Tag Archives: DA Hub

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/

 

The State of the Art in Analytics – EU Style

(You spent your vacation how?)

I spent most of the last week at the fourth annual Digital Analytics Hub Conference outside London, talking analytics. And talking. And talking. And while I love talking analytics, thank heavens I had a few opportunities to get away from the sound of my own voice and enjoy the rather more pleasing absence of sounds in the English countryside.

IMG_3757

With X Change no more, the Hub is the best conference going these days in digital analytics (full disclosure – the guys who run it are old friends of mine). It’s an immensely enjoyable opportunity to talk in-depth with serious practitioners about everything from cutting edge analytics to digital transformation to traditional digital analytics concerns around marketing analytics. Some of the biggest, best and most interesting brands in Europe were there: from digital and bricks-and-mortar behemoths to cutting-edge digital pure-plays to a pretty good sampling of the biggest consultancies in and out of the digital world.

As has been true in previous visits, I found the overall state of digital analytics in Europe to be a bit behind the U.S. – especially in terms of team-size and perhaps in data integration. But the leading companies in Europe are as good as anybody.

Here’s a sampling from my conversations:

Machine Learning

I’ve been pushing my team to grow in the machine learning space using libraries like TensorFlow to explore deep learning and see if it has potential for digital. It hasn’t been simple or easy. I’m thinking that people who talk as if you can drop a digital data set into a deep learning system and have magic happen have either:

  1. Never tried it
  2. Been trying to sell it

We’ve been having a hard time getting deep learning systems to out-perform techniques like Random Forests. We have a lot of theories about why that is, including problem selection, certain challenges with our data sets, and the ways we’ve chosen to structure our input. I had some great discussions with hardcore data scientists (and some very bright hacker analysts more in my mold) that gave me some fresh ideas. That’s lucky because I’m presenting some of this work at the upcoming eMetrics in Chicago and I want to have more impressive results to share. I’ve long insisted on the importance of structure to digital analytics and deep learning systems should be able to do a better job parsing that structure into the analysis than tools like random forests. So I’m still hopeful/semi-confident I can get better results.

In broader group discussion, one of the most controversial and interesting discussions focused on the pros-and-cons of black-box learning systems. I was a little surprised that most of the data scientist types were fairly negative on black-box techniques. I have my reservations about them and I see that organizations are often deeply distrustful of analytic results that can’t be transparently explained or which are hidden by a vendor. I get that. But opacity and performance aren’t incompatible. Just try to get an explanation of Google’s AlphaGo! If you can test a system carefully, how important is model transparency?

So what are my reservations? I’m less concerned about the black-boxness of a technique than I am its completeness. When it comes to things like recommendation engines, I think enterprise analysts should be able to consistently beat a turnkey blackbox (or not blackbox) system with appropriate local customization of the inputs and model. But I harbor no bias here. From my perspective it’s useful but not critical to understand the insides of a model provided we’ve been careful testing to make sure that it actually works!

Another huge discussion topic and one that I more in accord with was around the importance of not over-focusing on a single technique. Not only are there many varieties of machine learning – each with some advantages to specific problem types – but there are powerful analytic techniques outside the sphere of machine learning that are used in other disciplines and are completely untried in digital analytics. We have so much to learn and I only wish I had more time with a couple of the folks there to…talk!

New Technology

One of the innovations this year at the Hub was a New Technology Showcase. The showcase was kind of like spending a day with a Silicon Valley VC and getting presentations from the technology companies in their portfolio (which is a darn interesting way to spend a day). I didn’t know most of the companies that presented but there were a couple (Piwik and Snowplow) I’ve heard of. Snowplow, in particular, is a company that’s worth checking out. The Snowplow proposition is pretty simple. Digital data collection should be de-coupled from analysis. You’ve heard that before, right? It’s called Tag Management. But that’s not what Snowplow has in mind at all. They built a very sophisticated open-source data collection stack that’s highly performant and feeds directly into the cloud. The basic collection strategy is simple and modern. You send json objects that pass a schema reference along with the data. The schema references are versioned and updates are handled automatically for both backwardly compatible and incompatible updates. You can pass a full range of strongly-typed data and you can create cross-object contexts for things like visitors. Snowplow has built a whole bunch of simple templates to make it easier for folks used to traditional tagging to create the necessary calls. But you can pass anything to Snowplow – not just Web data. It’s very adaptable for mobile (far more so than traditional digital analytics systems) and really for any kind of data at all. Snowplow supports both real-time and batch – it’s a true lambda architecture. It seems to do a huge amount of the heavy lifting for you when it comes to creating a  modern cloud-based data collection system. And did I mention it’s open-source? Free is a pretty good price. If you’re looking for an independent data collection architecture and are okay with the cloud, you really should give it a look.

Cloud vs. On-Premise

DA Hub’s keynote featured a panel with analytics leaders from companies like Intel, ASOS and the Financial Times. Every participant was running analytics in the cloud (with both AWS and Azure represented though AWS had an unsurprising majority). Except for barriers around InfoSec, it’s unclear to me why ANY company wouldn’t be in the cloud for their analytics.

Rolling your own Technology

We are not sheep
We are not sheep

Here in the States, there’s been widespread adoption of open-source data technologies (Hadoop/Spark) to process and analyze digital data. But while I do see companies that have completely abandoned traditional SaaS analytics tools, it’s pretty rare. Mostly, the companies I see run both a SaaS solution to collect data and (perhaps) satisfy basic reporting needs as well as an open-source data platform. There was more interest in the people I talked to in the EU about a complete swap out including data collection and reporting. I even talked to folks who roll most of the visualization stack themselves with open-source solutions like D3. There are places where D3 is appropriate (you need complete customization of the surrounding interface, for example, or you need widespread but very inexpensive distribution), but I’m very far from convinced that rolling your own visualization solutions with open-source is the way to go. I would have said that same thing about data collection but…see above.

Digital Transformation

I had an exhilarating discussion group centered around digital transformation. There were a ton of heavy hitters in the room – huge enterprises deep into projects of digital transformation, major consultancies, and some legendary industry vets. It was one of the most enjoyable conference experiences I’ve ever had. I swear that we (most of us anyway) could have gone on another 2 hours or more – since we just scratched the surface of the problems. My plan for the session was to cover what defines excellence in digital (what do you have to be able to do digital well), then tackle how a large-enterprise that wants to transform in digital needs to organize itself. Finally, I wanted to cover the change management and process necessary to get from here to there. If you’re reading this post that should sound familiar!

Lane
It’s a long path

Well, we didn’t get to the third item and we didn’t finish the second. That’s no disgrace. These are big topics. But the discussion helped clarify my thinking – especially around organization and the very real challenges in scaling a startup model into something that works for a large enterprise. Much of the blending of teams and capabilities that I’ve been recommending in these posts on digital transformation are lessons I’ve gleaned from seeing digital pure-plays and how they work. But I’ve always been uncomfortably aware that the process of scaling into larger teams creates issues around corporate communications, reporting structures, and career paths that I’m not even close to solving. Not only did this discussion clarify and advance my thinking on the topic, I’m fairly confident that it was of equal service to everyone else. I really wish that same group could have spent the whole day together. A big THANKS to everyone there, you were fantastic!

I plan to write more on this in a subsequent post. And I may drop another post on Hub learnings after I peruse my notes. I’ve only hit on the big stuff – and there were a lot of smaller takeaways worth noting.

See you there!
See you there!

As I mentioned in my last post, the guys who run DA Hub are bringing it to Monterey, CA (first time in the U.S.) this September. Do check it out. It’s worth the trip (and the venue is  pretty special). I think I’m on the hook to reprise that session on digital transformation. And yes, that scares me…you don’t often catch lightning in a bottle twice.

Digital Transformation in the Enterprise – Creating Continuous Improvement

I’m writing this post as I fly to London for the Digital Analytics Hub. The Hub is in its fourth year now (two in Berlin and two in London) and I’ve managed to make it every time. Of course, doing these Conference/Vacations is a bit of a mixed blessing. I really enjoyed my time in Italy but that was more vacation than Conference. The Hub is more Conference than vacation – it’s filled with Europe’s top analytics practitioners in deep conversation on analytics. In fact, it’s my favorite analytics conference going right now. And here’s the good news, it’s coming to the States in September! So I have one more of these analytics vacations on my calendar and that should be the best one of all. If you’re looking for the ultimate analytics experience – an immersion in deep conversation with the some of the best analytics practitioners around – you should check it out.

I’ve got three topics I’m bringing to the Hub. Machine Learning for digital analytics, digital analytics forecasting and, of course, the topic at hand today, enterprise digital transformation.

In my last post, I described five initiatives that lay the foundation for analytics driven digital transformation. Those projects focus on data collection, journey mapping, behavioral segmentation, enterprise Voice of Customer (VoC) and unified marketing measurement. Together, these five initiatives provide a way to think about digital from a customer perspective. The data piece is focused on making sure that data collection to support personalization and segmentation is in place. The Journey mapping and the behavioral segmentation provide the customer context for every digital touchpoint – why it exists and what it’s supposed to do. The VoC system provides a window into who customers want and need and how they make decisions at every touchpoint. Finally, the marketing framework ensures that digital spend is optimized on an apples-to-apples basis and is focused on the right customers and actions to drive the business.

In a way, these projects are all designed to help the enterprise think and talk intelligently about the digital business. The data collection piece is designed to get organizations thinking about personalization cues in the digital experience. Journey mapping is designed to expand and frame customer experience and place customer thinking at the center of the digital strategy. Two-tiered segmentation serves to get people talking about digital success in terms of customer’s and their intent. Instead of asking questions like whether a Website is successful, it gets people thinking about whether the Website is successful for a certain type of customer with a specific journey intent. That’s a much better way to think. Similarly, the VoC system is all about getting people to focus on customer and to realize that analytics can serve decision-making on an ongoing basis. The marketing framework is all about making sure that campaigns and creative are measured to real business goals – set within the customer journey and the behavioral segmentation.

The foundational elements are also designed to help integrate analytics into different parts of the digital business. The data collection piece is targeted toward direct response optimization. Journey mapping is designed to help weld strategic decisions to line manager responsibilities. Behavioral segmentation is focused on line and product managers needing tactical experience optimization. VoC is targeted toward strategic thinking and decision-making, and, of course, the marketing framework is designed to support the campaign and creative teams.

If a way to think and talk intelligently about the digital enterprise and its operations is the first step, what comes next?

All five of the initiatives that I’ve slated into the next phase are about one thing – creating a discipline of continuous improvement in the enterprise. That discipline can’t be built on top of thin air – it only works if your foundation (data, metrics, framework) supports optimization. Once it does, however, the focus should be on taking advantage of that to create continuous improvement.

The first step is massive experimentation via an analytics driven testing plan. This is partly about doing lots of experiments, yes. But even more important is that the experimentation be done as part of an overall optimization plan with tests targeted by behavioral and VoC analytics to specific experiences where the opportunity for improvement is highest. If all you’re thinking about is how many experiments you run, you’re not doing it right. Every type of customer and every part of their journey should have tests targeted toward its improvement.

Similarly on the marketing side, phase II is about optimizing against the unified measurement framework with both mix and control group testing. Mix is a top-down approach that works against your overall spending – regardless of channel type or individual measurement. Control group testing is nothing more than experimentation in the marketing world. Control groups have been a key part of marketing since the early direct response days. They’re easier to implement and more accurate in establishing true lift and incrementality than mathematical attribution solutions.

The drive toward continuous improvement doesn’t end there, however. I’m a big fan for tool-based reporting as a key part of the second phase of analytics driven transformation. The idea behind tool-based reporting is simple but profound. Instead of reports as static, historical tools to describe what happened, the idea is that reports contain embedded predictive models that transform them into tools that can be used to understand the levers of the business and test what might happen based on different business strategies. Building tool-based reports for marketing, for product launch, for conversion funnels and for other key digital systems is deeply transformative. I describe this as shift in the organization from democratizing data to democratizing knowledge. Knowledge is better. But the advantages to tool-based reporting run even deeper. The models embedded in these reports are your best analytic thinking about how the business works. And guess what? They’ll be wrong a lot of the time and that’s a good thing. It’s a good thing because by making analytically thinking about how the business works explicit, you’ve created feedback mechanisms in the organization. When things don’t work out the way the model predicts, your analysts will hear about it and have to figure out why and how to do better. That drives continuous improvement in analytics.

A fourth key part of creating the agile enterprise – at least for sites without direct ecommerce – is value-based optimization. One of the great sins in digital measurement is leaving gaps in your ability to measure customers across their journey. I call this “closing measurement loops”. If you’re digital properties are lead generating or brand focused or informational or designed to drive off-channel or off-property (to Amazon or to a Call-Center), it’s much harder to measure whether or not they’re successful. You can measure proxies like content consumption or site satisfaction, but unless these proxies actually track to real outcomes, you’re just fooling yourself. This is important. To be good at digital and to use measurement effectively, every important measurement gap needs to be closed. There’s no one tool or method for closing measurement gaps, instead, a whole lot of different techniques with a bunch of sweat is required. Some of the most common methods for closing measurement gaps include re-survey, panels, device binding and dynamic 800 numbers.

Lastly, a key part of this whole phase is training the organization to think in terms of continuous improvement. That doesn’t happen magically and while all of the initiatives described here support that transformation, they aren’t, by themselves, enough. In my two posts on building analytics culture, I laid out a fairly straightforward vision of culture. The basic idea is that you build analytics culture my using data and analytics. Not by talking about how important data is or how people should behave. In the beginning was the deed.

Creating a constant cadence of analytics-based briefings and discussions forces the organization to think analytically. It forces analysts to understand the questions that are meaningful to the business. It forces decision-makers to reckon with data and lets them experience the power of being able to ask questions and get real answers. Just the imperative of having to say something interesting is good discipline for driving continuous improvement.

foundational transformation Step 2

That’s phase two of enterprise digital transformation. It’s all about baking continuous improvement into the organization and building on top of each element of the foundation the never ending process of getting better.

 

You might think that’s pretty much all there is to the analytics side of the digital transformation equation. Not so. In my next post, I’ll cover the next phase of analytics transformation – driving big analytics wins. So far, most of what I’ve covered is valid for any enterprise in any industry. But in the next phase, initiatives tend to be quite different depending on your industry and business model.

See you after the Hub!

Gelato was the word I meant

I spent most of the last week on holiday in Italy. But since the holiday was built around a speaking gig in Italy at the Be Wizard Digital Marketing conference I still spent a couple of days talking analytics and digital. A couple of days I thoroughly enjoyed. The conference closed with a Q&A for a small group of speakers and while I got a few real analytics questions it felt more like a meet and greet – with plenty of puff-ball questions like “what word would use to describe the conference?” A question I failed miserably with the very pathetic answer “fun”.

I guess that’s why it’s better to ask me analytics questions.

The word I probably should have chosen is “gelato”.

And not just because I hogged down my usual totally ridiculous amount of fragola, melone, cioccolato, and pesca – scoop by scoop from Rimini to Venice.

Gelato because I had a series of rich conversations with Mat Sweezey from Salesforce (nee Pardot) who gave a terrific presentation on authenticity and what it means in this new digital marketing world. It’s easy to forget how dramatically digital has changed marketing and miss some of the really important lessons from those changes. Mat also showed me a presentation on agile that blends beautifully with the digital transformation story I’ve been trying to tell in the last six months. It’s a terrific deck with some slides that explain why test&learn and agile methods work so much better than traditional methods. It’s a presentation with the signal virtue of taking very difficult concepts and making them not just clear but compelling. That’s hard to do well.

Gelato because I also talked with and enjoyed a great presentation from Chris Anderson of Cornell. Chris led a two-hour workshop in the revenue management track (which happens to be a kind of side interest of mine). His presentation focused on the impact of social media content on sites like TripAdvisor on room pricing strategies. He’s done several compelling research projects with OTAs (Online Travel Agents) looking at the influence of social media content on buying decisions. His research has looked at key variables that drive influence (number of reviews and rating), how sensitive demand is to those factors, and how that sensitivity plays out by hotel class (turns out that the riskier the lodging decision the more impactful social reviews are). He’s also looked at review response strategies on TripAdvisor and has some compelling research showing how review response can significantly improve ratings outcomes but how it’s also possible to over-respond. Respond to everything, and you actually do worse than if you respond to nothing.

That’s a fascinating finding and very much in keeping with Mat’s arguments around authenticity. If you make responding to every social media post a corporate policy, what you say is necessarily going to sound forced and artificial.

That’s why it doesn’t work.

If you’re in the hospitality industry, you should see this presentation. In fact, there are lessons here for any company interested in the impact of reviews and social content and interested in taking a more strategic view of social outreach and branding. I think Chris’ data suggest significant and largely unexplored opportunities for both better revenue management decisions around OTA pricing and better strategies around the review ask.

Gelato because there was one question I didn’t get to answer that I wanted to (and somehow no matter how much gelato I consume I always want a little more).

Since I had to have translations of the panel questions at the end, I didn’t always get a chance to respond. Sometimes the discussion had moved on by the time I understood the question! And one of the questions – how can companies compete with publishers when it comes to content creation – seemed to me deeply related to both Mat and Chris’ presentations.

Here’s the question as I remember it:

If you’re a manufacturer or a hotel chain or a retailer, all you ever hear in digital marketing is how content is king. But you’re not a content company. So how do you compete?

The old-fashioned way is to hire an agency to write some content for you. That’s not going to work. You won’t have enough content, you’ll have to pay a lot for it, and it won’t be any good. To Mat’s point around authenticity, you’re not going to fool people. You’re not going to convince them that your content isn’t corporate, mass-produced, ad agency hack-work. Because it is and because people aren’t stupid. Building a personalization strategy to make bad content more relevant isn’t going to help much either. That’s why you don’t make it a corporate policy to reply to every review and why you don’t write replies from a central team of ad writers.

Stop trying to play by the old rules.

Make sure your customer relations, desk folks, and managers understand how to build relationships with social media and give them the tools to do it. If you want authentic content, find your evangelists. People who actually make, design, support or use your products. Give them a forum. A real one. And turn them loose. Find ways to encourage them. Find ways to magnify their voice. But turn them loose.

You can’t have it both ways. You can’t be authentic while you try to wrap every message in a Madison Avenue gift wrapping bought from the clever folks at your ad agency. Check out Mat’s presentation (he’s a Slideshare phenom). Think about the implications of unlimited content and the ways we filter. Process the implications. The world has changed and the worst strategy in the world is to keep doing things the old way.

So gelato because the Be Wizard conference, like Italy in general, was rich, sweet, cool and left me wanting to hear (and say) a bit more!

And speaking of conferences, we’re not that far away from my second European holiday with analytics baked in – The Digital Analytics Hub in London (early June). I’ve been to DA Hub several years running now – ever since two old friends of mine started it. It’s an all conversational conference modeled on X Change and it’s always one of the highlights of my year. In addition to facilitating a couple conversations, I’m also going to be leading a very deep-dive workshop into digital forecasting. I plan to walk through forecasting from the simplest sort of forecast (everything will stay the same) to increasingly advanced techniques that rely, first on averages and smoothing, and then to models. If you’re thinking about forecasting, I really think this workshop will be worth the whole conference (and the Hub is always great anyway)…

If you’ve got a chance to be in London in early June, don’t miss the Hub.