PE portfolio SaaS optimization: funnel fixes, pricing clarity, and support automation that drove 7× revenue growth.
Project Summary
Grindstone Capital portfolio companies ran several B2B SaaS products with feature depth but fuzzy paths to first value. The work was find friction, fix it, measure it, repeat.
My Role
Product and UX strategist across portfolio companies: audits, flow redesigns, team structure, and measurement dashboards tied to revenue.
The Challenge
Each product had different leakage points, but the pattern was the same: users never reached a clear first win. We had to show impact in quarters, not years.
My Mandate
Find where revenue leaked in onboarding, pricing, and activation. Fix those paths first. Tie every change to a number the PE team could track.
Find where users stalled
I started with session replays and funnel cuts on each product. Onboarding drop-off clustered in the same places: too many decisions before first value, pricing pages that listed features instead of outcomes, and activation steps that assumed power-user patience. We wrote one paragraph per product: where users quit, what job they came for, and what proof we had that the fix would move revenue. No roadmap until those three lines were clear.
Fix the first ten minutes
The biggest lifts came from shortening paths to first value. We cut onboarding steps, prefilled defaults where data allowed, and rewrote pricing around the job the buyer already named in sales calls. On one product, activation moved when we surfaced a sample output instead of an empty dashboard. On another, pricing conversion moved when we dropped a tier nobody selected and named the middle plan after the buyer segment that actually closed.
Automate support that ate the team
For a marketing platform build, we routed tickets through intent classification and self-service flows before they hit humans. Response time dropped roughly 90%. Automated resolution cleared 80%+ of routine requests so the team could focus on billing edge cases and integration bugs. We prototyped routing rules with real ticket samples, released weekly, and measured deflection rate instead of model accuracy alone.
Measure what the firm funded
Every change linked to activation rate, pricing conversion, churn, or support cost. I built simple dashboards the partners could read without a product translator. Portfolio revenue moved 7× over the engagement. Most of that traceable lift sat in onboarding, pricing, and support cost, not net-new feature launches.
Outcome
Portfolio revenue grew 7×. Onboarding completion, pricing conversion, and activation improved across companies. AI-assisted support cleared 80%+ of routine tickets and cut response time roughly 90% on the platform we built.
Closing Insight
Seven-x sounds dramatic until you remember it was mostly fixing the first ten minutes of the product.