On a separate note, I have to agree with your point about the value of behavioral data. The key is that demographic and attitudinal data has limited utility for purposes of predicting behavior. People do what they do, not what they say they’ll do, or what people who look like them do.

I used to run e-commerce for a series of mass market brands, attracting many tens of millions of unique visitors each year who spent well over $1 billion during my tenure there (a pretty significant sample ;-). We segmented attitudinal data and demographic data across a number of behaviors and found:

1. The demographic attributes of the folks who purchased looked just like the demographics of the folks who didn’t.

2. The attitudes of those that purchased varied slightly from those that didn’t. The main difference was that those who purchased more frequently tended to have a more favorable impression of the brand, though which was cause and which was effect, we were never able to say with confidence.

Juan, I do think that attitudinal data and demographic data may help you determine which types of sales and marketing channels provide you the greatest benefit (i.e., you might have more success with TV advertising - for example - if your core demographic consists of older folks who like to watch a lot of TV). Otherwise, nothing in my experience tells you what you can expect customers to do better than what those customers have done in the past. Good thing we’re starting to get tools that allow us to know what that is and do something with it.

Keep up the great work, Avinash and good luck on your rotten apple picking!

I can imagine that a Woman’s lingerie website could benefit from knowing that a website visitor is Female or Male (the latter have no idea what they are doing on a Woman’s lingerie site so they can be charged high prices and sold anything!).

But for most websites my humble experience reflects Jim’s slide, behavioral data is a ton more actionable than demographic. And of course in context.

That last part is important. I am a much better fan of the “Google philosophy”: What I know about you (behavior) from the immediate past (say the last few days) is a better predictor of what you want now. I might have your history from last year or from when you were born, but that is less of a predictor of what you want now. Maybe some, but the most recent history is most delightful.

Thanks for your thought provoking comment, please keep ‘em coming.

-Avinash.
PS: Tim : Your quote brought a smile, it is fantastic. Thank you for sharing, I am going to use it in my presentations and am off to look for pictures of rotten apples! :)

thanks for the Awesome stuff you shared with us,

Very true….

Accuracy vs. Precision…..

Ready than taking your dollars and cents and just spreading your fire and doesn’t really hit the precise point.

More please…haha

Great. As always.

A good friend of mine got me hooked on precision a few years back with the following folksy quote:

“Apples are apples. It doesn’t matter if your apples are rotten as long as you’re comparing ‘em to other rotten apples.”

A combination of nitpicking and an extension of the concept with regards to application of predictive analytics. I FULLY realize that I’m about to make a point that is out of the context of the full presentation, so, Neil, if you read this, realize that I *suspect* you might have covered this in the delivery of the presentation. BUT…

It strikes me as odd that none of the factors driving audience size was directly related to the content of the presentation: Content Relevance or Content Freshness or Topic Hotness. If someone presented “Pictures of My Hawaiian Vacation” at eMetrics…the lack of relevance would dwarf the other factors.

Now, that’s saying that there can be factors that are subjectively measured that need to be taken into account. And, it may be that, given that the topic wouldn’t be accepted if it wasn’t reasonably current/pertinent, it may be that that’s a non-significant variable when it comes to a practical application of the model. Still, it’s very much a lever that can be controlled.

So…the extension of the idea that comes from this nitpicking is that, when sitting down to list out the levers, sometimes it makes sense to list levers that you *cannot* easily quantify or that you cannot necessarily influence (the strength of the economy, for instance). That allows you to consider whether you’re building a predictive model that is largely irrelevant or not — either through logic (all eMetrics presentations are relevant) or testing (is there a correlation between the economy and my dependent variable that is so strong and highly weighted that I’m not likely to be able to build a strong/meaningful model without it).

Hats off to Jim - I am in complete agreement with his thoughts on accuracy vs precision. As you point out, it is a changing world and going for the “deterministic” measurement approach can be fatal in the marketplace (IMHO the go-to-market speed is more critical than accuracy). I was sitting and chewing on this and remembered something from my high-school physics - Heisenberg’s Uncertainty Principle. To have some fun, I thought I will have twist on it to reflect Jim’s theory :-)) — The more time you spend on increasing the “ACCURACY” (zero variance) of a metric, the less time you will have to make it “PRECISE” (consistent and repeatable) and vice-versa.

On the predictive analytics - well,coming from the world of traditional analytics and modeling I am for sure biased and will vote for Neil all the time:-). The only comment I want to add is this — as you and Neil point out, identifying the key levers or variables that impact the outcome is the important thing. I have seen folks not spend enough time on this task and then use fancy tool/tech like neural networks or genetic algorithms to get “good” results”. This is a waste of time — the technology can deliver an output quality only as good as the input quality.

Ciao,
Ned

The feeder from Applied Insights blog is broken. See link below. Avinash, could you help address this?

http://www.applied-insights.co.uk/news/category/blog/feed/

Re: Accuracy vs. Precision, if you are having trouble getting “traction” with management, please consider this: Management is in the business of Forecasting, making Precision more important to them than Accuracy.

A measurement approach that yields Consistent, Repeatable results rather than “Absolute Accuracy” may be in your best interest.

Typically, this means not drilling down so deeply. Same data, just not drilled down to the level where noise becomes very loud and signal weak!

According to me, Acquisition strategy optimization and cart/checkout complexity are the two stand-out points for predictive analysis of Conversion.

Your humble attempt to guide us in this respect is praiseworthy.

Keep doing it
Ankur P Mody