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How I applied machine learning to my real-time network and built $600k of warm pipeline in 5 minutes
Sounds like a dream, right? We all know the struggles of getting a foot in the door into our best prospects. There is so much literature on how to build the best enterprise sales teams, or how to use account-based marketing tactics to attack strategic accounts. But there’s one thing that everyone is forgetting: how are you tapping your real-time network for introductions? Your network of colleagues, management, advisors and investors – what warm connections do they have that you don’t know exist? How can you use those connections to get into your top accounts?
That’s exactly what I did. I made a list of my top 250 prospects. In my case, these were primarily investment firms. After that, I used Affinity’s machine learning platform to mine my investors’ and advisors’ real-time networks with 3 clicks. And voila, in front of me were 120+ introduction paths that I could immediately act on out of the 250 listed doors. No cold emails. No frustration from unanswered emails.
You are probably thinking – what’s the “real-time” network? Let me explain. At Affinity, our thesis is that the real-time network of any individual lies in their communications. Who do they talk to? How often do they talk to them? What are their response times with different people? By intelligently analyzing email and calendar data, Affinity can instantly compute a relationship score for each relationship that you have sitting in your inbox. All your team has to do to set this up is sign into Affinity with 1 click with their corresponding Gmail or Exchange account. Affinity then lets you easily and selectively share these relationship scores with the rest of your team, organization, network – allowing you to leverage their network and vice-versa. In my case, I leveraged my investors.
We want to bring Affinity’s AI technology to every team that spends hours prospecting their way into their top accounts. If you want to use this latent real-time information that already exists in your trusted network, please drop us a line at firstname.lastname@example.org.