Tag Archives: segmentation

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!

Measuring the Digital World

After several months in pre-order purgatory, my book, Measuring the Digital World is now available. If you’re even an occasional reader of this blog, I hope you’ll find the time to read it.

I know that’s no small ask. Reading a professional book is a big investment of time. So is reading Measuring the Digital World worth it?

Well, if you’re invested in digital optimization and analytics, I think it is – and here’s why. We work in a field that is still very immature. It’s grown up, as it were, underneath our feet. And while that kind of organic growth is always the most exciting, it’s also the most unruly. I’m betting that most of us who have spent a few years or more in digital analytics have never really had a chance to reflect on what we do and how we do it. Worse, most of those who are trying to learn the field, have to do so almost entirely by mentored trial-and-error. That’s hard. Having a framework for how and why things work makes the inevitable trial-and-error learning far more productive.

My goal in Measuring the Digital World wasn’t so much to create a how-to book as to define a discipline. I believe digital analytics is a unique field. A field defined by a few key problems that we must solve if we are to do it well. In the book, I wanted to lay out those problems and show how they can be tackled – irrespective of the tools you use or the type of digital property you care about.

At the very heart of digital analytics is a problem of description. Measurement is basic to understanding. We are born with and soon learn to speak and think in terms of measurement categories that apply to the physical world. Dimensionality, weight, speed, direction and color are some of the core measurement categories that we use over and over and over again in understanding the world we live in. These things don’t exist in the digital world.

What replaces them?

Our digital analytics tools provide the eyes and ears into the digital world. But I think we should be very skeptical of the measurement categories they suggest. Having lived through the period when those tools where designed and took their present shape, I’ve seen how flawed were the measurement conceptions that drove their form and function.

It’s not original, but it’s still true to say that our digital analytics tools mostly live at the wrong level and have the wrong set of measurement categories – that they are far too focused on web assets and far too little on web visitors.

But if this is a mere truism, it nevertheless lays the ground work for a real discipline. Because it suggests that the great challenge of digital is how to understand who people are and what they are doing using only their viewing behavior. We have to infer identity and intention from action. Probably 9 out of every 10 pages in Measuring the Digital World are concerned with how to do this.

The things that make it hard are precisely the things that define our discipline. First, to make the connection between action and both identity and intention, we have to find ways to generate meaning based on content consumption. This means understanding at a deep level what content is about – it also means making the implicit assumption that people self-select the things that interest them.

For the most part, that’s true.

But it’s also where things get tricky. Because digital properties don’t contain limitless possibilities and they impose a structure that tries to guide the user to specific actions. This creates a push-pull in every digital world. On the one hand, we’re using what people consume to understand their intention and, at the very same time, we’re constantly forcing their hand and trying to get them to do specific actions! Every digital property – no matter its purpose or design – embodies this push-pull. The result? A complex interplay between self-selection, intention and web design that makes understanding behavior in digital a constant struggle.

That’s the point – and the challenge – of digital analytics. We need to have techniques for moving from behavior to identity and intention. And we need to have techniques that control for the structure of digital properties and the presence or absence of content. These same challenges are played out on Websites, on mobile apps and, now, on omni-channel customer journeys.

This is all ground I’ve walked before, but Measuring the Digital World embodies an orderly and fairly comprehensive approach to describing these challenges and laying out the framework of our discipline. How it works. Why it’s hard. What challenges we still face. It’s all there.

So if you’re an experienced analyst and just want to reflect your intuitions and knowledge against a formal description of digital analytics and how it can be done, this book is for you. I’m pretty sure you’ll find at least a few new ideas and some new clarity around ideas you probably already have.

If you’re relatively new to the field and would like something that is intellectually a little more meaty than the “bag of tips-and-tricks” books that you’ve already read, then this book is for you. You’ll get a deep set of methods and techniques that can be applied to almost any digital property to drive better understanding and optimization. You’ll get a sense, maybe for the first time, of exactly what our discipline is – why it’s hard and why certain kinds of mistakes are ubiquitous and must be carefully guarded against.

And if you’re teaching a bunch of MBA or Business Students about digital analytics and want something that actually describes a discipline, this book is REALLY for you (well…for your students). Your students will get a true appreciation for a cutting edge analytics discipline, they’ll also get a sense of where the most interesting new problems in digital analytics are and what approaches might bear fruit. They’ll get a book that illuminates how the structure of a field – in this case digital – demands specific approaches, creates unique problems, and rewards certain types of analysis. That’s knowledge that cuts deeper than just understanding digital analytics – it goes right to the heart of what analytics is about and how it can work in any business discipline. Finally, I hope that the opportunity to tackle deep and interesting problems illuminated by the book’s framework, excites new analysts and inspires the next generation of digital analysts to go far beyond what we’ve been able to do.

 

Yes, even though I’m an inveterate reader, I know it’s no trivial thing to say “read this book”. After all, despite my copious consumption, I delve much less often into business or technical books. So many seem like fine ten-page articles stretched – I’m tempted to say distorted – into book form. You get their gist in the first five pages and the rest is just filler. That doesn’t make for a great investment of time.

And now that I’ve actually written a book, I can see why that happens. Who really has 250 pages worth of stuff to say? I’m not sure I do…actually I’m pretty sure there’s some filler tucked in there in a spot or two. But I think the ratio is pretty good.

With Measuring the Digital World I tried to do something very ambitious – define a discipline. To create the authoritative view of what digital analytics is, how it works, and why it’s different than any other field of analytics. Not to answer every question, lay out every technique or solve every problem. There are huge swaths of our field not even mentioned in the book. That doesn’t bother me. What we do is far too rich to describe in a single book or even a substantial collection. Digital is, as the title of the book suggests, a whole new world. My goal was not to explore every aspect of measuring that world, but only to show how that measurement, at its heart, must proceed. I’m surely not the right person to judge to what extent I succeeded. I hope you’ll do that.

Here’s the link to Measuring the Digital World on Amazon.

[By the way, if you’d like signed copy of Measuring the Digital World, just let me know. You can buy a copy online and I’ll send you a book-plate. I know it’s a little silly, but I confess to extreme fondness for the few signed books I possess!]

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!