Tag Archives: advanced analytics

Measuring the Digital World – The Movie!

I’ve put together a short 20 minute video that’s a companion piece to Measuring the Digital World. It’s a guided tour through the core principles of digital analytics and a really nice introduction to the book and the field:

Measuring the Digital World : Introduction

Measuring the Digital World

An Introduction to Digital Analytics

The video introduces the unique challenges of measuring the digital world. It’s a world where none of our traditional measurement categories and concepts apply. And it doesn’t help that our tools mostly point us in the wrong direction – introducing measurement categories that are unhelpful or misleading. To measure the digital world, we need to understand customer experiences not Websites. That isn’t easy when all you know is what web pages people looked at!

But it’s precisely that leap – from consumption to intent – that underlies all digital measurement. The video borrows an example from the book (Conan the Librarian) to show how this works and why it can be powerful. This leads directly to the concepts of 2-Tiered segmentation that are central to MTDW and are the foundation of good digital measurement.

Of course, it’s not that easy. Not only is making the inference from consumption to intent hard, it’s constantly undermined by the nature of digital properties. Their limited real-estate and strong structural elements – designed to force visitors in particular directions – make it risky to assume that people viewed what they were most interested in.

This essential contradiction between the two most fundamental principles of digital analytics is what makes our discipline so hard and (also) so interesting.

Finally, the video introduces the big data story and the ways that digital data – and making the leap from consumption to intent – challenges many of our traditional IT paradigms (not to mention our supposedly purpose-built digital analytics toolkit).

Give it a look. Even if you’re an experience practitioner I think you’ll find parts of it illuminating. And if you’re new to the field or a consumer of digital reporting and analytics, I don’t think you could spend a more productive 20 minutes.

Afterward (when you want to order the book), here’s the link to it on Amazon!

Bet your Shirt on The Big Short

Early Results

We’re still tweaking the machine learning system and the best actor and actress categories. But our text/linguistic culture-matching model produced the following rank ordering for the best picture category:

MoviePicks

So if you don’t know, now you know…The Big Short wins it.

Incidentally, we also scored movies that had best actor/actress nominees (since they were in our corpus). Big Short still won, but some of those movies (such as Trumbo) scored very well. You can read that anyway you like – it might indicate that the best actor and actress nominations are heavily influenced by how much voters liked the type of movie (which is certainly plausible) or it might indicate that our model is a pretty bad predictor since those movies didn’t even garner nominations. And, of course, given our sample size, it probably means nothing at all.

I think the list makes intuitive sense – which is always something of a relief when you’ve gone the long way around with a methodology. I particularly think the bottom of the list makes sense with The Martian and Mad Max. Both movies feel well outside any current Hollywood zeitgeist (except maybe the largely silent super-model refugees in MMFR). If a system can pick the losers, perhaps it can pick the winners as well. But more important to me, it suggests that our method is doing a credible job of culture matching.

With a few more weeks, we’ll probably take a closer look at some of the classifications and see if there are any biasing words/themes that are distorting the results. This stuff is hard and all too easy to get wrong – especially in your spare time. We’ll also have results from the black-box machine learning system, though we’re not confident about it, as well as what I hope will be interesting results for the actor/actress category. We’ve never believed that the method is as applicable to that problem (predicting acting awards) but we’re fairly satisfied with the initial themes that emerged from each actor/actress so we’re a little more optimistic that we’ll have an interesting solution.

Stay tuned…