Pain based scientific process of advancing revenue-generating programs from concept to fully integrated programs in a consistent, scalable way.
So you know your ideal customer. But you don’t know how to find them, you don’t have the time and enterprise-level data is irrelevant.
You need data for your ideal customers, but data from tools like Zoom is too expensive, out of date and the same data EVERYONE ELSE IS USING.
Sherlock inverts traditional lead research by first finding leads with the most amount of pain, filtering them by their potential value to you, and then communicating with them based on how much their pain is costing them. This leads to 22%+ meeting rates.
Let's cut to the chase, deals are done with the people in the room. And not the ones sending you their newsletter or leaving you a transactional email from their sales team.
I took a job building lists of ideal customers for an outbound agency, having never done it before. I quickly realized that enterprise data vendors, doing it manually or crawling the web myself was not the way.
Upon my initial research, I noticed that freelancers on sites like Upwork were claiming to reproduce the same data as data vendors, so I took a couple of calls and reverse-engineered their process.
I quickly figured out how to achieve an 8x outcome without dealing with individual freelancers, not paying platform fees or facing issues with infinitely scaling the process without sacrificing quality.
From all the time I saved on the first three steps, it allowed me to get great at identifying these companies at scale, figuring out buying intent inferences and making the data personalized to the personas I was going after.