It’s pretty cold in Miami – coat weather. Not quite freezing like the rest of you. But cold. A few points from day 1: I’m impressed with Jeff Robertson of Delta. It’s the first time I’ve heard a loyalty points analyst use customer centric language, doing right by people, and meaning it. His reported actions, and the manner in which he reached conclusions, is truly customer centric. His mouth was aligned with his hands and aligned with his heart. A very smart, very brilliant presentation in applied analytics. No cynicism. Loved it. Language. Much of the rhetoric is very similar to what you’ll hear at an eMetrics conference. Some of the words are a little bit different. For instance, instead[…]
I caught a fragment of comment on an analytics podcast: “Everybody has been asking me, where can I find great analysts that have the analytical skills, the communication skills, and the business acumen?” To which I laugh. Purple hippopotamus time? Mega-swiss army knife time? They are so few and far between. They exist, certainly. But in such small quantities. And there are fundamentally good reasons why this is the case. Maintaining subject matter expertise is a challenge – it mandates keeping on top of new developments and practicing them. It’s time consuming. (Seriously time consuming!) Communication is perhaps a direct statement about the ability to produce ppt’s and being concise. Being a subject matter expert does not lend itself well[…]
I appreciate how people use analytics to inquire. Let me put forward an initial schema: Sometimes an inquiry is geared towards confirmation. You’re only interested in information that supports your original point. Sometimes an inquiry is geared towards a situation. You’re only interested in knowing what was going on. So you can keep an eye on it. (Situational reporting of straight numbers is not analytics. But sometimes people think it is.) Sometimes an inquiry is geared towards explaining a (perceived) an outlier. You’re only interested in information that explains why that thing, that doesn’t make sense, happened. Sometimes an inquiry is geared towards discovery. You’re only interested in learning something new that you can ultimately use to your advantage. The[…]
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[…]
“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[…]
The obvious agenda of the next Toronto Web Analytics Wednesday is pretty obvious. When passionate developers get together, they usually hack. What happens when passionate analysts get together? That’s the question. I’ve put out a pretty basic call – bring 25 copies of a single sheet of paper to the next WAW. Have 3 bullet points and supporting data. Be prepared to distribute it and talk about what you found. It is indeed homework prior to the next one. It’s an opportunity for analysts to move beyond talking about web analytics to sharing what they do. There are loads of open data sets out there, with very, very rich datasets. Never before has there been so much opportunity. To that[…]