Quantifying the Performance of Brand Partnerships

Last week we released Market Intel for Twitter. This product lets you sort and filter the followers of any public Twitter account. We’ve seen people use Market Intel for everything ranging from customer acquisition and targeted advertising to market research and recruiting.

One of the features we released with Market Intel is a new filter called “Compare to Another Account.”


This filter lets you compare two accounts and see the overlap or difference of their followers. Who follows both Audi and Mercedes? Who follows Audi, but not Mercedes? Who follows Mercedes, but not Audi?

The obvious application here is to build stronger tailored audiences for your advertising campaigns. But over the past several months, we’ve discovered another intriguing use case for this filter: quantifying the performance of cross-promotion.


What does that mean? Let’s say two brands are partnering together — they agree to post about each other on their respective Twitter accounts.

Before these posts go out, the brands track the overlap and difference in their followers. When the promotion ends, they check the overlap and difference again. In particular, these brands can use this data to ask the following questions:

  • Did the overlap increase substantially?
  • Did the difference increase substantially?
  • Which brand contributed most to any increase in the overlap?

If the overlap in followers increased a lot, this is great news. People who previously followed just one of the brands (or neither of the brands) now follow both of them. This partnership might be one continuing for the long-term (or at the very least, it might make sense to partner again on another cross-promotion). However, there’s the possibility that one brand contributed disproportionately to this increased overlap.

With this new filter, you can effectively quantify the performance of these types of relationships.


Wearer of many hats at SocialRank. @AmmarM08

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