A growth case stands or falls on whether the commercial engine creates qualified demand, converts it predictably, and does so at attractive unit economics. In diligence, you do not need a perfect CRM or a glossy enablement plan; you need evidence that the revenue machine works by segment and route‑to‑market, that capacity and coverage support the plan, and that forecast risk is understood and priced. This chapter provides a practical toolkit to interrogate the funnel, sales capacity, channels, and forecast hygiene. The outputs plug straight into your model: conversion rates by stage, cycle times, win rates against named competitors, required pipeline coverage, ramp assumptions, and discount impacts on realized price and retention.
Start from first principles. Define the buying journey your customers actually follow; map that journey to the target’s funnel stages; and measure where volume, value, or time is lost. Keep the analysis segmented (customer size, vertical, product family, channel, geography), because averages hide truths. Then connect funnel math to unit economics (pricing, discount leakage, CAC/payback) and to capacity (ramped reps, territory coverage, partner mindshare). The aim is not to produce a museum‑quality dashboard; it is to arrive at a short list of constraints to fix, levers to pull, and model inputs you can defend.
9.1 Sales Funnel Diagnostics – Step-by-Step Guide
Sales funnel diagnostics reveal whether growth is a capacity problem (not enough qualified pipe), a conversion problem (too much friction or weak differentiation), a velocity problem (deals age out), or a mix problem (wrong segments/channels). Follow this sequence. It assumes a two‑ to four‑week diligence sprint with clean‑team access to CRM and marketing systems; where access is limited, use the outside‑in substitutions noted.
1) Lock vocabulary, scope, and the “unit” of analysis
Before touching data, write and circulate a one‑page glossary. Fix:
- What counts as lead, MQL, SQL, opportunity, stage names, closed‑won, closed‑lost, expansion, renewal.
- The time window (e.g., last 6–8 quarters) and periodicity (monthly/quarterly).
- The unit to analyze (opportunity, account, order) and denominator for conversion (e.g., stage advances ÷ records that entered the prior stage, not “records currently sitting in stage”).
- Bookings vs. revenue conventions, especially if hardware/services or channel pass‑throughs exist.
- The segmentation from Chapter 6 (customer/job, size band, channel, region, product family). Freeze it for all cuts.
This step prevents definition drift—the fastest way to break credibility under time pressure.
2) Request the right data once (and only once)
Ask for raw, not just dashboards. Minimum viable export:
- Opportunities/opps: ID, account, product/SKU or family, segment tags, source (inbound/outbound/partner/product‑led), created date, every stage change (from, to, timestamp), owner, amount, currency, probability at each stage, expected close date, actual close date, status (won/lost/open), loss reason, primary competitor, discount at close or list vs. net (if captured).
- Leads/contacts: ID, source, created date, MQL/SQL dates, campaign, account match, persona/role, time‑to‑first‑touch, touches.
- Activities: emails/calls/meetings count by opp stage and by week; SLA metrics (time‑to‑first‑touch, follow‑up cadence).
- Forecast snapshots: weekly/biweekly commit/upside/best case by rep and region for the last 4–8 quarters.
- Channel/partner: sourced and influenced opps, partner type, tier, take rate, delist/list events.
- Pricing/discount: CPQ/quotes or deal‑desk extract with list components, discounts, approvals.
- Marketing automation/web: sessions, sign‑ups, form fills, PQL/MQL, campaign tags (to connect spend to pipe).
- Rep roster & quotas: ramp start dates, on‑target earnings (OTE), quota, territory/segment, attainment, active coverage.
If CRM access is thin, substitute with: pipeline review packs, partner portal reports, distributor line cards, win/loss notes, and invoices to reconstruct realized ASP and discount.
3) Rebuild the canonical stage map and baseline math
Do not trust stage probabilities until they are calibrated. Compute from raw histories:
- Stage‑to‑stage conversion: % of opps that entered a stage and progressed to the next within the analysis window.
- Win rate: won ÷ (won + lost) for opps that reached a stable decision stage (avoid counting early disqualification as “loss” unless that is your standard).
- Cycle time: median time in stage and time from create to close, with the 25th/75th percentiles; averages hide skew.
- Pipeline coverage: pipeline value for period ÷ bookings target (by segment/channel).
- Sales velocity (sanity check): number of opps × win rate × ASP ÷ cycle length.
- Forecast accuracy: within‑quarter MAPE and slip rate (share of commits that push out of the period).
Produce these for the whole funnel and for each segment/channel. This is your baseline.
4) Run hygiene and reality checks before interpreting results
A few simple screens save days of rework:
- Stage definition drift: did names or entry criteria change mid‑period? Reconcile or split the window.
- Duplicate or zombie opps: multiple opps for one deal; opps with >2× median age; opps with no activity for >30/60 days.
- Probability calibration: compare stated probabilities to observed conversion by stage; many CRMs overstate likelihood.
- Date games: repeated close‑date pushes; quarter‑end creates; sandbagging (late‑entered wins with minimal stage history).
- Amount inflation: amounts that drop ≥25% at late stages signal forecasting and governance issues.
- Channel leakage: partner‑sourced opps mis‑tagged as direct; reconcile with partner portal where possible.
Quarantine dirty records or recut metrics with and without them; disclose the treatment.
5) Build a cohort view—opps created in the same month/quarter
Cohorts let you see whether the engine is improving or decaying:
- For each created month, plot cumulative progression (to Stage 2, Stage 3, Close) and cycle times.
- Compare cohorts across segments/channels to spot deterioration (e.g., outbound Mid‑Market cohorts stalling at evaluation).
- Overlay major events: pricing changes, channel policy shifts, product releases, territory reassignments.
Cohorts prevent the “pipeline is big, therefore we’re fine” fallacy.
6) Cut by the four lenses that usually move value
Not all funnels are created equal. Always cut by:
- Route‑to‑market: direct vs. reseller vs. marketplace vs. OEM. Expect different conversion, ASP, cycle, and discount patterns.
- Deal archetype: new logo vs. expansion/upsell/renewal; single‑product vs. multi‑module.
- Customer size/vertical/geo: enterprise vs. mid‑market vs. SMB; regulated vs. unregulated; region‑specific procurement.
- Competitor at final round: win rate and discount needed vs. named rival.
These cuts reveal where the right‑to‑win is real and where the plan is heroic.
7) Diagnose where the bottleneck lives—coverage, conversion, velocity, or mix
Use a simple decision tree:
- Coverage problem: pipeline coverage < 2× (SMB/transactional) or < 3× (enterprise/long cycle) and top‑of‑funnel (MQL→SQL or SQL→Opp) is weak. Remedy is to demand gen/partner listings, not price.
- Conversion problem: healthy coverage but low win rate at late stages; pair with win/loss and pricing analysis—often a differentiation or pricing governance issue.
- Velocity problem: median time on stage or create‑to‑close much higher than peers; look for procurement gates, security/compliance reviews, contract redlines, or overloaded implementation teams.
- Mix problem: pipe skewed to low‑ASP or low‑fit segments; even perfect execution won’t hit the revenue plan—reset targeting.
Back every diagnosis with 2–3 quantitative exhibits and one qualitative proof point.
8) Overlay price and discount behavior on the funnel
Price often explains stalled deals or expensive wins:
- Plot discount depth by stage and its relationship with win rate and cycle time. Deep late‑stage discounting that does not improve win rate is margin leakage.
- Compare ASP vs. competitor in won and lost deals; identify the price gap at which outcomes flipped (from VoC and quotes).
- For subscription, check price‑led churn hazard in cohorts exposed to increases (Chapter 6.4). If hazard rises, curb aggressive uplift assumptions.
Translate findings into realized price corridors by segment and guardrails for the deal desk.
9) Add the capacity lens—can the org carry the plan?
A good funnel cannot overcome thin coverage or slow ramp:
- Ramped reps: count fully ramped AEs vs. plan; compute bookings per ramped AE and attainment distribution (watch the 80/20).
- Ramp curve: months to 50% and 100% productivity by segment; tie to hiring pipeline.
- Manager span: too wide often correlates with low coaching and poor forecast hygiene.
- BDR/SDR throughput: meetings set, accept rate, show rate, SQL creation by source.
- Partner capacity: active partners producing pipe, by tier; mindshare signals (co‑op usage, certifications).
- Coverage heatmap: territories/segments with uncovered accounts or partner gaps.
Push these into model capacity constraints (headcount, ramp timing, partner activation).
10) Respect product‑led and consumer variants of the funnel
Not every engine is sales‑led:
- PLG/usage‑led: traffic → sign‑up → activation/aha → weekly active → PQL → sales‑assist → paid → expansion. Track active rate, time‑to‑aha, and conversion of PQLs to opportunities.
- E‑commerce/consumer: sessions → product views → add‑to‑cart → checkout → purchase; track CVR, AOV, cart abandonment, and channel ROAS.
- Channel‑heavy models: partner registration → joint discovery → quote → sell‑through; measure partner source vs. influence, pipeline velocity through partner steps, and delist risk.
Use the variant that matches how demand actually forms.
11) Tie findings to the model and to forecast risk within 24 hours
For each material segment/channel, update:
- Stage conversion rates and cycle times (base and downside).
- Win rates vs. named competitors and realized ASP (corridors).
- Pipeline coverage required to hit the plan and the capacity to generate it (reps, partners).
- Price/discount guardrails and the expected impact on win rate and margin.
- Forecast accuracy/slippage and its translation into scenario ranges.
If evidence is thin, widen ranges and lower confidence; never round directional signals into point precision.
12) Early‑warning indicators to monitor monthly post‑close
Pick a short list that predicts slippage:
- Coverage: pipeline/target by segment; partner listings and delistings; inbound vs. outbound ratio.
- Conversion: stage‑by‑stage conversion and win rate; loss reasons; competitor frequency.
- Velocity: time in stage vs. threshold; close‑date push count; backlog at security/procurement/legal.
- Price & margin: discount depth distribution; promo reliance; realized ASP vs. corridor.
- Capacity: ramped reps vs. plan; attainment distribution; BDR throughput; partner‑sourced pipe.
Wire this into the operating rhythm; act before quarter‑end scramble.
Funnel Diagnostics Checklist (copy, fill, enforce)
- Glossary and scope frozen; segmentation applied consistently.
- Full CRM export landed (opps, leads, activities, stage histories, forecast snapshots); channel and pricing data included.
- Baseline math built: stage conversions, win rate, cycle times (p25/median/p75), coverage, velocity, forecast accuracy.
- Hygiene filters applied; dirty records quarantined; probability calibration performed.
- Cohort view built; events overlaid; trends called.
- Segment/channel/competitor cuts produced; bottlenecks classified (coverage, conversion, velocity, mix).
- Pricing/discount overlay run; realized ASP corridors and guardrails defined.
- Capacity analysis completed: ramp, productivity, coverage, partner capacity.
- PLG/consumer/channel variants considered where relevant.
- Model updated (conversion, cycle, win rate, ASP, coverage/capacity) with ranges and confidence.
- Early‑warning indicators defined; operating cadence agreed.
- Verifier sign‑off logged; clean‑team rules observed; exhibits footnoted.
Key formulas you will actually use
- Stage Conversion (s→s+1) = # progressed from s to s+1 ÷ # that entered s.
- Win Rate = # won ÷ (# won + # lost) for opps reaching the decision stage.
- Median Cycle = median(close date − create date); also median time in stage.
- Pipeline Coverage = Pipeline for period ÷ Bookings target (segment/channel).
- Sales Velocity ≈ # opps × Win Rate × ASP ÷ Cycle length (use for direction only).
- Forecast MAPE = mean(|Forecast − Actual| ÷ Actual) within the period.
- Slip Rate = value in commit that moved to next period ÷ commit value.
Outside‑in substitutions when data access is limited
- Pipeline: partner and distributor line cards, marketplace ranks, channel listings; web traffic and demo requests as proxies for top‑of‑funnel.
- Conversion/win: structured win/loss calls with customers and partners; competitive quote comparisons.
- Cycle: procurement and legal interviews on approval gates and typical timing by segment.
- Price/discount: invoice samples; distributor quotes; procurement recalls; marketplace price histories.
- Capacity: rep roster, hiring postings, partner certifications and co‑op usage.
72‑hour sprint plan (from blank page to decision‑grade)
- Day 0: Publish glossary and segmentation; issue data request; confirm clean‑team scope.
- Day 1: Land raw exports; build baseline stage conversions, win rate, cycle times; run hygiene checks; draft cohort view.
- Day 2: Cut by segment/channel/competitor; overlay pricing/discount; assess capacity and coverage; calibrate probabilities; compute forecast accuracy/slip.
- Day 3: Synthesize bottlenecks and levers; update model with base/downside ranges; propose term‑sheet protections (e.g., earnouts tied to price realization or NRR, covenants on promo/take‑rate intensity); set early‑warning indicators and owners.
Acceptance criteria for a decision‑grade funnel diagnostic
- Stage conversions, win rates, cycle times, and pipeline coverage produced by segment and channel; hygiene issues disclosed and controlled.
- Price/discount and competitor overlays complete; realized ASP corridors set.
- Capacity and coverage quantified; ramp and productivity linked to plan.
- Forecast accuracy and slip quantified with history.
- Model inputs updated with ranges and confidence; residual risk translated into valuation or terms.
- All exhibits footnoted; verifier replication complete; clean‑team and privacy rules observed.
Run this play with discipline and your funnel analysis will do what diligence needs most: separate story from system, pinpoint the few constraints that actually govern growth, and convert those findings into numbers you can price, structure, and manage.
9.2 Channel Mix Evaluation Template
Channel mix is your route‑to‑market in numbers: who originates and closes demand, what they keep, what you keep, and how reliably the machine scales by segment and geography. A good evaluation replaces generic “partner strength” claims with a quantified view of gross‑to‑net, CAC, payback, and LTV by channel—plus the operational gates and policy risks that can compress margin overnight. Use this template to get a decision‑grade answer in days, not weeks, and to push the findings directly into your model and post‑close plan.
What “good” looks like
You will end with (1) a segmented channel P&L (vendor‑realized and pocket price), (2) conversion and cycle time by route‑to‑market, (3) partner productivity and coverage heatmaps, (4) a mix‑shift bridge to revenue and CM2, (5) risk scenarios (take‑rate hikes, delistings, policy changes), and (6) a 90‑day playbook to re‑weight the mix.
Channels to evaluate (keep labels plain and mutually exclusive)
Direct (field/inside), Self‑serve/PLG, Reseller/VAR, Distributors/Wholesalers, Marketplaces/App stores, OEM/White‑label, System Integrators/MSPs, Referrals/Affiliates, Retail/e‑commerce (1P/3P).
Step‑by‑step evaluation (fast path for diligence)
1) Freeze definitions, scope, and segmentation
Write one sentence per route: “Reseller/VAR in North America, Mid‑Market manufacturing, multi‑module deals, LTM.” Lock your segmentation from Chapter 6 (customer/job, size band, geo, channel, product family). Decide whether you will report in vendor‑realized or pocket price (8.2) and in nominal or constant currency.
2) Assemble the channel evidence pack
Request once; analyze many times.
- Contracts and program guides: take‑rates, rebate ladders, co‑op/MDF accruals, paid placement/search, price‑parity/MAP, deal‑registration rules, tier requirements, termination, and audit clauses.
- Partner portal and marketplace exports: sourced/influenced pipeline, listings/delistings, category rank, seller tiers, certification counts, joint pipeline, and close rates.
- Distributor/POS sell‑through: by SKU and region; returns/chargebacks; inventory and buy‑backs.
- CRM + CPQ: opp source, registration dates, stage histories, win/loss by partner and competitor, discount/approval trails, ASP vs. list.
- Marketing systems: spend, UTMs, MQL→SQL→opp conversion; for paid media—channel, campaign, ROAS, and any holdout tests.
- Finance: rebate accruals, MDF utilization, co‑op true‑ups, DSO/DPO by route, warranty/returns.
- For OEM/white‑label: revenue recognition mechanics, branding constraints, and exclusivity.
3) Rebuild channel gross‑to‑net and pocket margin
For each route, compute the waterfall:
List → standard discounts → promos → rebates/chargebacks → returns → channel margin/take‑rate → off‑invoice incentives (MDF, co‑op, paid placement) → vendor‑realized → pocket price → COGS/service cost → CM1/CM2.
Report corridors (median, interquartile) by segment—not single points. Tie to 8.2.
4) Connect channel to funnel performance
Cut the core funnel metrics (9.1) by route:
- Stage‑to‑stage conversion, win rate vs. named rivals, cycle times (p25/median/p75).
- ASP and discount depth distribution; price governance exceptions by channel.
- Pipeline coverage and velocity by route (Sales Velocity ≈ # opps × win rate × ASP ÷ cycle).
- Forecast accuracy/slip rate by channel owner.
5) Build acquisition economics and payback by route
Define CAC consistently.
- Direct CAC: sales + marketing + SDRs + deal‑desk + promos used to acquire the customer (cycle‑aligned).
- Partner CAC: channel manager FTEs, partner incentives (MDF, SPIFFs), partner enablement/training, paid placement/search on marketplaces; include rev share if used to acquire.
- Paid media CAC: use incrementality (holdouts/switchbacks) where available; otherwise triangulate with geo or time‑based contrasts.
Compute payback two ways: P&L (CM2 covers P&L CAC) and cash (after working capital: prepayments/deferred revenue, DSO, inventory/returns).
6) Attribute fairly—avoid double counting
Pick a defensible attribution approach, state it, and hold it constant.
- Simple rule for diligence: originating source owns the opp unless a registered partner meets program rules; show an “influence” overlay but don’t double count.
- When paid media is material, favor incrementality evidence (geo holdouts, rotation tests) over last‑touch myths. Label confidence accordingly.
7) Quantify partner health and coverage
Partners follow their own funnel: recruited → onboarded → enabled → active → productive.
- Activation/productivity: % partners closing ≥1 opp/quarter; bookings per active partner; attainment distribution (watch the 80/20).
- Mindshare: co‑op drawdown rates, certifications, pipeline updates frequency, participation in QBRs.
- Coverage: heatmap of target accounts/geos with no tier‑1 partner; account mapping overlaps; white‑space.
- Quality: win rate and ASP by partner; discount escalation frequency; compliance with deal‑reg SLAs.
8) Map shelf and discoverability
In marketplaces/retail, shelf equals demand.
- Listings/delistings; category rank; paid placement share; review velocity/ratings; search share for key terms.
- Retail: distribution points, planogram placement, KVI exposure, promo depth/length; sell‑through vs. sell‑in gap.
9) Stress‑test risks and response levers
Turn channel myths into scenarios with toggles in the model:
- Take‑rate increase (+100–300 bps) and introduction of paid placement; net impact to vendor‑realized and CAC/payback.
- Delisting or tier downgrade at a top partner; velocity and coverage loss; time‑to‑replace.
- Policy changes (MAP/RPM enforcement, platform bundle, API/terms changes).
- Partner consolidation (fewer, larger distributors; rebate cliffs).
- Direct expansion (self‑serve/PLG lift) and potential channel conflict; set price fences and deal‑reg protections.
10) Mix‑shift bridge—show how route changes move revenue and CM2
Decompose revenue change into volume × price × mix with channel as the mix axis.
- Channel mix effect (CMX) in dollars:
CMX ≈ Σ_s [Rev_t0(s) × (Mix_t1(s) − Mix_t0(s)) × CM2%(s)], where s indexes channels. - Tie to LTV/CAC and cash payback by route; a mix that lifts CM2 but slows cash return may still be wrong for the hold period.
11) Concentration and resilience
Compute HHI for revenue by partner and by channel (HHI = Σ share²). High HHI flags counterparty risk and bargaining power. Add time‑to‑replace estimates for the top three partners.
12) Industry nuances you must handle explicitly
- SaaS/PLG: Self‑serve drives low CAC and fast cash payback; channel partners often add enterprise access but raise gross‑to‑net. Protect price fences and land‑and‑expand rules.
- Marketplaces/platforms: Revenue = GMV × take rate − incentives − returns/fraud. Discoverability and paid placement dominate; treat take‑rate compression as the base case unless countered by unique value.
- Hardware + distribution: Distinguish sell‑in from sell‑through; returns and inventory buy‑backs drive pocket price. Warranty and field service costs belong in CM2 by route.
- Healthcare/regulated: Contract vehicles, credentialing, and reimbursement codes are “channel gates.” Model approval timing and payer mix by route.
- Retail/CPG: KVIs set price image; monetize the tail. Trade spend (promo, co‑op, slotting) is CAC and gross‑to‑net—separate it from price.
- OEM/White‑label: Revenue recognition, branding constraints, and geographic exclusivities can cap future direct expansion; treat as structural, not temporary.
13) Compliance and governance
Operate under clean‑team rules for invoice‑level or partner‑specific analysis; share only aggregated, anonymized outputs beyond the ring‑fence. Respect MAP/RPM and platform terms; no pre‑close pricing coordination. Note MFNs, price‑parity, and audit rights that create unintended cross‑channel linkages.
Copy‑ready templates (paste into your workspace)
A) Channel Mix Canvas (one per route × segment)
- Scope & unit (e.g., Mid‑Market NA; reseller; ARR dollar).
- Vendor‑realized corridor; pocket price; sources.
- Waterfall shares: discounts, rebates, returns/chargebacks, take‑rate, off‑invoice incentives.
- CM1/CM2 and key cost drivers (COGS, service cost‑to‑serve).
- Funnel metrics: conversion, win rate, cycle; ASP/discount patterns; forecast slip.
- CAC (definition and inclusions); payback (P&L and cash).
- Coverage & shelf: active partners, activation rate, listings/ranks, review velocity.
- Risks & gates: take‑rate policy, delist risk, MAP/MFN, exclusivities, regulatory approvals.
- Headroom & hazards: price corridors by route, elasticity notes, promo dependency.
- Confidence & sources; verifier sign‑off.
B) Partner Scorecard (one page per top partner)
- Tier, certifications, regions served, product scope.
- Bookings and win rate trend; ASP vs. direct; discount exceptions.
- Pipeline sourced/influenced; deal‑reg SLA adherence; data hygiene.
- Co‑op/MDF usage; QBR participation; enablement completed.
- Delist/list events; shelf rank (if marketplace/retail).
- Risks: financial stability, contract cliffs, exclusivity traps, conflict.
- Actions: keep/grow/fix/exit; owner and next QBR date.
C) Marketplace/Retail Diagnostic (per platform/retailer)
- Category size; share and rank; paid placement share; review velocity.
- Take‑rate tiers; hidden fees (fulfillment, payments, ads); returns/fraud rates.
- Policy watchlist (parity, bundling, API changes).
- Scenario: +200 bps take‑rate; delisting risk; residual impact after counter.
D) Paid Media Diagnostic (where spend is material)
- Channel/campaign spend; CAC and ROAS; incrementality evidence (holdouts/geo tests).
- Saturation/scale curves; diminishing returns point.
- Cannibalization with direct/brand; post‑click vs. post‑view bias.
- Actions: budget reweighting; creative/landing improvements; attribution upgrade.
Early‑warning indicators (add to the PMO dashboard)
- Partner listings/delistings; certification count; co‑op drawdown rates; deal‑reg SLA compliance.
- Take‑rate and policy change notices; rebate accrual % drift; paid placement share.
- Shelf rank and review velocity; sell‑through vs. sell‑in gap; return/chargeback rate.
- Discount depth by route; ASP vs. corridor; promo reliance.
- Pipeline coverage by channel; ramped partners/AEs vs. plan; attainment distribution.
- DSO/DPO by route; warranty and service cost per unit by channel.
72‑hour sprint plan (from blank page to a defendable view)
- Day 0: Freeze segments and route definitions; issue the channel data request; confirm clean‑team and compliance boundaries.
- Day 1: Build gross‑to‑net waterfalls and CM2 for two top routes; land funnel cuts and basic capacity/coverage; draft first Channel Mix Canvases.
- Day 2: Complete partner scorecards; compute CAC/payback; assemble shelf/discoverability metrics; run two risk scenarios (take‑rate hike, delist).
- Day 3: Publish mix‑shift bridge, LTV/CAC by route, and concentration (HHI); update model; propose term‑sheet levers and a 90‑day post‑close channel plan.
Acceptance criteria (use this as your “done” check)
- Channel definitions and segments frozen; clean attribution rule stated and applied consistently.
- Verified gross‑to‑net and pocket price by route; CM1/CM2 computed with sources and a second‑person tie‑out.
- Funnel performance, ASP/discount behavior, and forecast hygiene cut by route; capacity/coverage quantified.
- CAC/payback (P&L and cash) by channel; LTV by route derived from cohorts where applicable.
- Mix‑shift bridge produced; two or more risk scenarios quantified with toggles in the model.
- Partner scorecards for top contributors; marketplace/retail diagnostics where relevant.
- Early‑warning indicators defined; owners and cadence set; compliance notes documented.
- Model updated within 24 hours; residual risk translated into valuation ranges and terms (earnouts on price realization/NRR, covenants on promo/take‑rate intensity, conditions on key partner renewals/tier maintenance).
Common failure modes—and the quick fix
- Counting influenced and sourced as separate revenue. Fix: freeze an attribution rule; present “influence” only as an overlay.
- Treating trade spend as price. Fix: separate promos, co‑op, MDF from list/discounts; show promo decay and ROI.
- Averages that hide route realities. Fix: compute by route and segment; only roll up with value weights.
- Underestimating policy risk. Fix: add take‑rate and MAP/parity scenarios to base/downside with trigger‑based counters.
- Ignoring working capital. Fix: report cash payback by route; include DSO, inventory, returns, and deferred revenue.
- Channel conflict hand‑waving. Fix: codify deal‑reg SLAs, price fences, and lead routing; quantify cannibalization and protect KVIs.
Use this template and you will turn “strong channels” into a quantified route‑to‑market plan: which channels to grow, which to prune, how mix affects realized price and payback, and how to protect margin if partners or platforms change the rules.
9.3 Marketing ROI Benchmark Checklist
Marketing ROI is not a single number. It is a set of linked measures—incremental revenue and contribution margin per dollar spent, payback speed, and scalability—cut by segment and route‑to‑market. In diligence, your goal is to separate what truly moves demand from what platforms or last‑click attribution claim, then benchmark performance against decision‑grade guardrails so you can reweight budgets, price risk, and size upside credibly. The checklist below gives you a fast, auditable path: define the financial ground rules, assemble a clean data pack, estimate incrementality with two methods, benchmark against base‑rates by business model, and wire results into unit economics and the forecast.
Ground rules (set these before touching data)
- Objective function: Optimize for incremental CM2 (contribution margin after variable costs and channel incentives), not top‑line only.
- Granularity: Report at two levels—blended (business‑wide MER) and channel/campaign cohorts—always by the segments from Chapter 6.
- Time windows: Use weekly for MMM/experiments and monthly for financial roll‑ups; keep a 24‑month history if possible to capture seasonality.
- Attribution policy: “Incrementality first.” Platform‑reported conversions are inputs, not answers.
- Numerators/denominators: Revenue is vendor‑realized (8.2). Profit is CM2 (8.3). Spend includes media, fees, and off‑invoice incentives tied to acquisition (MDF/co‑op, paid placement).
- Compliance: Respect clean‑team rules for invoice‑level data; no pre‑close coordination with competitors or partners.
Step‑by‑step ROI build (fast, defensible)
1) Assemble the data pack (once, with clear owners)
- Spend & meta: Daily/weekly spend, channel, campaign/ad set, creative, geo, device, audience, platform fees.
- Traffic & conversion: Sessions, sign‑ups, PQL/MQL, SQL, opportunities, orders; return/cancel flags; UTM standards; tag health.
- Revenue & margin: Orders and realized revenue; discounts, rebates, refunds/chargebacks; COGS; fulfillment/shipping; payment fees; warranty/service.
- Customer outcomes: Cohort GRR/NRR, ARPU, LTV; first‑purchase vs. repeat; retention curves by source.
- Context: Promotions, pricing changes, stockouts, site outages, channel delistings, seasonality markers, external shocks.
- Governance: A single source register with field definitions, coverage notes, and known gaps.
2) Normalize and reconcile (the accounting pass)
- Align calendars and currencies; choose nominal vs. real and stick to it.
- Map revenue to vendor‑realized and to CM2 by order.
- Reconcile pass‑through promotions (trade/co‑op/MDF) as spend, not price.
- Deduplicate UTMs and collapse vanity campaigns; repair obvious tagging breaks (direct/none floods).
- Produce a “data health” memo; quarantine outliers and document treatment.
3) Compute baseline metrics (blended and by channel/segment)
- MER (Media Efficiency Ratio): Revenue ÷ Spend (blended).
- ROAS (attributed): Platform revenue ÷ Spend (diagnostic only).
- iROAS (incremental): Incremental revenue (or CM2) from tests or MMM ÷ Spend.
- CPA / CAC: Spend ÷ conversions (order or paying customer); cycle‑align CAC to first revenue.
- Payback (P&L and cash): Months for cumulative CM2 (and cash inflows) to cover CAC.
- LTV:CAC: From cohort LTV (6.3, 8.3) divided by CAC; show range and confidence.
- Saturation curve: Plot weekly spend vs. incremental revenue; mark diminishing returns point.
4) Estimate incrementality with two lenses (always two)
- Experiments (preferred):
- Geo splits/market tests: Rotate on/off by DMA/city; 6–12 week windows; ensure balance on seasonality and retail calendars.
- Audience holdouts/ghost ads: Suppress a statistically valid control; measure lift in orders/GMV and CM2.
- Switchbacks: On/off by region or channel over time; fit a simple pre/post model with controls.
- MMM (pragmatic):
- Weekly data ≥ 24 months, with adstock/carryover and saturation terms; include promos, price, distribution/shelf variables, holidays, and exogenous shocks.
- Use MMM to get channel‑level iROAS and a budget response curve; validate against at least one real‑world test.
- Guardrail: If experiments and MMM disagree materially, widen ranges, lower confidence, and prioritize the experiment’s direction for near‑term decisions.
5) Connect ROI to unit economics and retention
- Translate allowable CAC: Allowable CAC = LTV ÷ target (LTV:CAC).
- Or allowable CPA per order: Allowable CPA = Target CM2 per order × Target marketing cost‑to‑sales ratio.
- If price increases or promo cuts are in plan, re‑run elasticity (6.4) and refill CM2; re‑test iROAS—many “wins” disappear once promo is removed.
- For subscription/usage models, compute iROAS on ARR/NRR, not just on first‑order revenue.
6) Benchmark by business model (base‑rate guardrails; tighten with your data)
Use these to frame debate, not as absolutes; refine by segment and margin structure.
- B2B SaaS (Mid‑Market, direct): LTV:CAC 3–5×; payback 9–18 months; iROAS focus on pipeline‑to‑ARR with sourced vs. influenced split; ABM/mid‑funnel content rarely shows same‑quarter ROI—judge on opportunity quality and cycle compression.
- PLG/self‑serve: CAC materially lower; payback often < 6–9 months; watch activation rate and PQL→paid conversion; attribute uplift from lifecycle/email/product prompts separately from paid media.
- E‑commerce (mid/high gross margin): Blended MER 2–4×; allowable CPA tied to CM2; returns and promo depth can cut true iROAS in half—always net them out.
- Marketplaces: Measure on GMV and take‑rate net; buyer vs. seller acquisition ROI differ; subsidies (free shipping, incentives) are spend, not price.
- Mobile apps (IAP/subscription): CPI/CPE is a vanity metric without day‑n retention and paid conversion; treat SKAN/platform‑reported ROAS as directional; rely on cohorts and geo tests.
- Retail trade/media: Treat trade spend as acquisition cost; ROI rides on sell‑through, not sell‑in. KVIs cap price—monetize tail with mix.
7) Diagnose scale vs. waste (saturation and spillovers)
- Fit a simple saturating response (e.g., “each +$X adds less than the last”) per channel/segment; mark the efficient frontier.
- Quantify spillovers: brand search, direct, and email often ride on upper‑funnel display/TV—give them credit only if lift appears in tests/MMM.
- Identify crowding: if paid search cannibalizes organic/brand beyond a threshold, cap bids on branded terms (protect KVIs if needed) and re‑deploy to true incremental spend.
8) Fraud, leakage, and hygiene checks (never skip)
- Invalid traffic/bot checks, click flooding/time‑to‑install anomalies, duplicate UTMs, inorganic “direct” spikes after tag loss.
- Coupon/promo leakage and affiliate hijacking; view‑through inflation; audience overlap across platforms.
- Stockouts/outages masking demand; creative wear‑out; geo mismatch between spend and conversion.
- Channel policy risks: take‑rate hikes, parity/MAP rules, paid placement creep (9.2).
9) Budget decision rules (write them down)
- Maintain two‑tier targets: (a) marginal iROAS ≥ threshold on CM2 (by channel), and (b) portfolio MER that hits EBIT goals.
- Reallocate weekly using the response curves; move $ from channels past their diminishing‑returns knee to channels with headroom and proven lift.
- Tie experiments to budget unlocks: “If geo test shows iROAS ≥ 2.5× on CM2, increase by +20% until the next knee.”
10) Push into the model and term sheet (24‑hour rule)
- Update price realization, CM2, CAC/payback, and channel mix lines with base/range by segment.
- Where uncertainty is material, reflect it in valuation ranges and structure (earnouts tied to revenue or NRR from paid cohorts; covenants on promo/take‑rate intensity; conditions on key platform/partner renewals).
Copy‑ready checklists and templates
A) Marketing ROI Benchmark Checklist (tick each box)
- Objective function and financial ground rules written (CM2 focus; time windows; attribution policy).
- Data pack landed and reconciled (spend, traffic, revenue, CM2, cohorts; source register complete).
- Baseline MER/ROAS/CAC/payback/LTV:CAC computed (blended + by channel/segment).
- Incrementality estimated with two methods (experiments + MMM), with ranges and confidence.
- Response curves and diminishing‑returns knees identified; spillovers measured.
- Benchmarks applied by business model; deviations explained with segment economics.
- Fraud/leakage/hygiene checks run; anomalies documented and adjusted.
- Budget decision rules and test‑to‑invest criteria written; owners assigned.
- Model updated (price, CM2, CAC/payback, channel mix); term‑sheet levers proposed.
- Early‑warning indicators defined; reporting cadence set; verifier sign‑off logged.
B) Allowable Spend & Target Setting Card (one per segment/channel)
- Target LTV:CAC (range) and payback (P&L/cash).
- Current iROAS (CM2‑based) with confidence.
- Allowable CAC/CPA today; headroom to knee of curve ($ and %).
- Key risks (policy, tracking, seasonality); planned experiments and unlock criteria.
- Owner, next review date, and budget change triggers.
C) Experiment Design Mini‑Template
- Hypothesis and value at stake.
- Unit of randomization (geo/audience/time), sample size, duration, and power.
- Success metric(s): incremental CM2 per $; secondaries (new‑to‑file %, activation, repeat rate).
- Pre‑reg’d analysis plan; guardrails (brand/KVI constraints); dependencies (inventory, promo).
- Decision rule and next action (scale/prune/re‑test).
Early‑warning indicators (add to the operating dashboard)
- MER and iROAS vs. thresholds, weekly.
- Marginal iROAS drift at last +10–20% spend increment.
- Payback by cohort/source; LTV forecast error vs. actual at 30/60/90 days.
- Promo depth/length and return/chargeback rate trend.
- Shelf/discoverability metrics (rank, paid placement share, reviews) when marketplaces/retail matter.
- Tracking health: tag loss, spike in “direct/none,” platform reporting deltas.
- Policy/partner changes: take‑rate notices, parity/MAP enforcement, delistings.
72‑hour sprint plan (from blank page to decision‑grade view)
- Day 0: Lock ground rules; issue the data request; publish attribution policy and experiment shortlist.
- Day 1: Build baseline metrics (MER/ROAS/CAC/payback/LTV:CAC) blended and by channel/segment; run hygiene checks; start response curves.
- Day 2: Land at least one live test (geo/audience) and a first‑pass MMM; compute iROAS ranges; draft Allowable Spend Cards; identify quick budget reweights.
- Day 3: Update the model and unit economics; set early‑warning indicators and test‑to‑invest gates; circulate a two‑page decision memo (what to scale, what to cap, what to test next).
Acceptance criteria (what “done” looks like)
- Results presented in CM2 and incremental terms, with ranges and confidence.
- Two independent incrementality lenses (experiment + MMM) reconciled; conflicts and implications stated.
- Benchmarks applied by business model and segment; outliers explained with evidence.
- Budget and mix recommendations tied to response curves and allowable CAC/CPA.
- Model updated within 24 hours; residual risk translated into valuation and terms.
- QA completed: verifier can reproduce metrics from the source register; clean‑team and compliance notes on file.
Common failure modes—and quick fixes
- Chasing platform ROAS: Treat as diagnostic; decide on iROAS (CM2) from tests/MMM.
- Ignoring returns/promo: Net them out before computing ROI.
- Counting influenced and sourced twice: Freeze an attribution rule; show influence as an overlay only.
- Last‑click cannibalization: Cap brand bids where cannibalization exceeds a set threshold; reinvest in channels with proven lift.
- No cycle alignment: Shift CAC to revenue start; otherwise payback is fiction.
- Averages that hide truths: Segment by route‑to‑market, geo, margin band, and customer type; roll up only with value weights.
Use this checklist and you will turn marketing ROI from dashboard noise into decisions: which dollars to keep, which to cut, and where to place the next incremental $1 so revenue, margin, and payback improve—by segment, with evidence, and on a clock.
9.4 Customer Acquisition Cost Calculator Template
Customer Acquisition Cost (CAC) is only useful when it is well‑defined, segmented, cycle‑aligned, and tied to cash. In diligence, your calculator must produce numbers that a CFO and CRO would both sign off on—by route‑to‑market and segment—and that plug straight into unit economics (8.3), pricing (8.2), and the funnel (9.1). This template gives you the exact inputs, formulas, attribution rules, and QA steps to build a defendable CAC view in days, not weeks.
Start by fixing the unit of analysis (“per new paying customer,” “per first order,” or “per activated account”) and the scope (segment, channel, geography, time window). Then decide which CAC flavors you will report. For diligence, report at least two:
- Blended CAC (fully loaded): All acquisition‑related Sales & Marketing cash costs divided by new paying customers acquired in the period, cycle‑aligned.
- Channel CAC: CAC by route‑to‑market (direct, reseller, marketplace, self‑serve/PLG, paid media cohort), using channel‑specific costs and attribution rules.
You may also compute CPA (cost per order) for transactional businesses; translate CPA to CAC by applying the new‑to‑file share and retention.
What your CAC calculator must include
- A clear glossary of included/excluded costs (cash basis vs. P&L, handled consistently).
- Cycle alignment: spend shifted by the average lag from first touch to revenue, by channel.
- Attribution rules: who “owns” a customer when multiple touches or partners are involved.
- Segmentation: customer job/vertical, size band, product family, region, and route‑to‑market.
- Outputs: CAC (blended and by channel), P&L and cash payback, and LTV:CAC by segment.
- QA pack: source register, reconciliations to GL, and a verifier tie‑out.
Inputs to collect (one‑time request; reuse everywhere)
- Sales & Marketing spend (cash view preferred): payroll (base + variable comp), benefits, contractors, agencies, paid media, events, creative, tools, data, partner enablement, spiffs, referral bounties, trial incentives, samples, and off‑invoice incentives that are acquisition‑linked (co‑op/MDF, paid placement). If capitalized commissions are present (ASC 606), add back amortization to reflect cash.
- Channel economics: marketplace or reseller take rates, rebates, co‑op accruals, paid search/placement fees, and any acquisition‑specific partner incentives.
- Funnel and cohort outputs: new paying customers, first orders, new logos, expansion vs. new breakdown, stage‑by‑stage volumes, and time‑to‑close by segment/channel.
- Revenue and margin: vendor‑realized revenue, CM2 (contribution margin after variable service and channel costs) for first period(s), returns and chargebacks.
- Timing and context: pricing changes, promotions, stockouts, tracking/tagging gaps, policy changes.
Build sequence and formulas (copy into your model notes)
1) Freeze the unit and segmentation
Write one sentence per view, for example: “CAC per new paying Mid‑Market customer in North America, direct field route, last 4 quarters (cycle‑aligned).”
2) Decide the accounting basis (and keep it consistent)
- Cash CAC (preferred for payback): cash outflow for acquisition activities.
- P&L CAC (secondary): GAAP Sales & Marketing expense; add back non‑cash items you don’t want in payback (e.g., stock comp) if you use this view.
3) Cycle‑align spend to revenue start
For each channel ccc, compute the median lag LcL_cLc from first touch (or opp create) to first revenue. Then shift spend backward:
- Cycle‑aligned spend in month ttt, channel ccc
Sc,taligned=Sc,t−LcbookedS^{aligned}_{c,t} = S^{booked}_{c,t-L_c}Sc,taligned=Sc,t−Lcbooked
Use weeks for high‑velocity consumers; months or quarters for enterprise. If cycle length is a distribution, weight is spent across the lag histogram (acceptable in diligence to use the median).
4) Define who “owns” the customer (attribution)
- Sourcing rule (default for diligence): the originating source or registered partner that created the opportunity owns the acquisition. Track influence as a separate overlay—do not double count.
- Paid media incrementality: where spend is material, prefer geo/audience experiments or MMM to adjust platform‑reported conversions (see 9.3). Use the incremental conversions when available.
5) Compute CAC (blended and by channel)
- New customers (cohorted):
Nc=N_{c} =Nc= number of new paying customers in period attributable to channel ccc (per the sourcing rule). - Channel CAC:
CACc=ScalignedNcCAC_{c} = \dfrac{S^{aligned}_{c}}{N_{c}}CACc=NcScaligned
where ScalignedS^{aligned}_{c}Scaligned includes channel‑specific acquisition costs:- Paid media + agency fees + platform/marketplace ads
- Partner incentives (MDF, co‑op, bounties), paid placement/search
- Channel manager FTEs and enablement directly tied to partner acquisition
- Sales comp and deal‑desk tied to channel ccc (for direct, include AE/SDR payroll and variable comp)
- Trial incentives and samples; referral rewards
- Blended CAC (fully loaded):
CACblended=∑cScaligned+Sshared∑cNcCAC_{blended} = \dfrac{\sum_c S^{aligned}_{c} + S^{shared}}{\sum_c N_{c}}CACblended=∑cNc∑cScaligned+Sshared
where SsharedS^{shared}Sshared is the shared spend you allocate by your chosen driver (see below).
6) Allocate shared spend transparently
Pick one rule and stick to it; show a sensitivity if it matters.
- By sourced new customers: proportionally to NcN_cNc (simple; fair when channels are similar).
- By influencing pipeline dollars: when upper‑funnel or brand spend is significant.
- By sales effort: allocate AE/SE payroll by time tracking or opp counts per channel.
Document the rule in the calculator and keep a version without shared spend for sanity checks.
7) Compute payback (P&L and cash)
- P&L payback (months): months until cumulative CM2 per customer covers P&L CAC.
- Cash payback (months): months until cumulative cash inflows (after working capital effects) exceed cash CAC.
Working‑capital notes: annual prepay (SaaS) shortens cash payback; inventory and returns (retail) lengthen it; DSO and chargebacks matter in marketplaces.
8) Connect CAC to LTV
Use cohort‑based LTV (6.3, 8.3). Report both:
- LTV:CAC ratio and range by segment/channel.
- Allowable CAC: CACallow=LTVTarget LTV:CACCAC_{allow} = \dfrac{LTV}{Target\; LTV{:}CAC}CACallow=TargetLTV:CACLTV (e.g., 3:1 mid‑market SaaS baseline, edited to your evidence).
9) CAC bridges you should always produce
- Spend → CAC bridge: total aligned spend → minus non‑acquisition items → minus expansion spend → allocated shared spend → channel spend → CAC by channel.
- CPA → CAC bridge (consumer/e‑com): CPA ÷ new‑to‑file % → CAC; then overlay repeat behavior to get LTV:CAC.
What counts as “acquisition cost” (include vs. exclude)
Include (if it exists to get new customers):
- Paid media and agency fees, sponsorships, events
- AE/SDR/BDR payroll, benefits, variable comp, spiffs; sales engineering tied to pre‑sale
- Partner manager FTEs; partner incentives (MDF, co‑op, bounties), paid placement/search
- Referral rewards; trial incentives; samples/free goods intended for acquisition
- Tools and data subscriptions used primarily for acquisition (intent data, enrichment)
- Creative production for acquisition campaigns
- Capitalized commissions (use cash picture—add back amortization to CAC)
Exclude (or treat consistently elsewhere):
- Customer success and account management for post‑sale retention/expansion (belongs in service cost‑to‑serve or expansion CAC)
- General brand or corp comms if not primarily acquisition (unless you allocate)
- Product R&D and growth/experimentation engineering (disclose separately; do not bury in CAC)
- Fulfillment, onboarding labor billed as services (belongs in CM2 unless used purely as an acquisition incentive)
- Free‑tier infrastructure for non‑converting users (account as COGS/opex; disclose if treated as CAC in PLG tests)
Calculator structure (tabs you can replicate)
- Inputs: time window, segments, channels, accounting basis (cash/P&L), allocation rule, cycle lags.
- Spend Register: detailed lines by channel and shared buckets with GL references.
- Funnel & Timing: new customers, first orders, lags by channel; cohort keys.
- Channel CAC: per‑channel aligned spend, customers, CAC, confidence.
- Blended CAC: with/without shared spend; sensitivity to allocation rule.
- Payback & LTV: CM2 per period, working capital assumptions, payback (P&L/cash), LTV:CAC.
- Bridges: Spend→CAC, CPA→CAC, and CAC→Payback.
- QA & Notes: reconciliations to GL, exclusions, data health checks, verifier sign‑off.
Worked mini‑examples (proportioned for slides)
- Mid‑Market SaaS (direct field):
Aligned quarterly S&M cash spend = $6.0M; new paying logos = 750 → Blended CAC ≈ $8,000.
CM2 per customer in Months 1–12 averages $1,200/month with 10% annual prepay. P&L payback ≈ 7–8 months; cash payback ≈ immediate to 3 months (prepay effect).
LTV (36‑month horizon, GRR 92%, NRR 112%, GM 80%) ≈ $24k → LTV:CAC ≈ 3.0×. - E‑commerce (high‑margin DTC):
CPA on first order = $28; new‑to‑file share = 60% → CAC ≈ $47.
CM2 per order = $22; repeat rate yields 2.1 orders in 90 days. Cash payback ≈ 2 orders; watch returns (8%)—if returns rise to 12%, payback slips beyond 90 days. - Marketplace (buyer acquisition):
Spend + incentives (shipping subsidies, coupons) = $1.2M; net new buyers = 30k → CAC ≈ $40.
GMV per buyer in 6 months = $220; take rate net of returns/fraud = 12% → revenue $26.40; variable platform costs $6.40 → CM2 $20. Payback > 6 months → tighten subsidies or lift take rate on the tail.
QA steps that prevent bad CAC math
- Cycle alignment applied and documented; show the lag histogram per channel.
- Numerator/denominator match: only new paying customers in the period; exclude expansions unless you compute expansion CAC separately.
- Attribution rule frozen; no sourced + influenced double counting.
- Trade spend vs. price: off‑invoice incentives are either spend (CAC) or gross‑to‑net—never both.
- GL tie‑out: sum of spend lines reconciled to the general ledger; variances explained.
- Data health: remove zombie opps, fix tagging gaps (9.1, 9.3); label low‑confidence segments.
- Verifier sign‑off: a second person can reproduce CAC from the register and assumptions.
Red flags (and immediate responses)
- Rising CAC with flat CM2 and stagnant win rates: shift budget to channels with demonstrable incrementality; kill vanity spend; enforce deal‑desk guardrails (8.2).
- Great CAC, terrible retention: LTV:CAC < 2× in core segments—reweight toward cohorts with stronger GRR/NRR; fix onboarding before scaling spend.
- Channel dependency: HHI high; one marketplace controls >50% of sourced customers—run a take‑rate hike and delist scenario (9.2) and reflect in payback ranges.
- Capitalized commissions hide economics: move to cash CAC view; reveal payback sensitivity.
- “Free” PLG leads that aren’t free: include growth engineering emails/push and promo credits used to convert; treat as acquisition spend or disclose separately.
Copy‑ready CAC Calculator Canvas (one per segment × channel)
- Scope & unit: [Segment, region, route, period].
- Accounting basis: Cash / P&L; cycle lag LcL_cLc and alignment policy.
- Attribution: Sourcing rule; incrementality adjustment (Y/N).
- Aligned spend: media/fees, partner incentives, sales payroll/comp, enablement, off‑invoice incentives, shared spend allocation rule.
- New paying customers (N): count, data source, confidence.
- CAC: Saligned/NS^{aligned}/NSaligned/N (range and point).
- CM2 (first period[s]): value and variability drivers.
- Payback: P&L and cash months; working‑capital assumptions.
- LTV:CAC: method, horizon, discount rate, range.
- Risks & gates: policy (MAP/parity), take‑rate, promo dependency, tracking gaps.
- QA & sources: GL tie‑out ref, funnel export ref, verifier initials, date.
72‑hour sprint plan (from blank sheet to decision‑grade CAC)
- Day 0: Freeze units, segments, and attribution; set accounting basis and cycle alignment policy; issue a single data request with a source register.
- Day 1: Land spend register and funnel outputs; compute lags; build channel CAC and blended CAC; reconcile to GL; label low‑confidence cuts.
- Day 2: Add P&L/cash payback and LTV:CAC; produce Spend→CAC and CPA→CAC bridges; run at least one risk scenario (take‑rate hike, promo withdrawal).
- Day 3: Update the model (CAC, payback, LTV:CAC by segment/channel); set allowable CAC targets; propose budget reweights and term‑sheet levers where uncertainty remains (earnouts tied to NRR or price realization; covenants on promo/take‑rate intensity).
Acceptance criteria (use this as your “done” check)
- Units, segments, attribution, and accounting basis frozen and documented.
- Cycle‑aligned channel CAC and blended CAC computed with sources; GL tie‑out complete.
- P&L and cash payback calculated; LTV:CAC shown with ranges and confidence.
- Spend→CAC and CPA→CAC bridges produced; shared‑spend allocation rule disclosed.
- Model updated within 24 hours; residual risk reflected in valuation/terms; early‑warning indicators defined (CAC drift, payback slippage, take‑rate notices).
- Verifier sign‑off logged; clean‑team and data‑handling rules observed.
Use this template and your CAC won’t be a vanity number. It will be a decision tool that aligns Sales and Finance, exposes true payback by route‑to‑market, and shows exactly where the next dollar of acquisition should go—and where it should not.
