the social and innovation ROI of online engagement


When it comes to social media ROI, everyone keeps talking about correlating votes and comments to sales. Totally important, I agree. And especially challenging when you are talking about most products and services, which are sold offline. That we’ll discuss in a future blog post.

For now, there are (at least) two additional aspects of ROI that are really important but not ever talked about. First, social ROI. The value of someone engaging actively on your online community has spillover effects on everyone else in the community as well as that individual’s broader network (especially when social network hooks like Facebook Connect broadcast the person’s activity back to their friends on Facebook).

Second is innovation ROI. Just look at MyStarbucksIdea and Dell Ideastorm, two examples of community forums which crowdsource ideation and feedback from customers. The company seeds the site with ideas for new products and features, and the community responds by voting ideas up or down, commenting, and even suggesting their own ideas. For FREE, customers help Starbucks and Dell come up with new ideas and prioritize where they should be investing. For example, as a result of a community idea, Dell started shipping hardware with Linux operating systems which has resulted in real revenue, not only from the person who came up with the idea and the people who voted on Ideastorm, but across the market and from attracting new customers.

Customer lifetime value can no longer just be viewed as the present value of future cash flows from a particular customer in isolation. The new calculation should be something like this:

New customer lifecycle value calculation

New customer lifecycle value calculation

* Obviously a very rough approximation, assumes there is little to no overlap between the individual’s network and the vendor community. Sales resulting from idea contribution can be defined as total revenue (or incremental increase in revenue) resulting from new products or features suggested by the community, divided by the number of participants in the community.

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