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[…]
Danny Sullivan wrote a pretty good blog post about an article getting deleted. You can read it here. I’m not so interested or outraged about it. This spawned a Hacker News thread. You can read the whole thing here. The comment I want to draw attention to comes to us from Phil Welch. It’s so good that I’m quoting it below. “Turns out if you throw together a few thousand neckbeards and convince them to play status games around building an encyclopedia, you get an encyclopedia. You also get a whole lot of stupid politics, wasted energy, process wanking, flamewars, and acronym-laden cryptic discourses where words like “arbitration” have strange, Orwellian connotations. (“Arbitration” is Wikipedia’s name for the process governing,[…]
Predictive analytics is somewhat mysterious. So, let’s shed some light on it. (Note that I’m simplifying this quite a bit to be accessible.) The first step in predictive analytics is to understand what you’re predicting. We’ll call this the Y variable. In this instance, ‘how many visits from Boston can I expect on a given day’. My Y will be ‘Visits’. I’m curious about it. Have some discipline. I see way too many analysts change the Y variable before their investigation is through. The second step is to identify all the variables that might be associated with a variation in Y. These might include factors like paid media, search, new visits, returning visits – and date. Then there are paid[…]
Gary Morgenthaler had a few interesting statements to make: “Therefore, when Siri was an independent company, its plan was to map these domains deeply and seamlessly to automate transactions for its users within them. For example, “Buy that Steve Jobs biography book and send it to my dad”; “Send a dozen yellow roses to my wife”; “Book me the usual table for 2 tonight at 8 p.m. at Giovanni’s”; and “Get me 2 box seats for the Giants game on Saturday.” Then comes the question of what solves our biggest problems. Ultimately, Siri’s value is that of automation and removing “friction” on the Internet. Siri achieves this by: (1) understanding speech input in natural language form, (2) mapping user requests[…]
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Joe Stanhope wrote a good piece for Forrester. If you have a subscription to Forrester, read it. It summarizes the state we’re in, and has a few very good points on the last page. In that piece, web analysts themselves list ‘attribution’ as a major challenge. This is a wicked problem. All the energy you put into untying that knot only causes it to become tighter. But let’s try this again, together. If you haven’t seen this previous post, it’s new to you. I drew out a conceptual model report, in part to demonstrate how cause-effect can be embedded into a report. Alright – so that’s a conceptual model. I believe that paid spend causes paid visits. I believe that[…]
The complexity in measurement ramps with the complexity of the channel. In this post, I’ll write a bit about an interpretation of systems thinking, and how I apply it to marketing and marketing modeling. We all seek to minimize complexity and maximize predictability. We want to minimize risk and maximize empowerment. We want to synthesize a huge amount of information and boil it down to a handful of levers. Levers cause empowerment and they enable people to make really good decisions. Systems thinking is an actual thing now. Some organizations already have models in place, and are all fairly standardized. Not every organization has them. Understanding them is pretty important. This is my approach: I write a load of variables[…]