Platt Perspective on Business and Technology

Monetizing social networks and the valuation of social media connectivity – 5: influence scores 2

Posted in macroeconomics, social networking and business by Timothy Platt on February 8, 2012

The predictable, effective valuation of online connections is simultaneously one of online business’ most vexing challenges, and one of its most enticing goals. This is my fifth posting in a series on the issues that collectively comprise that challenge (see Macroeconomics and Business, postings 42-45 for parts 1-4), and my second to look into one of the newer and more promising, or at least hyped approaches currently in the market for achieving this: influence scores (see Part 4: influence scores 1.)

In Part 4 of this series I outlined some of the problems and limitations to using social media influence per se as a predictive metric for gauging marketing and sales potential. I add that the concept behind this approach does appear to offer real potential as a source of actionable marketing intelligence. How would this approach be developed in practice, to sidestep the one size fits all limitations that I have already discussed? My goal in this posting is to explore two avenues for doing that:

Stochastic and deterministic models and how probability enters into this type of marketplace model, and
• Influence scores as a tightly managed and focused component of sector-based marketing.

But before I address them, I want to go back to the basics and discuss what social networking and social media connectivity per se are in this context. And I begin by noting something that they are not, but that is frequently assumed to be true and by many if not most online participants.

• Social networking and social media connectivity are not necessarily about getting or staying in contact with friends and family. A social media influence score measures influence based on the ability to drive action in others. It is not a measure of how closely you are connected to anyone who you are measured as influencing, and it is certainly not a measure of friendship or of interpersonal relationship. For people who achieve high influence scores, most of the people they influence, they have never even heard of and it is likely that they never will, at least as individuals.

So I start out looking at social networking and social media connections, direct and indirect in this context as a system of potential marketing leads. For businesses that seek to tap into these systems, that is the only meaningful way to view them. And I give a specific example.

• If a current, high image-value celebrity – consider the pre- and early teenage girl idol Justin Bieber, suggests that he really likes girls who wear some specific brand of makeup, that will most likely prompt girls in the demographic he influences to buy that brand of makeup – even though he does not know and has never even heard of most of the girls he is connected to through Facebook, directly as well as indirectly, and even though he will never see them, with or without that makeup on.

Note that this is a highly focused, demographic-specific example of where influence scores would hold marketing value, and an example within that of the well established practice of developing celebrity endorsements – marketing using high influence score participants before it was called an influence score.

• Marketing, and marketing that is directed in a meaningful way towards specific targeted demographics is all about probability, and about increasing the probability that members of a targeted group will favorably hear about a product or service, and act upon that knowledge by making a purchase.

This is not deterministic or even just near-deterministic, setting out to specifically sell to particular individuals as through a completely individualized marketing campaign and with an exquisitely personalized message. This is stochastic and seeks to simply improve the numbers at a demographics level. But this only works if it is organized and built around a detailed understanding of the market stratifications and groupings that are out there, and their behavioral dynamics.

• Influence score-based marketing cannot work if it is only done as a matter of indiscriminately marketing around people with high influence scores per se.
• Effective influence score-based marketing defines marketing strata and target groups in terms of specific influencers’ connectivity and influence reach.
• And that means selecting high influence score holders with care, and marketing to them as a demographic with care to enlist their support.

In this, influence score-based marketing is just another way of specifying and characterizing target groups for stratified marketing campaigns – and simply tapping into high influence spokespersons as per Klout or other scoring systems cannot consistently work.

• Think of effective influence score-based marketing as crowd sourced celebrity endorsement-based marketing.

And this brings me to my basic social networking taxonomy model and the strategies that highly connected and high influence networkers follow – and how that determines the monetizable value of their influence. I am going to turn to that in my next series installment. Meanwhile, you can find this and related postings at Macroeconomics and Business. You can also find this and related postings at Social Networking and Business.

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