- June 28, 2010
Back in May when our API was still in its infancy, Sean McDonald, founder of Jute Networks, requested access to the Trstrank data to explore the potential application of it on network relationship management. He created a proficient report and raised some pointed questions that some of our other datasets can now answer. We thought it prudent to showcase his work, not only because it’s just plain nifty, but also because it illustrates the exciting synergy of our calls and the particularly appetizing value of them to market researchers.
If you’re attempting to promote something on Twitter, it’s likely that you would want to focus on promoting it amongst the Twitter luminaries. Enter Trstrank, our exciting little measure of Twitter luminescence. Getting your product promoted by someone with a high Trstrank could potentially be marketing gold. The likelihood, however, of someone with a very high Trstrank nurturing your product’s visibility with a steady stream of cooing retweets is slim to, well, none. So how to know where to focus your evangelizing efforts?
Sean wondered the same thing when he set about to promote his report. He created the following visualization of an arbitrarily selected sample of his Twitter friends positioning himself in the center, companies in the inner circle, and contacts associated with those companies in the outer circle. Any contact or company with a Trstrank greater than five is designated by a blue dot; those with a Trstrank between two and five are designated by an orange dot. This gives a useful snapshot of who occupies a “strategic position” in his Twitter universe.
Sean hypothesized that the least likely to engage and retweet his report were both the most top-ranked and most bottom ranked. Eliminating those two tails would yield a swath of active users to target, the orange dots. Ten of Sean’s thirty sample contacts were orange dots. Of those ten users, Sean eliminated seven of them based on personal knowledge he had of them (i.e. he didn’t know them very well or knew they didn’t care about data and data visualization). This left him with three contacts to enlist in his promotional efforts. Sean’s strategy is very savvy, but requires some amount of personal familiarity with contacts, a luxury not every promoter has.
Fortunately, two of our newer API calls, can simulate Sean’s marketing method. Influencer Metrics will show you how likely a user is to retweet a post based on their tweeting history. Coupling Influencer Metrics with Trstrank would enable a promoter to identify not only the users most likely to engage, but also the most influential of those users. Throw Wordbag into the mix and a promoter could also discover if users in the active, influential target population have a potential interest in their product.
We would love reader feedback about our current API calls. How do you envision them working together? What other kind of calls would be of benefit to you? Let us know your ideas.