GTM Foundry is brought to you by Spiky.ai, a video analysis platform for revenue teams. Their AI models add emotional context so you can better understand if your customers are getting excited…. or losing interest.
Unique signals
Every company has a unique signal they look for to prioritize the best accounts that have the highest chance of converting to revenue.
It optimizes for:
Right timing
Highest chance of pain
Highest chance of demand
Figuring out your unique ICP signal, and being able to find it, unlocks tremendous productivity for your prospecting and closing teams.
An example of this is something along the following lines...
An example
DevTraining, Inc. sells training software. The best time for them to reach out to an account is when a company is migrating from one programming language to a new one. This is because DevTraining, Inc. helps developers learn new languages. It's the right time with the highest chance that company will need DevTraining, Inc. training software.
These signals can get hyper specific and very hard to pick up at scale because that data doesn't sit in a database somewhere. Or, it would require A LOT of manual research.
How to find your signal
You can do this by examining the characteristics or situation the company was in when they first engaged, and then closed.
Were they hiring for specific roles?
Did they just a hire a new CRO within the last 3 months?
Were they moving to a different programming language?
Did they enter into a specific FedRAMP process? (A type of certification that government agencies require in order to do business.)
A # of developers download something from a GitHub repo.
Something changed on their website.
A specific software platform was adopted.
Construction company wins a bid.
The list goes on and you’d be surprised just how many I come across (or have never heard of) before at Clay.
Another way to look at it…
Why did the customer buy now?
What changed about that company’s situation at the time?
What caused that situation to be more painful than the status quo?
What would’ve been the consequence if they did nothing?
Signals could be from companies or people at those companies.
Cross referencing with your sales cycle
The next step is to correlate this data with the length of your sales cycle and ACV. For example, the signals should come from companies that:
Closed the fastest relative other deals.
And, had highest ACV
If you can nail this, then you’re finding the signal of company that has the highest chance of converting the fastest at the upper level of your ACV.
Good stuff. Thanks for sharing.