Tag Archives: segmentation

Join me for what I hope will be a really challenging webinar (hosted by the 4A’s) on improving customer experience with analytics. Feb. 15th at 1pm EST.

What You Will Learn

  • How behavioral segmentation creates a framework for continuous improvement
  • How you can most effectively use VoC to enhance a segmentation framework
  • How you can get around the common limitations of in-line VoC, such as sample bias and survey fatigue
  • What changes in the organization are required to really operationalize this type of process

Frictionless Competition and the Surprising Monopolization of the Internet

In the last few months I’ve been spending quite a bit of time thinking about the challenges in physical retail – stores. I’m going to be talking much more about that in the months to come, but thinking about the challenges in physical retail and whether and to what extent digital techniques might help, I’ve also had to think about why digital retail has evolved the way it has.

There’s no doubt that digital has disrupted and hurt traditional retail. But it’s a mistake to attribute that solely to advantages inherent in digital. After all, if it was just a matter of digital being superior to B&M, then Borders should have been fine moving online. That didn’t work out so well.

In fact, one of the most interesting aspects of our digital world is how a perfect leveling of the playing field has produced such a strong tendency to natural monopoly. This isn’t just about retail. In most of the key areas of internet – from retail to video streaming to music to search to ride summoning, we’ve seen an extraordinary tendency toward massive consolidation around a single leader.

It’s not exactly what most of us expected. By eliminating most barriers to entry, creating frictionless geographies, and creating technology environments that scale seamlessly to almost any size, the digital world has removed many of the traditional bastions of monopoly. Old-world monopolies used to spring from cases where scale precluded competition. If, for example, you owned the pipes that carried gas to homes or the wires that carried electricity, it was incredibly hard for anyone else to compete.

In today’s world, that kind of ownership has mostly vanished. You could argue that if you own search you own the pipes to the Web. But the analogy doesn’t hold. It doesn’t hold because anybody can create a competing search system at any time and every single internet user can have instant access to it. It doesn’t hold because there are multiple ways to pipe through the internet besides search. And it doesn’t hold because there really are no physical barriers to building or deploying that alternative search system.

So it wouldn’t be unreasonable to expect the digital world to have morphed into a wild west of tiny artisanal companies with meteoric rises, equally sudden collapses, and constant, ubiquitous competition. Mostly, though, that’s not the way it looks at all. It looks as if monopoly, despite the absence of physical barriers, is actually a more powerful tendency in the digital world than the physical world.

It’s not that hard to understand why things have gone this way. Natural monopolies around things like electricity delivery occurred because of the immense friction involved in setting up the delivery system. Economies of scale were absolutely decisive in such situations. But most traditional markets are resilient to natural monopoly because of fundamental facts of the physical world that worked AGAINST too much scale. In the physical world, it makes perfect sense to have gas stations on the opposite side of a street. And it’s quite likely that two such stations can not only co-exist but thrive despite their close proximity. After all, it’s a pain to cross the street when you want to get gas. I may prefer Whole Foods to Safeway or vice versa. But I often go the grocery store that’s closest to me regardless of brand. And when I lived in San Francisco I bought most of my Diet Coke and impulse snacks at the corner store up my block. No, it wasn’t nice and it wasn’t cheap. But it sure was close. I may like Sol Food in San Rafael better than Los Moles, but so do a lot of other people – and I hate standing in line.

The natural friction that the physical world carries in terms of geographic convenience and capacity help ensure that countless niches for delivery exist. Like my old corner store, in the physical world, you can o be worse at everything except location and still thrive.

That doesn’t happen in the digital world.

It turns out – and I guess this should be no surprise – that in a frictionless world, any small advantage can be decisive. A grocery has to be a LOT better than its competitors to get me to drive an extra 10 minutes. But online, the best grocery is always just a few milliseconds away.

It doesn’t have to be a lot better. In fact, the difference can be incredibly tiny. Absent friction, the size of the advantage is no longer that meaningful. The digital world can make even tiny advantages decisive.

So why doesn’t every aspect of the digital world turn into a monopoly?

The answer lies in segmentation. A very small advantage may be decisive in the digital world. But it’s hard to have an advantage to EVERYONE.

In areas like news and entertainment, for example, it’s impossible to produce content that is better for everyone. Age, education, interest, background, geography and countless other factors create an infinity of micro-fractures. Not only is the content itself differentiated, but it’s creation is almost equally fractured. A.O. Scott could no more produce a version of Real Housewives than Andy Cohen could write a NY Times film review.

Content creation turns out to be friction-full in a way that was somewhat obscured by the old limitations in distribution. In fact, it appears that the market for segmented content and the ability of content to create barriers to consolidation is almost limitless. That’s why there’s almost nothing so important to becoming a good digital company than content creation. It’s the best way there is to guard your marketspace.

All this suggests that there are two paths to success in the digital world. One path involves scale and the other segmentation. They aren’t mutually exclusive and the companies that do both well are formidable indeed.

 

It’s only a little more than a month till the Digital Analytics Hub in Monterey and a chance to talk all things digital – both practical and philosophical. After all, there is no monopoly on great conversation. Looking forward to talking deep analytics, natural monopolies, digital transformation and digital advantage!

A Guided Tour through Digital Analytics (Circa 2016)

I’ve been planning my schedule for the DA Hub in late September and while I find it frustrating (so much interesting stuff!), it’s also enlightening about where digital analytics is right now and where it’s headed. Every conference is a kind of mirror to its industry, of course, but that reflection is often distorted by the needs of the conference – to focus on the cutting-edge, to sell sponsorships, to encourage product adoption, etc.  With DA Hub, the Conference agenda is set by the enterprise practitioners who are leading groups – and it’s what they want to talk about. That makes the conference agenda unusually broad and, it seems to me, uniquely reflective of the state of our industry (at least at the big enterprise level).

So here’s a guided tour of my DA Hub – including what I thought was most interesting, what I choose, and why. At the end I hope that, like Indiana Jones picking the Holy Grail from a murderers row of drinking vessels, I chose wisely.

Session 1 features conversations on Video Tracking, Data Lakes, the Lifecycle of an Analyst, Building Analytics Community, Sexy Dashboards (surely an oxymoron), Innovation, the Agile Enterprise and Personalization. Fortunately, while I’d love to join both Twitch’s June Dershewitz to talk about Data Lakes and Data Swamps or Intuit’s Dylan Lewis for When Harry (Personalization) met Sally (Experimentation), I didn’t have to agonize at all, since I’m scheduled to lead a conversation on Machine Learning in Digital Analtyics. Still, it’s an incredible set of choices and represents just how much breadth there is to digital analytics practice these days.

Session 2 doesn’t make things easier. With topics ranging across Women in Analytics, Personalization, Data Science, IoT, Data Governance, Digital Product Management, Campaign Measurement, Rolling Your Own Technology, and Voice of Customer…Dang. Women in Analytics gets knocked off my list. I’ll eliminate Campaign Measurement even though I’d love to chat with Chip Strieff from Adidas about campaign optimization. I did Tom Bett’s (Financial Times) conversation on rolling your own technology in Europe this year – so I guess I can sacrifice that. Normally I’d cross the data governance session off my list. But not only am I managing some aspects of a data governance process for a client right now, I’ve known Verizon’s Rene Villa for a long time and had some truly fantastic conversations with him. So I’m tempted. On the other hand, retail personalization is of huge interest to me. So talking over personalization with Gautam Madiman from Lowe’s would be a real treat. And did I mention that I’ve become very, very interested in certain forms of IoT tracking? Getting a chance to talk with Vivint’s Brandon Bunker around that would be pretty cool. And, of course, I’ve spent years trying to do more with VoC and hearing Abercrombie & Fitch’s story with Sasha Verbitsky would be sweet. Provisionally, I’m picking IoT. I just don’t get a chance to talk IoT very much and I can’t pass up the opportunity. But personalization might drag me back in.

In the next session I have to choose between Dashboarding (the wretched state of as opposed to the sexiness of), Data Mining Methods, Martech, Next Generation Analytics, Analytics Coaching, Measuring Content Success, Leveraging Tag Management and Using Marketing Couds for Personalization. The choice is a little easier because I did Kyle Keller’s (Vox) conversation on Dashboarding two years ago in Europe. And while that session was probably the most contentious DA Hub group I’ve ever been in (and yes, it was my fault but it was also pretty productive and interesting), I can probably move on. I’m not that involved with tag management these days – a sign that it must be mature – so that’s off my list too. I’m very intrigued by Akhil Anumolu’s (Delta Airlines) session on Can Developers be Marketers? The Emerging Role of MarTech. As a washed-up developer, I still find myself believing that developers are extraordinarily useful people and vastly under-utilized in today’s enterprise. I’m also tempted by my friend David McBride’s session on Next Generation Analytics. Not only because David is one of the most enjoyable people that I’ve ever met to talk with, but because driving analytics forward is, really, my job. But I’m probably going to go with David William’s session on Marketing Clouds. David is brilliant and ASOS is truly cutting edge (they are a giant in the UK and global in reach but not as well known here), and this also happens to be an area where I’m personally involved in steering some client projects. David’s topical focus on single-vendor stacks to deliver personalization is incredibly timely for me.

Next up we have Millennials in the Analytics Workforce, Streaming Video Metrics, Breaking the Analytics Glass Ceiling, Experimentation on Steroids, Data Journalism, Distributed Social Media Platforms, Customer Experience Management, Ethics in Analytics(!), and Customer Segmentation. There are several choices in here that I’d be pretty thrilled with: Dylan’s session on Experimentation, Chip’s session on CEM and, of course, Shari Cleary’s (Viacom) session on Segmentation. After all, segmentation is, like, my favorite thing in the world. But I’m probably going to go with Lynn Lanphier’s (Best Buy) session on Data Journalism. I have more to learn in that space, and it’s an area of analytics I’ve never felt that my practice has delivered on as well as we should.

In the last session, I could choose from more on Customer Experience Management, Driving Analytics to the C-Suite, Optimizing Analytics Career-Oaths, Creating High-Impact Analytics Programs, Building Analytics Teams, Delivering Digital Products, Calculating Analytics Impact, and Moving from Report Monkey to Analytics Advisor. But I don’t get to choose. Because this is where my second session (on driving Enterprise Digital Transformation) resides. I wrote about doing this session in the EU early this summer – it was one of the best conversations around analytics I’ve had the pleasure of being part of. I’m just hoping this session can capture some of that magic. If I didn’t have hosting duties, I think I might gravitate toward Theresa Locklear’s (NFL) conversation on Return on Analytics. When we help our clients create new analytics and digital transformation strategies, we have to help them justify what always amount to significant new expenditures. So much of analytics is exploratory and foundational, however, that we don’t always have great answers about the real return. I’d love to be able to share thoughts on how to think (and talk) about analytics ROI in a more compelling fashion.

All great stuff.

We work in such a fascinating field with so many components to it. We can specialize in data science and analytics method, take care of the fundamental challenges around building data foundations, drive customer communications and personalization, help the enterprise understand and measure it’s performance, optimize relentlessly in and across channels, or try to put all these pieces together and manage the teams and people that come with that. I love that at a Conference like the Hub I get a chance to share knowledge with (very) like-minded folks and participate in conversations where I know I’m truly expert (like segmentation or analytics transformation), areas where I’d like to do better (like Data Journalism), and areas where we’re all pushing the outside of the envelope (IoT and Machine Learning) together. Seems like a wonderful trade-off all the way around.

See you there!
See you there!

https://www.digitalanalyticshub.com/dahub16-us/

 

Getting Started with Digital Transformation

For most of this year I’ve been writing an extended series on digital transformation in the enterprise. Along the way, I’ve described why organizations (particularly large ones) struggle with digital, the core capabilities necessary to do digital well, and ways in which organizations can build a better, more analytic culture. I’ve even put together a series of videos that describe how enterprises are currently driving digital and how they can do better.

I think both the current-state (what we do wrong) and the end-state (doing digital right) are compelling. In the next few posts, I’m going to wrap this series up with a discussion around how you get from here to there.

I don’t suppose anyone thinks the journey from here to there is trivial. Doing digital the way I’ve described it (see the Agile Organization) involves some pretty fundamental change: change to the way enterprises budget, change to the way they organize, and change to the way they do digital at almost every level. It also involves, and this is totally unsurprising, investments in people and technology and more than a dollop of patience. It would actually be much easier to build a good digital organization from scratch than to adapt the pieces that exist in the typical enterprise.

Change is harder than creation. It has more friction and more fail points. But change is the reality for most enterprise.

So where do you start and how do you go about building a great digital organization?

I’m going to answer that question here from an analytics perspective. That’s the easy part. Once I’ve worked through the steps in building analytics maturity and digital decisioning, I’ll tackle the organizational component, wherein I expect to hazard a series of guesses, speculation and unlikely theory to paper over the fact that almost no one has done this transformation successfully and every organization has fundamentally unique structures and people that make its dynamics deeply specific.

The foundation of any analytics program is, of course, data. One of the most satisfying developments in digital analytics in the past 3-5 years has been the dramatic improvement in the state of data collection. It used to be that EVERY engagement we undertook began with a plodding slog through data auditing and clean-up. These days, that’s more the exception than the rule. Still, there are plenty of exceptions. So the first step in just about any analytics effort is to make sure the data foundation is solid. There’s a second aspect to this that’s worth pointing out. For a lot of my clients, basic data collection is no longer much of an issue. But even where that’s true, there are often significant gaps in digital analytics data collection for personalization. So many Adobe designs are predicated on meeting reporting requirements that it’s not at all unusual for key personalization elements like filtering selections, image expansions, sorting behaviors and DHTML exposures to go largely untracked. That’s true on both the Web and Mobile sides. Part of auditing your data collection should be a careful look at whether your capturing all the personalization cues you could – and that’s often a critical foundational element for the steps to follow.

Right along with auditing your data collection comes building a comprehensive customer journey framework. I’ve added the word “framework” here not to be all “consulty” but to emphasize that a customer journey isn’t built once as a static map. That’s the old way – and it’s wrong in every respect (so be careful what you buy). It’s wrong because it’s not segmented. It’s wrong because it’s too high-level. And most of all it’s wrong because it’s too static. So while a customer journey framework is more a capability and a process than a “thing”, it’s also true that you have to start somewhere. Getting that initial segmented journey map in place provides the high-level strategic framework for your digital strategy and for your analytics and testing. It’s the key strategic piece welding your operational capabilities to your strategic vision.

My third foundational building block is (Chorus sings refrain) “2-Tiered segmentation”. I’ve written voluminously on digital segmentation and how it works, so I won’t add much more here. But if journey mapping is the piece linking your strategic vision to your operational capabilities, 2-tiered segmentation is the equivalent piece linking at the tactical level. At every touchpoint in a customer journey there is the need to understand who somebody is and where in their journey they are. That’s what 2-tiered segmentation provides.

Auditing your data, creating a journey mapping and tying that to a digital segmentation are truly foundational. They are all “you can’t get there from here without going through these” kind of activities. Almost every significant report, analysis and decision that you make will rely on these three activities.

That’s not really true for my next two foundational activities. I chose building an integrated voice of customer (VoC) capability as my fourth key building block. If you’ve read my book, you know that one of the main uses for a VoC program is to refine and tune your journey map and segmentation. So in one sense, this capability may be prior to either of those. But you can do enough VoC to support those two activities without really building a full VoC program. And what I have in mind here is a full program. What do I mean by a full program? I mean an enterprise feedback management system that makes it easy to deploy surveys at any point in the journey across any device. I mean a set of organizational processes that ideate, design, deploy, interpret and socialize VoC information constantly. I mean an enterprise-wide reporting capability that integrates different VoC sources, classifies them, tracks them, and provides drill-down (and that’s important because VoC data is virtually useless without cross-tabulation) access to them across the organization. I also mean a culture where one of the natural and immediate parts of making a decision is looking at what customer’s think and – if that isn’t available – launching a survey to figure it out. I put VoC as part of this foundational set because I think it’s one of the easiest ways to deliver real wins to the organization. I also like the idea of driving a combination of tactical (data, segmentation) and strategic (journey, VoC) initiatives in your early phases. As I’ve pointed out elsewhere, we analytics folks tend to over-focus on the tactical.

Finally, I’ve included building a campaign measurement framework into the initial set of foundational activities. This might not be the right choice for every organization, but if you spend a significant amount of money on marketing, it’s a critical element in evolving your maturity. Like data audits, a lot of my clients are already pretty good at this. For many folks, campaigns are already measured using a pretty rich and well-thought out framework and the pain point tends to be deeper – around attribution and mix. But I also see organizations jumping right to questions of attribution before they’ve really done the work necessary to pick the right KPIs to optimize against. That’s a prescription for disaster. If you don’t put in the intellectual sweat equity to understand how campaigns should be measured (and it’s often surprisingly complicated in real-world businesses where conversion rate is rarely the be-all-and-end-all of optimization), then your attribution modelling is doomed to fail.

So here’s the first five things to tackle in building out the analytics part of a digital transformation effort:

foundational Transformation Step 1Small

These five activities provide a rich foundation for analytics driven transformation along with some core strategic analytic capabilities. I’ll cover what comes after this in my next post.

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!

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

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

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

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

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

Digital Transformation Video Series

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

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

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

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

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

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

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

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!