A 2–6 week sprint that hands you a defensible thesis pre-deal — or a costed, sequenced 100-day plan post-close.
Built for
- PE deal teams in IOI → SPA
- Operating partners on day 1 of ownership
- Boards stress-testing a growth story
The mandate
Quantify the real growth engine behind the deck. Pressure-test channel economics, martech debt, data integrity and team capability. Translate findings into EBITDA levers a CFO can model and an operator can run.
Scope of work
Growth engine diagnostic
- Channel-level CAC, LTV and contribution margin reconstruction
- Paid media account forensics — wasted spend, attribution drift, brand vs non-brand split
- Organic, CRM and lifecycle contribution to revenue
- Sales/marketing handover and pipeline conversion economics
Martech & data audit
- Stack map, contracts, renewal dates and overlap
- Tracking, consent, server-side tagging and data quality scoring
- CRM / CDP / warehouse architecture and integration debt
- AI / agentic readiness — where automation removes cost, not just noise
Team, vendors & cost-to-serve
- Org design vs growth ambition — gaps, duplication, key-person risk
- Agency roster review and commercial terms benchmarking
- Run-rate cost-to-serve across marketing P&L
Timeline
- Week 0Data room access, NDA, stakeholder list
- Week 1Account forensics, stack map, management interviews
- Week 2–3Findings synthesis, leakage quantification, lever shortlist
- Week 4Board read-out and signed-off 100-day plan
What good looks like
EBITDA levers identified
Typically 3–6 levers worth 150–400 bps
Wasted media flagged
Common range: 12–28% of paid spend
Decision in hand
Bid, walk away, or buy with a costed plan
Deliverables
- →Red / amber / green diligence findings deck (board-ready)
- →Quantified leakage register with size-of-prize per lever
- →100-day value-creation plan with owners, sequencing & investment ask
- →Re-baselined marketing P&L and KPI tree
You walk away with
Diligence deckLeakage register (xlsx)100-day planKPI tree
Not for
A rubber-stamp on a thesis you've already committed to. Findings land where the data lands.