Category Archives: Analytics Education

Awareness versus Persuasion

Analytics Education, DataBase Marketing, Web Analytics

In the early days of Home Shopping Network (live TV, not online), we were doing some ethnographic research and started to find “physical clusters” of customers – neighbors or people who worked together. For example, one of these groups was nurses at hospitals, especially nurses who worked the night shift.

We looked for the most active member of the cluster (our “thought leader”) and asked them if they would help us with a “member get a member” program. Would they be willing to distribute discount coupons to their friends, especially ones who were not already customers? Time after time, the answer was:

“Honey, all my friends are already customers of yours”.

We launched the program anyway, because it was a pet project from upstairs – I was a junior marketer at that point so I couldn’t kill it ;) The program never, ever worked, no matter how hard we tried. It generated very few new customers while giving lots of discounts to people who were already active buyers. Basically, the cost of those discounts overwhelmed the value of the new customers generated.

Apparently a similar thing happens online with Social marketing.

As part of a DAA program that reviews academic research for DAA members, I was able to take a look at a paper titled: Firm-Created Word-of-Mouth Communication: Evidence from a Field Test by David Godes and Dina Mayzlin.

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eMetrics “ShootOuts” We’d Like to See

Analytical Culture, Analytics Education, Web Analytics

I was in Vancouver for a presentation to CAUCE [kay-yoose, thanks Raquel] and was able to grab a quick dinner with fellow WAA BaseCamp stakeholders Andrea Hadley, Raquel Collins, and Braden Hoeppner.  We’re rolling out a new 2-day format for BaseCamp and got to talking about web analytics education in general. 

We started talking audience segmentation and content at the eMetrics Summit, and specifically the “shootout” format from the old days.  You know, 10 vendors on the stage at the same time taking questions from the audience.  Those sessions were both educational and hilarious at the same time, as the vendors side-swiped each other on topics like accuracy, how visitors are counted, cookie structures, and so forth.

But that was back when the technology was in flux, and now that issue has settled down a lot.  Braden brought up the concept of returning the “shootout format”, but more on the business side.  You know, get some practitioners, vendors, and consultants up on stage and have them thrash out stuff like:

1.  Attribution – does it really make sense to even bother with attribution at the impression / click level when there is often not a strong correlation to profit?  I mean, just because someone sees or clicks on an ad does not mean the ad had a positive effect; in fact, it may have had a negative effect.  Why not go straight to action or profit attribution, instead of using creative accounting?

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Marketing Science (Journal)

Analytical Culture, Analytics Education, Marketing Research

As I said in the Heavy Lifting post, I think the Web Analytics community is becoming increasingly insular and should be paying more attention to what is going on outside the echo chamber in Marketing Measurement.  I also think the next major leaps forward in #wa are likely to come from examining best practices in other areas of Marketing Measurement and figuring out how they apply to the web.

For example, did you even know there is a peer-reviewed journal called Marketing Science, which calls itself “the premier journal focusing on empirical and theoretical quantitative research in marketing”?

Whoa, say what?

This journal is published by the Institute for Operations Research and the Management Sciences, and articles are the work of premiere researchers in visitor and customer behavior from the best known institutions around the world.  In case you didn’t know, “peer-reviewed” means a bunch of these researchers (not including the authors, of course) have to agree that what you say in your article is logical based on the data, and that any testing you carried out adhered to the most stringent protocols – sampling, stats, test construction, all of it.

And, most mind-blowing of all, they show you the actual math right in the article – the data, variables, formulas, graphs – that lead to the conclusions they formulate in the studies.  You know, like this:

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