). Despite the fact that these two systems function independently, it’s impossible to measure performance of either without measuring their interdependencies. Content performance is ALWAYS relative to the mix of audience created by the acquisition systems. This dependency is even tighter in closed loop systems like Search Engine Optimization – where the content of the page heavily determines the nature of the traffic sent AND the performance of that traffic once sourced (though the two can function quite differently with the best SEO optimized page being a very poor content performer even though it’s sourcing its own traffic).
  5. Marketing performance is a function of four things: the type of audience sourced, the use-case of the audience sourced, the pre-qualification of the audience sourced and the target content to which the audience is sourced. Continuous improvement must target all four factors to be effective.
  6. Content performance is relative to function, audience and use-case. Some content changes will be directly negative or positive (friction causing or reducing), but most will shift the distribution of behaviors. Because most impacts are shifts in the distribution of use-cases or journeys, it’s essential that the relative value of alternative paths be understood when applying continuous improvement.

These are core ideas, not a formal process. In my next post, I’ll take a shot at translating them into a formal process for digital improvement. I’m not really confident how tightly I can describe that process, but I am confident that it will capture something rather different than any current approach to digital analytics.

 

With Thanksgiving upon us now is the time to think about the perfect stocking stuffer for the digital analyst you like best. Pre-order “Measuring the Digital World” now!