Friday, January 21, 2011

Social Contagions and Social Media Marketing Effectiveness

A little background terminology here: things that transmit between nodes in a social network are known as contagions. The most simple real-life contagion example is, of course, diseases, but intuitively we also know that attitudes (eg. product preference) are influenced by who our friends are.

Further, as with disease, in order for contagions to effectively spread, they must be in an appropriate environment. In the case of diseases, not only must the shape of the network be appropriate, but varying degrees of physical contact (it might be as simple as a handshake, or intimate as sexual contact, and genetic predisposition) may be a factor. For product preference, as an example, the degree of susceptibility to a contagion is similarly nuanced. Of course, centrality and number of connections may be large factors, but there are others.

Companies are spending a lot of money promoting their products on Facebook. Estimates have Facebook's advertising revenue at ~$1.86B dollars for 2010 (not inconsequential vs. competitive "portals"). Much of the advertiser interest in advertising on Facebook stems from the belief that "social ads" are more effective than are non-social standard banner ads due to the influence our contacts have over us.



Much of the research by mainstream analysts (1, 2) frame the impact of social media in the form of "influence". This has an implication of a sort of cognitive awareness and formality by consumers; that they make their purchase decisions rationally based on friend's behaviors (eg. "ah, I see Jim has bought , so I will also get one.". Though there may be some contribution by rational decision making processes, I hypothesize that modeling influence as a contagion (eg disease) is a more effective way to measure impact. In other words, you can't control your desire for a product anymore so than you can control your ability to catch a cold.



Google is often criticized for the lack of transparency in their advertising marketplace. Advertisers don't know the publishers, targeting and ad rotation is opaque, and the true price is unclear. Facebook has a similar problem, but it's not as direct or obvious as Google's.

Here's the scenario: An advertiser creates a facebook page and buys ads to promote it. Nested in the ad is a "like" button that, when pressed, acts like other like buttons on the site: for some of your contacts, it inserts an item into their newsfeed. Here's the key problem: Facebook doesn't permit the advertiser visibility into who and where they surface these "likes". For brand advertisers, not all impressions are created equal.
* People are not monolithic influences or non-influencers. My mom might influence cold remedies, but not music taste, for example.

While tastes do signal social identity, what others infer from one’s choice depends upon group membership (Berger and Heath 2007; McCracken 1988; Muniz and O’Guinn 2001). For example, Berger and Heath (2007) find that people
may converge or diverge in their tastes based on how much their choice in a given context signals their social identity. [Do Friends Influence Purchases in a Social Network? Raghuram Iyengar]

* People that I have strong ties with, for some types of products, have already influenced me offline resulting in a wasted cost of an impression. For example, don't bother showing my closest friends that I liked "Against Me's" latest album, we all already have it. That said, if I liked a car brand, it's probably worth showing them my "Like".

With Facebook, as with google, this targeting is done algorithmically. You may get a lot of impressions, and people may say that they're influenced by social media, but are they actually being influenced?

Some further, research from Harvard Business School talks about how relative social standing may paradoxically reduce influence:
Our results show that there are three distinct groups of users with very different behavior.
The low-status group (48% of users) are not well connected, show limited interaction with other members and are unaffected by social pressure. The middle-status group (40% users) is moderately connected, show reasonable non-purchase activity on the site and have a strong and positive effect due to friends’ purchases. In other words, this group exhibits “keeping up with the Joneses” behavior. On average, their revenue increases by 5% due to this social influence. The high-status group (12% users) is well connected and very active on the site, and shows a significant negative effect due to friends’ purchases. In other words, this group differentiates itself from others by lowering their purchase and strongly pursuing non-purchase related activities. This social influence leads to almost 14% drop in the revenue of this group. We discuss the theoretical and managerial implications of our results.

This is consistent with what's known as the middle status conformity thesis. Detailed here (Philips and Zuckerman 2001). Not to put to fine a point on this implication, but if 48% of the population is a "low-class", and these people are not influenced socially, then social advertising to them is ineffective.

Other background reading:
* Impact of Social Influence in E-Commerce Decision Making
* Distinguishing between Drivers of Social Contagion: Insights from Combining Social Network and Co-location Data

3 comments:

Sanjay said...

Fascinating post. I generally agree with the theme and direction.

I do have three counter-arguments

1- The fact that facebook is attracting significant dollars vs portals could be because previously effective portals have become ineffective due to audience erosion and/or portal consolidation. This is a minor point.

2- "you can't control your desire for a product anymore so than you can control your ability to catch a cold" - I think you do. Historically, advertising has been about identifying these resistance elements and removing them. In that spirit, what will be interesting (and I think you are suggesting a similar modeling path) is to identify connectors in the social graph that are far more effective than connectors that are least effective in removing the resistance to product purchase. You might find this paper on sensitivity analysis of method of propagation of West Nile Virus interesting. While most research in disease contagion is focussed on how to squelch the effectiveness of propagation, advertising will need to figure how to amplify the effectiveness :-)

3- It might be because the research/background reading is NA focussed, but I think there is a case to be made for regional differences in influence effectiveness. For instance, in a culture where family nodes are 'thick', social strata might not be the delineating factor.

That said, great read for a Sunday evening.

Bryan Hurren said...

@Saksobi - thanks for the note. On #2, see what I just posted. Interesting that, in some contexts, co-location is more of a determinant than is network connectivity. On #3, the paper was based on Cyworld (Korean) data. :)

Sanjay said...

Haha - I did not notice it was Cyworld.