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From April 7th – April 10th, I’ll be attending and speaking at the eMetrics Marketing Optimization conference in Toronto. These are my thoughts, feelings, and (hopefully) epiphanies from interacting with the world’s best and brightest in the world of digital measurement.

WEDNESDAY – APRIL 7TH – 9:35 PM

eMetrics day one. First off, the conference director Andrea Hadley did an awesome job putting things together. As with any conference, the true direction of thought leadership happens over dinner (not on stage), and sadly my inbox had to take priority this evening. So at this point, all I can offer is a quick rundown and a few opinions from the first sessions of the day.

Kickoff

After Andrea gave a quick intro, Alex Langshur, President of the Web Analytics Association, talked a bit about the WAA. A good summary highlighting the emphasis on community beyond Web Analytics Wednesday, legislation of privacy information, and education. I’m a big supporter of the WAA’s agenda, so it was a great way to start.

NHL.COM

Theresa Locklear, the director of Web Analytics & Customer Insight talked extensively about the reporting practices of the NHL. I was impressed by their degree of transparency – sharing numbers, talking about real case studies (albeit, mostly wins), and highlighting a few examples of real life optimization success. She also showcased an analyst team bigger than many (five to be exact). Clearly the NHL sees value in data driven decision making.

But at the same time, something kept popping into my head. There were quite a few examples of web optimization in the presentation, and when I asked about testing, they said they did none. In my mind, that means the decisions are largely left up to heuristics. Can’t black box that.

Qualitative vs. Quantitative panel

I’ll be honest – I caught what I could of the panel. But I’m trying to buy a house right now in Toronto, and in this market when the phone rings, you answer. But I did see enough to have a few observations.

A. I’m not sure I fully understand the logic behind having five people with (or formerly with) “Analyst” or “Consultant” in their title debating the merits or uses of qualitative data. I think we lost an important perspective by having no pure qualitative researchers representing their field. Back in my old web days, I used to think anyone could do qualitative research. When you work with PHD’s in the subject, people trained in observing and interpreting human behaviour, you recognize that there’s a difference. A few discussions at the WAA Mixer after the session reinforced my thinking.

B. When did “Quantitative” and “Business” become interchangeable? As the discussion progressed, it felt like a bit of a B-School / D-School discussion – not in terms of one versus the other, but in terms of purpose. Research is used for a lot of reasons, and yes, in some situations one is preferred over the other. Tonight felt rigid, like every situation was an either/or but not a both. Marko Hurst was the exception who I thought represented both disciplines well.

So tonight was largely a technology, mathematics, and alienation free evening. A positive in my eMetrics journey. Looking forward to a big day tomorrow.

WEDNESDAY – APRIL 7TH – 7:54 AM.

Today is my start of the eMetrics Marketing Optimization conference in Toronto, an event I am simultaneously looking forward to and dreading. On the “looking forward to” side, there are many, many awesome speakers presenting. From Avinash Kaushik to Jim Novo (a hero in the space) to my always entertaining former co-conspirator Christopher Berry, it’s an all star lineup.  I’ll also be presenting the numbers behind the Aviva Community Fund, a marquee project for Idea Couture in the social innovation and cause marketing space.

On the “dread” side, I can’t help but feel this conference is going to be the end of my formal association with the digital measurement community.I’ve been a practicing Web Analytics analyst for about 6 years now – an old guy in digital terms. But even in that space, I always felt a bit like an outsider as a user experience strategist (researcher) first and an analytics practitioner second – a divide that is becoming an argument of validation versus prediction.

Meanwhile, many of the dominant conversations are distant and foreign. As Web Analysts, we focus our efforts on what will derivate the largest impact. Usually that means optimizing the user experience around the highest revenue-generating opportunities. Rather than focus our efforts on any one individual, we analyze patterns because it’s a better use of our time. But it seems as though we’ve lost our own way – now focusing in on technologies and methods designed to derive actionable results around strategies that most clients can’t afford to build in the first place.

Over the past six years, I’ve seen an industry migrate technologies, define definitions, formalize education, and find its way onto the book shelves of many of my friends (a big win when you feel you’re in a niche space). Our current direction has me concerned that everything’s about to get effed up.

For the rich few, yes, these new methods and technologies can (and will) create a competitive advantage. But for the poor greater, this only further creates barriers to entry – introducing alienating languages, foreign skills, and high costs. I’ve heard many people in this industry talk about how wonderful Web analytics is because it’s accessible to marketers and (with a little help), it can be understood all the way to the top. Because of this, there is power in our recommendations.

While I can’t be certain yet, I get the sense that digital measurement is going the way of the great black box – a fifedome for a chosen few to understand. It’s also focusing on things most clients simply can’t resource against. While, yes, the decision support may push us towards greater accuracy, we can’t forget the politics behind all of this. I’ve been in enough Marketing Business Intelligence briefings to know (whether right or wrong) who makes the final decisions. In the world of mixed quantitative and qualitative data, whoever tells the best story usually gets the most attention. Black boxes don’t make for good stories.

If you ever visit MIT, you’ll see that their floors look antiquated and cracked. It’s because, in many cases, the hallways of MIT were  where the building materials and construction methods were first developed, and they want every student to now, and remember the origins.

Evolution and revolution are both needed, but that doesn’t mean we should ignore where we came from. I’m hoping as the conference moves forward I see a little humanization in the midst of our attempts to automate human behaviour.