A recent paper funded by an NSF and Microsoft grant, distinguishes relative strength between friends in Social Networks. While the conclusions are preliminary and speculative, the approach offers a unique method of valuing certain friendships over others, within private networks.

Where traditional and interactive advertising considers viewer as having similar propensity for action, given their demographic profile, Tie-Strength looks at the relationship between users as a source of influence.

The traditional model has proven to be less effective for "In-Network" relationships, because users often come from dramatically different demographic segments, and value each other’s content on a varying scale. They further may have categorically different friends, going from close "real-life" friends, to people they keep in the list to keep their numbers up.
 

Key Insights:

The study evaluated a number of network attributes and discovered that the relative tie-strength between users directly related to the likelihood the level of influence each other’s messages carried.

As you can imagine, duration of communication, rich content, reciprocation and mutual friends all were factors on trust worthiness and influence.

Regression coefficient Interpretation:

Simple Recommendations or More?

Initially, the information has sparked conversation about recommendation engines within networks, but there's a twist on the statistics for marketers and advertisers.

Knowing that interruption models are inherently flawed when dealing within a network (although IN and OUT-OF network assets still perform best in concert) message response is strongest when it comes from a trusted and influential source.

Clearly you're more likely to listen to your best friend of 10 years about a subject than that guy you had a meeting with a few months back. Advertisers and Marketers can leverage that dynamic to position the strongest message between the most influential pairing of users.

To date, there's been little to no metric dealing with influence level at a granular level. Using the "Tie Strength" model, and the emerging API's from Social Media players, it’s possible to create a model that targets calls to action between users with the strongest ties; the same ties that govern influence and response. Valuating the invitation and response rates may in turn help marketers discover a next-generation ROI model that has eluded most Social Media advertisers to date.
 

At a minimum the models suggest that brands using Network tools must pay attention to key aspects of communication; Photographs, video, communication duration, reciprocation and mutual adherence are the difference between an Ad and an Experience.

"It is not so much our friends' help that helps us, as the confidence of their help." - Epicurus circa 341-271BC

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