A very smart person remarked that he liked numbers because they didn’t lie. People lie about numbers. Over the next 30 minutes, I demonstrated how two honest people can have two valid interpretations of the numbers, and have their models supported by the same facts. An hour later, during our measurement science biweekly meeting, I invited the team to analyze a 5×5 RM table, and asked a fairly loaded question about it. Diversity in opinion eventually gave way to consensus around a mean. Several honest people had feedback and conflicting models about the way the world really worked. Each version perhaps more probably true than the last. ‘Truth’ is one of those really strange words in analytics. It’s something we[…]
Month: November 2010
“L.A. Law Wikipedia Page Viewed 874 Times Today“, an article from the satirical media giant The Onion, is funny because it’s painful. The article starts off telling a story about irrelevant content. In this case, web analytics about a really old TV show on Wikipedia: “Our L.A. Law page typically gets 915 views on weekdays and 670 on weekends, so we’re about 40 off the pace,” Wikipedia web moderator Ben Stern said of the entry for the Steven Bochco series, which hasn’t aired a new episode since 1994. “Then again, the day isn’t over, and if our metrics are correct, Corbin Bernsen’s IMDB page should be viewed at least 15 more times before midnight. We generally get some runoff from[…]
One of my favourite sites is KillerStartups.com. It’s everything I love about startup culture and innovation. There are hundreds of independent variables that goes into explaining why some of these startups are going to thrive, and why most won’t. (It’s more complex than biology because people are involved!) My favourite variable is evident utility. Each startup has two paragraphs to convince me to even click to learn more. Do I see an actual use? Does it do something that somebody else already does in a better way? Cheaper way? Is it generalizable. It’s not the most predictive variable of success though. Twitter is a good example of something I could see no evident utility for. Eventually I saw utility, at[…]
It’s surprising how little time I’ve spent analyzing PowerPoint with the same rigor as social and the web. It’s amazing how that dissociation happens. There’s a set of methods that apply to these mediums over here, and a set of methods that apply to this set over here. And you can go along not even being aware of it. On Thursday, Nadia, Heather and I were remarking how a specific POV looked after Paul gotten his hands on it. The content was all there. The content was actually the same. It just looked more persuasive. Naturally, writing persuasive content is a cornerstone of marketing – so suddenly – powerpoint becomes an object of curiosity. We enumerated all the things that[…]
We did something very different for last night’s Web Analytics Wednesday Toronto. Out with the invite was a strongly worded request to produce three bullet points on one sheet. The hypothesis was that if you give analysts a platform for sharing some work with others, they will take it. The expected outcome was lower turnout with a higher intensity of participation, and a higher perception of value. Six sheets were presented by: Martin Ostrovsky (Repustate), Brian Cugelman (Alterspark), Kevin Richard, Heather Roxby, Greg Araujo, and myself (Syncapse). They were excellent and sparked very active debate. Fifteen people in total came out, including web analysts (Mark Vernon, @web_analyst), creative (@mimc03), data miners (Gar et al), developers (@chrismendis et al), managers, directors[…]
I’ve had a fairly rough 9 days with a very troublesome model. My original hypotheses are rejected. A piece of the world doesn’t really work the way that I expected. The great news is that I’m forced to look beyond the clean dataset and write new hypotheses. Even failures can be great. However, it doesn’t make for good commercial reading. Instead of having that nice, clean, nugget: Brands that did x realized y. There’s a much messier message: Neither a, b, c, d, e, f, g, h, i , j, k, l, m, n, nor p had a significant impact on y. That messier message works among marketing scientists. Usually a sound of surprise. Then acceptance when they see the[…]