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Archive for September 22nd, 2008

Social Media blogger extraordinaire Shel Israel interviewed my friend and Marketing 2.0 co-blogger Francois Gossieaux earier this month about the Tribalization of Business study he and Beeline Labs conducted earlier this year that looked into the way that companies incorporate communities into their business model.

The basis of the Tribalization of Business study:

We wanted to understand how companies leverage communities as part of their business processes and how they measure the progress and success of those efforts.

We quickly realized that for those companies who were doing it right we were looking at something that was transformational. We were tapping into an age-old human behavior, which we came to recognize as “tribalism.” Halfway through the project, we changed the title because of that observation.

The interview is fantastic, but I find these portions particularly important to the discussion:

What do you think makes us tribal by nature and why should a business strategist care?

People want to hang out with like-minded people and want to help and be helped by people who care. By providing a massive platform for participation, social media has allowed that tribal behavior to return to the forefront. Whether you like it or not, there is probably a good chance that your consumer tribe already hangs out in some corner of the online world. While at times a bit dense, you can find a collection on the most recent research Consumer Tribes.

Your survey showed the five most frequent goals of a corporate online community were close to tied: (1)insight, (2)idea generation, (3)loyalty, (4)word-of-mouth and (5)marketing. Did you find communities do better when they serve multiple purposes or a single purpose?

Communities can start out with a single purpose, but inevitably, they will end up serving multiple purposes. You need to prepare for that. If you start a customer support community, for example, people will eventually give you new product ideas. If you are not set up to execute against those product or service suggestions that the community finds important, they will lose interest and leave – it’s as if you are not listening to them. They don’t care what your internal goals are for the community. They care about having a better complete life-cycle experience with your product.

Your study seems to indicate that engagement is a more valid goal of an online community than say, revenue per customer. How would you measure either?

I am not sure that we found engagement to be a more valid goal of an online community, but it is what many companies try to measure. I assume that much of the reason why companies are looking at engagement as a success metric is because many of them are building their communities in partnership with their agencies.

What we did find is that those companies who were most satisfied with their community efforts were those who measured the effectiveness of their communities in the same way as they would measure the effectiveness of the business processes that the community was intended to support. For example, if you measure the success of your customer support call center in a certain way, then measure the impact of your online community-based support program in the same way.

The same is true for new product innovation-focused communities or co-marketing communities. Whether the original measurement framework is the right one or not, it is one that the department heads understands and which tends to be institutionalized across the company.

It was amazing to see companies, who normally measure all their marketing programs based on increased sales, all of sudden measure community efforts based on page views and time spent on the site – even when the community interactions were happening mostly through email and text messages. These are all clearly signs of an early market with lots of customer confusion.

Read the entire interview here.

Additional reading:

ROI and the scalability of social media.

Online Tribalism + The Future of Social Media.

photo credit: ecowordly.com

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Excellent article from Bob Garfield in AdAge on the subject of data mining + inspired analysis = serious monetization. (Hat tip to Scott Templar.)

Here’s a taste:

Volunteered data, priceless as it is, nonetheless takes a marketer only so far. To create a genuine bond, an intimate relationship, requires a thorough understanding of consumer behavior, consumer interests, consumer sentiments, consumer moods, consumer movements and so on — not the sort of information that you can put in a sign-up form, even if anybody were patient or generous or honest or self-aware enough to part with it. This requires what Sherlock Holmes called deduction. Also a bit of extrapolation, inference, intuition, divination, prediction and imputation. Or, put another way, guesswork.

[…]

“Let’s say you’d been on eBay three days ago and searched for a particular term,” [Matt] Ackley [ VP-net marketing at eBay] says. “We store that in the user’s cookie, so when we see that user out on the web again and we’re serving an ad — on Yahoo Mail, for instance — we’ll see that cookie. What we then do is pass that information to our banner ad. Now our banner ad is not a banner ad at all; it’s a miniature application. And what it does is then goes to eBay and finds items that are like that keyword and pools them into the banner ad.”

But beyond ad optimization, there is so much more going on. For instance, eBay algorithms intuit gender from the user’s first name and age from the shopping categories chosen.

“We know young people buy iPods and older people buy Longaberger baskets. This is the type of information you can tease out. Well, if you know somebody’s age and somebody’s gender and what kind of categories they’re active in, you can more or less predict what they might be looking for next.”

[…]

“We no longer have to rely on old cultural prophecies as to who is the right consumer for the right message. It no longer has to be microsample-based [à la Nielsen or Simmons]. We now have [total-population] data, and that changes everything. With [those] data, you can know essentially everything. You can find out all the things that are nonintuitive or counterintuitive that are excellent predictors. … There’s a lot of power in that.” [Dave Morgan, founder of Tacoda, the behavioral-marketing firm sold to AOL in 2007 for a reported $275 million.]

[…]

Reed Hastings, founder and CEO of Netflix, [describes] not only his company’s methods but also the essence of collaborative filtering, which is one of the “ABCs of predictive marketing.” B is behavioral: tracking your path online. C is contextual: paying attention to keywords, and A is associative: divining your tastes and interests based on patterns established by people like you.

If Netflix can figure out I admire “Manhattan,” “Strictly Ballroom,” “Happiness,” “The Girl in the Café” and “Downfall,” how badly can “Rabbit Proof Fence” and “Fitzcarraldo” disappoint me? The consequence is a great boon to me: easier selection process, fewer duds. It’s an obvious boon to Netflix, which had 239,000 subscribers when Cinematch was launched vs. 8.4 million today. And it is a veritable godsend to the movie industry — not to the Hollywood-studio part of the industry; “Spider-Man 17” or whatever will do just fine on its own. Netflix’s impact is on cinema’s everything else, the so-called Long Tail of moviemaking.

The Long Tail is the coinage of Wired Editor Chris Anderson, whose seminal 2004 magazine article on the subject yielded an ongoing blog about it, which in turn yielded a best-selling 2006 book about it — the “it” being how digital technology has ended the near-monopoly on distribution enjoyed since the Industrial Revolution by mainstream blockbusters at the expense of niche goods and services. The fat head of the Long Tail is “Spider-Man.” Way, way, wayyyy down in the skinny middle is “Fitzcarraldo.” But now I can rent them both in one click.

[…]

Compare prefiltering, then, to post-filtering — collaborative filtering — which, with the ultimate benefit of hindsight (it operates only in hindsight), knows everything. This is especially useful in a digital, Long Tail universe of seemingly infinite choices. Like my friends, former Soviet refugees, who walked into their first American supermarket and burst into tears, we are easily overwhelmed by the astonishing array of items on the internet’s virtual shelves. This phenomenon is often described as “information overload,” but Clay Shirky, author of “Here Comes Everybody” and professor of new media at New York University, says that’s not quite right. We suffer, he says, “not from information overload but filtering failure. The minute people are exposed to reality, they freak out. What collaborative filtering does is replace categorizations with preference.”

To truly appreciate the article in its full glory and start divining ways to apply some of the lessons learned here to your own business, go here.

Have a great Monday!

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