Go-to-market strategy for a venture advisory

The problem

A venture fund had launched an advisory service for founders who could not raise. It was acquiring clients through cold outreach and in-person events, and it knew its fee structure, but it had almost no analytics on how the service was performing. I was brought in to work out why the economics were not working, which levers would fix them, and what to do first.

Because there was no performance data, the starting point had to be assumed. The model uses estimates of the current funnel and says so, and the first step of the roadmap is to replace them with real figures.

What I did

The diagnosis. There were four structural problems, not one. Cold outbound is the right channel for high-value advisory, but running it against a product worth about $300 of lifetime value is not. Associates were absorbing unfiltered inbound and spending time on calls that did not convert. With no standardised product, quality depended on which associate answered. With no trust infrastructure, every conversion relied on personal relationships that do not scale.

Four levers.

• Productise the intellectual property into a repeatable framework (an evidence library, a scoring rubric and a report generator), which removes variability between associates and enables higher lifetime value and automation.
• Build website authority around demonstrable expertise rather than generic advisory copy.
• Add an automated application and scoring form built on that framework, to filter weak leads, show value before the call and convert traffic from rejected founders.
• Add a minimal thought-leadership tier for inbound signal and trust.

What I delivered. A diagnosis document segmented by lead intent (founders seeking funding versus those ready for advice) and by stage, from pre-seed to Series A. A five-step sequenced roadmap with scoped costs and durations, The total build was about three months before all three levers were live. A financial model covering all four levers across 18 configurations, with every variable a toggle: lifetime value, content tier, outreach spend, how many rejected founders are captured, and the level of ongoing growth support.

Tools, and how they fit together

The model is built in Google Sheets, with customer acquisition cost and lifetime value (CAC and LTV) and multi-scenario forecasting. Alongside it I built an outreach pipeline tool in Python and Gradio, connected to Notion and the Gmail API, and iterated on the outreach email templates. I also carried out structured due diligence on early-stage companies and wrote a growth strategy memo.

Who I worked with

I worked as a freelance growth strategist with the fund's team.

Result

The engagement has ended. The model's output is a projection, not a realised result: in the optimised scenario (website authority on, application form on, a higher-value product, signal-only content and a fractional growth officer at $2K a month) it showed a blended customer acquisition cost of $284, lifetime value of $1,100, a ratio of 3.87 to 1, and a cash position of +$43,657 at month 24 before overhead. The model likely underestimates how many rejected founders could be captured.

Limits

These are model projections built on assumed starting figures, because the client had no analytics when I started. Every assumption is a toggle for that reason. No result from the engagement is claimed here.