The next generation of digital analytics

I spent some time earlier this year with a bunch of fellow data nerds at the Los Angeles meeting of the Digital Analytics Association, held at UCLA. The topic: “Exploring the Romance between Marketers & Data.” Talk about a Hollywood hook for a session.

At the end of the day, I had a set of highlights sketched out in my notebook, plus a lot of notes, some random tweets at https://twitter.com/erikchaz and a few business cards. Top 3 thoughts captured:

1. No reporting without context (e.g. what else was going on when someone clicked a link?)
2. Always plot trends (i.e. I don’t really care what the number is today – how does that compare to yesterday / last week / last year)
3. Track your KPIs together (Success in one channel might be due to cannibalization from another channel)

This is all about tracking the entire customer journey, and then being able to predict in the future. This whole process map tied to analytics makes me think of an @StartupLJackson Tweet, “Blessed is he who, in the name of profit, shepherds the user through the funnel, for he is truly his user’s keeper.”

@JimSterne kicked things off with a discussion regarding privacy, and how much the average person is willing to give up in exchange for perceived value. I don’t mind that Amazon knows who I am, so that they can give me good recommendations for books and toys. Netflix has made a science of this with their recommendations as well (more on Netflix data later). Jim is hoping for a future where people get more control over their online presence, but the futility of that hope was illustrated through the challenges Google is having with the EU requirement to “forget” things.

If you want to see who YOU are online (in exchange for giving up just a bit more of data), check out @Acxiom’s beta site: https://www.aboutthedata.com

Hillary Haley ‪@RPA_advertising then hit on the red flags from decontextualized data. Any element of information gathered must be considered in the context of the old press adage of How, What, When, Where & Why. If you don’t provide the context for your data, someone else will add their own (whether accurate, appropriate, or not). Tell your story – don’t let someone else tell theirs using your information.

This segued perfectly into Meta Brown (@metabrown312) and her admonishment that we need to tell stories with our data. People remember stories, not facts. If you want people to remember your presentation after walking out of the room – wrap it up in a story. Remember though, a good data story has no fluff, exaggeration, or fiction. Truth is stranger than fiction, and a good bit of data analysis is more impactful than a bad bit of exaggerated importance.

As I was driving home from UCLA, Estaban Kolsky posted on Fixing the Suckiness of Predictive Analytics (http://estebankolsky.com/2015/02/fixing-the-suckiness-of-predictive-analytics/). He raised the problem with a lot of the current methods of data analysis and the accompanying predictive models:

“The difference is the narrowness of what predictive can do today. We are simply focused on one path, one way to get from point A to point B. If last time we were at point A we took a bus to get to point B, we will do the same today. The complexity of today’s world makes those “guesses” just about impossible. What if, for example, it is raining heavily and I am in a rush? Could I take a taxi instead? Or, what f I have time and it is a beautiful day? Could I walk? Or, what if I am with someone who owns a motorcycle?”

Esteban would have enjoyed Jake Zim’s challenges in predicting first weekend box office revenues. The predictive models have to encompass the type of movie, the actors, the directors, the time of year, who else is opening, the level of online buzz, etc. Add in that the studios don’t control the theaters, and therefore don’t have quality data on attendees, and you get big data problem.

Netflix, on the other hand, HAS the data and is starting to mine it. I spoke during Q&A about how Netflix successfully predicted House of Cards. The data analysis done by Netflix that I mentioned was described in a piece published in The New Yorker last year. I personally picked up on it via Reddit:

http://www.newyorker.com/magazine/2014/02/03/outside-the-box-2

Netflix tracks not only its subscribers’ preferences and habits but also how quickly they watch each episode and how many episodes they watch in one night. It has organized its library into seventy-nine thousand categories—Foreign Sci-Fi & Fantasy, Dark Thrillers Based on Books—to better predict what you might want to watch next. “There’s a whole lot of Ph.D.-level math and statistics involved,” Hunt says. The Netflix database indicated that the original series on which “House of Cards” was based, a British production with the same name, was popular with Netflix users. So were political thrillers, Fincher’s films (“Fight Club,” “The Social Network”), and Spacey, who has starred in a range of movies, including “Se7en” (another Fincher film) and “The Usual Suspects.”

This intersection of data points (interest in a show, interest in an actor and interest in a genre) is what helped make House of Cards so successful (plus, of course, a good script and production quality).

At this point I had spent a day on the joy of data, the challenge of data, the need for good data analysis, and concluded with how much a good data scientist can make. I have been working in Excel since version 1 for the Mac in 1985, and have always loved what I could do with my computer and a decent data set. The next generation is now being asked to do even more, and massive data sets. The moment when I really felt that the world was undergoing a shift was the next day, however. My 12 year old sharing this via IM:

Dad, an easy way to build simple graphs! ‪https://lnkd.in/bZFXZcA This is so cool!

He was so excited to be able to make graphs for his 6th grade math class, and start showing off data and testing correlations. Again, he is 12. Don’t be left behind by a kid in middle school.