Dr. Michael Wu, Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and online communities.
He's a regular blogger on the Lithosphere and previously wrote in the Analytic Science blog.
You can follow him on Twitter at mich8elwu.
Last week I presented a webcast with Prof Barak Libai and Sanjay Dholakia on the Holy Grail of WOM marketing. A central theme of the webcast is the importance of influencers, because they are essential in driving the success and effectiveness of WOM in online communities. And in terms of ROI, seeding a WOM program with influencers is 45% more effective than seeding randomly.
I presented one way of identifying influencers using Google's PageRankTM algorithm. But PageRank is only one of many social network metrics that we may use to score community members; we can do much more. Although PageRank may sound like something novel in the hi-tech world, it is actually a variant of the eigenvector centrality, which has been used in social network analysis (SNA) since 1970s. Let me briefly discuss the subject of social network analysis and what it means to markers.
As alluded to earlier, SNA provides numerous centrality measures that quantify the importance of individuals in a network. There are four centrality measures that are popularly used in SNA and each quantifies importance in a different way.
Depending on the desired result and the resource constraint of your marketing campaign, the optimal selection criteria for seeding your WOM program will differ.
If you simply want to spread your message to as many people as possible, you want to seed your program with members that have high degree centrality. On the other hand, if you want to spread your message as fast as possible (for example, if you want to reach 100K people in the shortest amount of time), then you will need to target members with high closeness centrality. If you care about conversion, and you must choose influencer that are reputable. In that case, you should pick members who have high eigenvector centrality. Finally, if your resource is very limited, (say you can only give away one car for someone to test drive) then you need to find the members that has the highest betweenness centrality.
Therefore, influencers are not created equal! But each type has its strength. And it is crucial to understand that influencers who can spread your message to the most people are not necessarily the same one who can do it fastest. Influencers that have the most friends are not necessarily the most reputable. Although in practice these centrality measures are somewhat correlated, understanding the science behind influencer selection and when to use which criteria will maximize the ROI of your WOM programs.
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