I used this blog to talk to very specific groups. Sometimes it’s marketers. Sometimes web analysts. Sometimes it was candidates applying for a position. Sometimes it’s data scientists, brandsters, and social analysts. Sometimes this worked. Sometimes I confused the hell out of different audiences at different times. I’ll continue to speak to web analysts through the research committee of the Web Analytics Association, in particular, through a new experiment we’re launching and ongoing Peer Review Journals. I’ll continue to speak and collaborate with ultra niche communities – data scientists, marketing scientists, and open data professionals through christopherberry.ca. Eyes on Analytics is shifting. I’ll be curating content from not just from marketing analytics, but also from further afield. My goal is[…]

It’s worth explaining The Gartner Hype Cycle. It’s topical for 2012. It works as follows: Usually many people invent a technology during the same envelope of time. Somebody really gets hooked on the idea. That somebody executes the technology sufficiently well that it produces a technological trigger. And that gets the ball rolling. Awareness spreads through a single market, and then transmits into adjacent markets. Excitement spreads like fire. People are quick to see potential. Enthusiasm is contagious, and opposing views are downvoted into gray obscurity. Innovators are visionaries. After all, I’m winking, pointing a finger at you, and making a ‘click click’ sound my voice. ‘Hay, click click’. This is an impolite way of saying that ‘ignorance increases’. Hype[…]

You may have read something about ‘Detecting Novel Associations in Large Data Sets’, a paper appearing in Science, 334, 1518 (2011) by David N. Reshef et al.. You can check out the software here. This is an initial commentary and an explanation about what it’s all about. The Longer You Look, The More Likely Error will Find You Take a very large dataset, say, all the customers of AT&T and their calling records 2001-2011, and divide it into to two random but equal sets. Say you didn’t have any hypothesis at all. You just wanted to see what was related to each other in that set. Say, each customer record has 5000 features, including gender, date of birth, credit score,[…]

I live and work at one of the most amazing intersections. It’s also the cause of why things don’t mean what people assume that they mean. There are technologists – developers and computer scientists – who grapple with the limitations imposed by API’s and big data. There are marketing scientists – analysts and statisticians – who grapple with the limitations imposed by computability and understandability. There are marketers – brand and channel – who grapple with the limitations imposed by budget and cognitive surplus. It’s pretty amazing how a technologist, a marketing scientist, and a marketer can all be right within their own silo, their own way of thinking, but collectively be misunderstood and wrong. The confluence of all three[…]

It’s an annual tradition. We navel gaze. Every December. It’s as predictable as the tides. So, let’s talk 2012. A Forrester author, Joe Stanhope, asked us what we wanted to be when we grew up. I replied, ‘doing something meaningful’, or something to that effect. I meant it. Let’s consider something really meaningful that’s happening right now. Joe painted a picture of accelerating medium fragmentation and bloatware trying to keep up. Indeed, I’m encountering more analysts gathering the pitchforks against the new-new media. After all, if we can’t even do x right, what business do they have to even attempt y? Because it’s there. It’s never been a better time to be a marketing scientist or an analyst. It’s never[…]