Platt Perspective on Business and Technology

Monetizing social networks and the valuation of social media connectivity – 4: influence scores 1

Posted in macroeconomics, social networking and business by Timothy Platt on February 3, 2012

This is my fourth installment in a series on the valuation of social media and social networking connections (see Macroeconomics and Business, postings 42, 43 and 44 for parts 1-3.) So far I have examined a range of measures and metrics that are used and both from the perspective of social networkers and from the perspective of businesses that seek to tap into social media to do more business. In Part 3: connecting valuation approaches I began a process of examining possible correlations between the two, and with a goal of:

• More effectively articulating how businesses can meaningfully make use of social media,
• Knowing what returns they gain on any social media investments they make.

In Part 2: a diversity of provider-side visions I wrote in part about eyeball and sticky eyeball counts and about how simply counting the page visits does not in and of itself give you actionable insight into marketing or sales effectiveness – even when you know that the people with those eyeballs are staying on your business’ web site and looking around a while before clicking away – the sticky side of eyeball counts. In Part 3, towards the end I raised the issue of a more recent social media metric: the influence score and I said that I would turn to consider that innovation next. And I begin that discussion here by making a few basic observations and by raising a question that comes out of them.

• Businesses seek out social media and online behavior metrics that can be used in successfully predicting rates of customer engagement, and that can be used to more successfully completing sales transactions.
• They seek to be able to make valid demographics level predictions so they can develop more effective marketing and sales tools, targeting the right market-level audiences the right way, and ideally they would also gain more personalized and individualized insight as well that would help them to more effectively market to specific individuals who they come into contact with.
• Sticky eyeball metrics were developed as a richer and more insightful predictive measure for this than simple eyeball counts. Quite simply, it became clear, and early in this process of developing better online marketing tools, that not all page hits or page visitors are the same, and that businesses need to understand the range of types of visits and visitors and act accordingly.
• A more effective measure of social media and online connectivity metric would offer a great deal of nuanced information about the people with whom a business at least potentially would come into contact with, and both for their potential direct engagement with the business and for their impact on others who might make purchases there.
• How do you develop a social media or online influence measure so that it offers more actual, actionable insight than the older eyeball and sticky eyeball counts do?

A number of online marketing information providers specifically offer social media oriented influence score data. Two that come immediately to mind for me are Klout (and see how they score individuals) and PeerIndex (and see how they score individuals.)

These businesses and their competitors seek to provide information and insight on who has what level of voice and influence online, and both directly and through the networks of contacts they have impact upon. So according to their metrics:

• An individual (identified as A) with a widely followed Twitter feed, a Facebook profile with a huge range of friends listed, and with a great many wall postings coming from them, and other indicators of active online interaction – and as a hub of all of this activity, would be measured as having a high influence score.
• A second person (identified as B) with a Twitter account they do not post to very often, and that does not have many followers would have a much lower overall influence score.
• But what does an individual’s influence score per se, unexamined as to the nature of their message say about anything?

I would argue that undirected, general measures of influence per se tell you no more as a business marketer than eyeball and sticky eyeball metrics do. A very, and even extraordinarily high-influence score celebrity who never posts or comments, or connects online in any way that by content connects with your business, is not going to have impact upon it. A lower, and even much lower-influence score social media participant who does directly address your business and products in their online voice might in practice have a much greater overall impact upon you – and particularly if their online message is spread off-line too, and to people who could realistically be your customers. And here that influence could be positive or negative.

And to stress an important point here:

• Online influence score metrics might seek to measure and include amplification effect value where a social networker’s voice is retweeted or otherwise repeated through a network by others.
• But even online, tracking the spread of influence is only going to yield lower range numbers, and off-line reach is not going to be included at all. Amplification of influence through indirect connections is at best difficult to measure or even detect.

But for this discussion, and much more importantly:

• Generic, one size fits all influence scores offer no value,
• As the only influence that matters for a business that would use this data is the specific influence that has direct impact on the business or organization itself and on its marketing and sales.

I am going to discuss this point in greater detail 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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