75% Trial Win Rate

A few months ago, I was… average.
At least when it came to qualifying deals.

Like most sellers, I was moving fast, excited to get to the demo, the trial, the close. But I didn’t have a clear qualification system. I wasn’t consistently separating high-likelihood wins from long-shot opportunities.

And I didn’t even realize how much that was costing me.

The Post That Changed Everything

I was scrolling LinkedIn when I stumbled across a post from Amanda Zhu Co-founder of Recall.

It introduced a simple but powerful idea: not all deals are created equal and your qualification process should reflect that.

This one post made me rethink how I qualify deals at Scratchpad.
We made a few tweaks, added AI, and suddenly our trial win rate jumped from 40% to 75%.

Here’s how.

The Framework: Qualify by Buckets, Act with Intention

We now leverage a version of Amanda’s qualification matrix tailored to our process.

1. Fast Close

• Problem Fit: They have a problem we solve and want to solve it now.

• Buying Readiness: Leadership is ready to invest.

• ICP Fit: They align with our ideal profile, tech stack, team size, structure, etc.

These get full resources. We prioritize them in every way.

2. Education

• Problem Fit: Right problem, but leadership hasn’t committed to solving it yet.

• Buying Readiness: There’s an internal champion, but no formal approval.

• ICP Fit: They look like a great fit if we can get buy-in.

We aim to move these to Fast Close.
We’ll run 1–2 learning sessions, then use AI to generate an executive summary they can take to leadership.
If we spark interest? Fast Close.
If not? Nurture.

3. Nurture

• Problem Fit: Some signs, but not urgent enough.

• Buying Readiness: No champion. No clear intent.

• ICP Fit: Still a good fit, just not now.

These are flagged for future follow-up.

4. Disqualify

• No Problem Fit or

• No ICP Fit

These get closed out early. No time wasted.

Where AI Comes In

After 1–2 conversations, we run each deal through our Scratchpad Deal Agent which runs a prompt that outputs where a deal falls in the matrix.

The Deal Agent leverages the following context to shed light in a way we humans can’t:

• Call transcripts

• Notes

• Salesforce fields

• Relevant files or docs

• emails (coming soon)

It then recommends which bucket the deal falls into, based on a tailored qualification matrix.

This isn’t replacing human intuition it’s sharpening it.

It gives us a second set of (very smart) eyes to pressure test where a deal stands and how we should progress it.

The Outcome: Clarity and Confidence

Today, when I look at our trial pipeline, I don’t feel guesswork.
I feel certainty.

Every trial in flight is a deal we believe we’ll win and that changes everything.
It changes how we show up. How we allocate time. How we close.

And the results speak for themselves.

Want to see V1 of the Qualification Matrix I built?

Premium subscribers can access it below.

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