Fractional executives bring experience and judgment. But they still need a team, tools, and time to build infrastructure. ProductQuant delivers the infrastructure directly — operational in weeks, not quarters.
Fractional executives are the right choice for specific situations. It's worth being precise about what those are.
Fractional CMOs and VPs of Growth are experienced operators. But the fractional model has structural limitations that no amount of talent can overcome.
Most fractional engagements are 10-20 hours per week. That's enough for strategy and oversight — not enough to build analytics infrastructure, run experiments, and monitor competitive shifts simultaneously.
A fractional VP Growth can design the experiment roadmap. But without a growth engineer, data scientist, and designer, the roadmap stays a document. You're paying for strategy that requires additional headcount to activate.
Even experienced fractional executives need 4-8 weeks to understand your product, data, customers, and competitive landscape before they can make high-quality decisions. That's ramp time you're paying for.
| Fractional CMO / VP Growth | ProductQuant |
|---|---|
| Cost: $10K-$25K/mo for 10-20 hrs/week | Cost: $7K-$25K/mo for full-scope delivery |
| Time commitment: part-time, shared across clients | Dedicated engagement with defined deliverables |
| Ramp time: 4-8 weeks before meaningful output | Ramp time: Week 1 kickoff, Week 6 full system operational |
| Scope: strategy + oversight (execution requires additional team) | Scope: analytics, experiments, churn prediction, competitive intel — all delivered |
| When it ends: strategy docs and frameworks leave with them | When it ends: infrastructure, models, dashboards, and experiment backlog stay with you |
| Builds your team's capability over time | Builds your growth infrastructure immediately |
Event taxonomy redesign, decision-first dashboards, and a compliant analytics stack — built and running, not recommended in a strategy doc.
Statistically rigorous experiments with pre-registered hypotheses, power analysis, and definitive results. Running — not planned for when you hire a growth engineer.
ML model trained on your data with 50-85 behavioral features. CS gets a weekly at-risk list — not a quarterly slide deck.
Continuous monitoring across product, pricing, and positioning. Automated alerts, monthly reports, and sales battle cards — not a one-time landscape review.
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