Pipeline is inconsistent
Good months followed by dry ones, with no clear lever to pull.
Growth Systems
Growth strategy, experimentation, analytics, and automation for SaaS and technical teams that need clearer systems and stronger execution.
The Problem
The issue usually is not effort. It is the absence of a clear operating system for demand, qualification, measurement, and execution.
Good months followed by dry ones, with no clear lever to pull.
Sales and marketing have different answers when you ask who the best customer is.
You know blended CAC but not by segment, channel, or cohort.
The best deals still come from founder relationships, not a repeatable system.
How I Work
Each engagement follows a deliberate sequence. Discovery grounds the strategy. The strategy defines what to measure. Implementation stays purposeful. Knowledge transfer locks in the gains.
01
Strong hypotheses come from understanding the current system: where signal is lost, which tools are trusted, and where the team is compensating manually.
02
A clear measurement model has to exist before experimentation begins. Goals, KPIs, ownership, and decision thresholds must be agreed before testing.
03
Automation and optimization are useful only when they improve execution quality, influence revenue, and reduce employee workload.
04
Knowledge transfer works when the team can operate the system without the consultant. Reusable documentation and standards make the implementation easier to maintain.
Selected case studies showing how strategy became measurable execution.
Led growth marketing across a technical multi-product SaaS portfolio, learning how product complexity, internal systems, web ownership, technical debt, and cross-functional execution shape scalable growth.
Frameworks and working notes on how growth systems are designed, measured, and run.
A structured approach to testing whether messaging resonates with target audiences before scaling.
A structured approach to organizing growth accountability across strategy, execution, and measurement.
Observations on common friction points in analytics infrastructure that slow down decision-making.
Share the challenge, current constraints, and timing. We can assess fit quickly and define the next move.