The customer had sent me a list of names he already had — informing me they’d be a nice addition to my “database.” The problem with that is 90% of his names were on LinkedIn.
I’m just not that interested.
Furthermore, several of his names were not only on LinkedIn — they were no longer at the respective companies he had them listed under, or their titles had changed; senior managers having moved on to principal or partner, which were of no interest because they were too high on the totem pole.
I don’t have time to chase phantoms.
I started calling in to the respective companies, pulling the senior managers and managers in tax. That’s how I know some of his listed names were not there (or that their titles had changed).
I ended up matching his names by about 150%. In other words, if he had five names at a company, I pulled eight more, most of whom were not on LinkedIn. Not only did I pull them, I pulled them knowing in real time that all of them were there, and their titles were current.
What was helpful to me was that his assembled names helped me collect the others.
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“Oh, Tom McKee’s no longer there? Has someone replaced him?” usually brings forth another name and sometimes that name brings forth others.
“While I’m asking, is there anyone else new there at your firm?” When asked in a straight-forward, matter-of-fact way to a receptive gatekeeper who has begun to give you information, just asking the question holds the possibility of a tumble of names coming forward.
I feel better these days about the fact that I never did keep a database; basically I’ve always felt the Internet was a good enough “database” to start any search. I very rarely, if ever (I can’t recall any!), have searches that resemble each other within close time-frames. So when I do, information in a database will likely have “aged.” It’s the same situation with names off (the database) LinkedIn.
I only see this problem (aged data) increasing as these online social databases age.
What do you think?