First and foremost:
Sentiment Analysis, Anyone? is the continuation of an ongoing push to bypass all the pain and suffering ahead of us on the social analytics front and move straight onto the good stuff. I want to avoid a lost decade scenario, and just bypass the trough in the Gartner Hype Cycle. It’s a lot to ask for, I know, but please – could we just make the decision this time to jump to the good stuff?
It’s worth a read and it lays out a very specific challenge.
Next is this theme of the “Power of Weak Ties”. There’s an early paper (1954 I think) on Word of Mouth marketing which proves a strong tie between two individuals in a homogenous group is less likely to produce successful referral behavior than two individuals who belong to two seperate heterogenous groups who are linked by a weak tie. A presentation of an unfinished paper yesterday by Christian Barrot confirmed that finding.
So think about it: In the Web Analytics community, if somebody were to reccomend a web analytics product to you, and they’re just like you – a member of the web analytics community, you are less likely to follow up on that referral with a purchase than if you were member of the data mining community. Why might that be? Well, if somebody comes at me and says “product X is absolutely awesome”, and I look at them and say, “wtf are you talking about, product X < Y because of A, B, and C" - well, two experts are unlikely to agree. If, on the other hand, you’re a data miner, and you know nothing of Product X, and you don’t even know about Y, Z, or W: you effectively don’t know what you don’t know – and you happen to know a web analyst, and that web analyst tells you Product X – you’re that much more likely to buy Product X. I’m certain that there are counter-examples. I know the importance of brand dominance for early adopters in a self-referential social network: but why the two theories are colliding – I don’t know. But carrying that ‘weak tie’ reference going and applying it to the INFORMS Marketing Science Conference here in Ann Arbor: Much of the work, perhaps 95% of it, is really quite good. It’s applicable to what Web Analysts and this emerging discipline of Social Analysts are tripping over. They don’t speak our language. They have their own world here, replete with hierarchies of authority, social networks, sheeple, and hidden colleges. They have their own jargon and their own biases. It’s their own culture. Every community has one. Whether they want to admit or not. So are there major differences between the Web Analytics Community and the Marketing Science Community?
- The goal of the Marketing Science academic is to publish in one of the big journals.
- The goal of a Web Anaytics Practitioner is generally to prove value.
- A Marketing Science academic is focused on answering one specific question effectively.
- The Web Analytics Practitioner is focused on answering every damn question efficiently.
- A Marketing Science academic focuses on the method and the model.
- The Web Analytics Practitioner is focused on the toolset. (STILL!)
- A Marketing Science academic is likely to troll another based on the elegance of a model.
- A Web Analytics Practitioner is likely to troll another based on validity of a conclusion or the accuracy of a tool (STILL!).
- A Marketing Science academic will gloat about the size of his dataset.
- A Web Analytics Practitioner will gloat about what new media s/he is trying to measure now (the EXPANSION of the dataset).
- A Marketing Science academic will casually mention they have a Monte Carlo sim running for them at home, and that they’ll check it when they get back.
- A Web Analytics Practitioner will casually mention they are running a VOC on one of their sites at work, and that they’ll check it when they get back. And they also have a copy of The SIMS 3 running back home. And they’ll check that too.
One of the biggest, and ongoing challenges for me, as a weak tie here, will be translate a large volume of this material into a format that #wa and #waw people can use, debate, and I really hope, start to measure. These people in Marketing Science are languishing with very old datasets. Web Analysts languish with very young, and frequently, very large datasets.
I’m kind of dealing with a world of extremes here.
Same base subject material. Two different cultures.
So, we’ll give it the old college try and see what falls out.