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Influencers, imitators and predicting product success 7 September, 2006

Posted by Jay Ball in theory.
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Whether it’s tipping points, chasms or tornadoes, there’s always been a keen desire among marketers to be able to predict the trajectory of a new product. But, of course, doing so is tricky (to say the least). While describing what’s already happened is interesting (as the Tipping Point showed) applying that analysis to future scenarios is the real deal.

Well, in a new research paper, two academics from Wharton School, University of Pennsylvania, have developed a mathematical model that predicts the pace of new product adoption. Warning: the paper is not for the faint-hearted, it is written with the density of a small star. But the information within is pretty interesting.

Many of us in technology marketing understand the role that the ‘knowledgeable friend’ plays in a technology sale. These are the early adopters, those that others turn to for advice. These days they are also the ones who blog about their experiences, contribute to price-comparison sites and get involved in user forums. As such, they are an incredibly important audience for new product launches. This paper terms these people influentials and those that follow their advice imitators.

Christophe Van den Bulle, one of the authors, comments,

“With our model, managers can see what will happen to sales depending on the size and behaviour of each segment. Also, managers do not need to guess these unknown parameters; marketing analysts can estimate them from data about older but similar products using standard statistical software.”

While this sounds a little optimistic to me reading the paper, the theory is attractive.

Interestingly, the paper shows that the chasm theory is not the only option. The authors consider scenarios where adoption can dip between the early and later phases before surging forward. Also, adoption by influentials will not necessarily diminish rapidly after launch, so marketers may be missing a trick by swapping attention over to the mainstream imitators. And determining who is an influential and who is an imitator is not necessarily linear (ie it’s not down to who adopts early or late).

The authors go on to look at the two-step flow process in more detail as well as the technology adoption chasm. They also talk about scenarios where imitators only imitate influentials, where imitators only imitate other imitators and where the two mix. They then lose me with a series of impressive looking, completely incomprehensible equations.

As I say, come at this one with a large coffee and a dictionary but there’s some interesting food for thought in here – now if anyone knows a good statistician who can explain the rest to me…

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