$20M → $100M GTM Decision Engine
Model the economics. Pressure-test the channels. Decide where to invest.
A working decision model for a recurring-revenue B2B services company selling dedicated remote professionals to U.S. SMB and mid-market teams. It does not assume knowledge of the company's internal data. Every assumption is editable, every default is illustrative, and every recommendation is recomputed from the inputs on screen.
Which growth motion deserves the next several million dollars?
The objective is not to generate more activity. It is to identify the acquisition model capable of supporting the next stage of growth with attractive economics, sufficient capacity, and a defensible market position.
Primary inputs
Every figure below is editable.
Illustrative assumptions — replace with company data
Required arithmetic
Derived directly from the inputs above.
$20.0M → $100.0M
Gross of churn on the existing base
Over 3 years
Decision to make
Set by the model, not by preference.
Displayed as NOT YET DETERMINED until discovery replaces the illustrative inputs. What is shown here is the ranking the current assumptions produce — not a conclusion about the business.
Model indication changes as assumptions change.
Growth math
A three-year revenue bridge built from retention, expansion and channel-acquired revenue. If the bridge does not close, no amount of activity will.
Three-year revenue bridge
Beginning − churn + expansion + acquired revenue = ending.
| Bridge line | Year 1 | Year 2 | Year 3 |
|---|---|---|---|
| Beginning recurring revenue | $20,000,000 | $35,425,500 | $58,455,240 |
| Less: churned revenue | ($2,000,000) | ($3,542,550) | ($5,845,524) |
| Plus: expansion revenue | $1,600,000 | $2,834,040 | $4,676,419 |
| Plus: outbound acquired | $6,006,000 | $9,009,000 | $12,612,600 |
| Plus: inbound / marketing-led acquired | $3,217,500 | $4,826,250 | $6,756,750 |
| Plus: partnerships acquired | $2,912,000 | $4,368,000 | $6,115,200 |
| Plus: referrals acquired | $3,690,000 | $5,535,000 | $7,749,000 |
| Ending annual revenue | $35.4M | $58.5M | $90.5M |
Bars are shown against the $100.0M target.
Outcome
Shortfall
At blended ACV across channels
Beyond what the model already produces
Bridge assumptions
Channel-level assumptions live in Section 04 and feed this bridge directly.
ICP scorecard
Segments are scored on economics (60%) and strategic fit (40%), normalized against each other. Ranking is relative — adding or editing a segment re-scores the entire set.
Illustrative assumptions — replace with company data
Lower Mid-Market
Relative strength in retention, expansion potential and competitive intensity is offset by weaker gross margin, decision-maker accessibility and operational complexity. Implied lifetime value to CAC is 11.0x at 90% retention and $43,000 acquisition cost, with a 74-day cycle. Low AI displacement risk supports the durability of pricing in this segment.
Mid-Market
Relative strength in contract value, retention and expansion potential is offset by weaker operational complexity, decision-maker accessibility and sales cycle length. Implied lifetime value to CAC is 12.6x at 93% retention and $86,000 acquisition cost, with a 118-day cycle. Low AI displacement risk supports the durability of pricing in this segment.
Upper SMB
Relative strength in acquisition cost relative to contract value, sales cycle length and decision-maker accessibility is offset by weaker contract value, AI displacement risk and competitive intensity. Implied lifetime value to CAC is 8.3x at 84% retention and $21,000 acquisition cost, with a 42-day cycle.
Small SMB
Relative strength in gross margin, sales cycle length and decision-maker accessibility is offset by weaker AI displacement risk, competitive intensity and expansion potential. Implied lifetime value to CAC is 5.5x at 74% retention and $9,500 acquisition cost, with a 21-day cycle. AI displacement risk is high, which discounts the durability of these economics.
Segment inputs
Segment names and every variable are editable. Scores are relative to the set.
Channel economics
Four acquisition motions modeled on the same arithmetic, then scored on economics, scalability, speed, capacity requirement and strategic risk. The ranking is an output, not an opinion.
Illustrative assumptions — replace with company data
Composite score 76/100 · 68.8x LTV:CAC · 1-month payback
Composite score 62/100 · 7.0x LTV:CAC · 5-month payback
Pressure test
Apply stress to every channel simultaneously and watch the ranking move.
Referrals
Demand exceeds entered capacity. Output is capped at 45 deals — spend above this point buys pipeline the organization cannot convert.
Outbound
Partnerships
Inbound / Marketing-Led
What does the acquisition machine actually need to produce?
The funnel is solved backwards from the revenue target using the conversion assumptions of the selected ICP and channel. Volume requirements are an arithmetic consequence, not a stretch goal.
Required funnel
Solved from $81.2M of required new ARR at $105,000 ACV (Lower Mid-Market) through Outbound conversion rates.
Across 3 years — $27.1M per year.
Requirement summary
Capacity reality check
The plan requires 168 more wins per year than the organization can currently absorb — approximately 23 additional quota-carrying heads at the entered productivity of 7.5 deals per head. A theoretically attractive strategy that exceeds capacity is not executable.
Referral engine
Referrals are the only channel where the acquisition cost is largely a function of delivery quality. The simulator below sizes what a structured program could contribute — and what it cannot.
Illustrative assumptions — replace with company data
Program inputs
Modeled output
Referrals close 17% of the remaining gap. The balance must come from a scalable outbound, inbound or partnership motion.
Model-indicated referral architecture
Illustrative framework text — replace with the motion the company can actually operate.
AI defensibility matrix
As AI commoditizes lower-complexity remote work, where does human talent become more valuable rather than less? Categories are entered by the user; the model only classifies what it is given.
Illustrative assumptions — replace with company data
Positioning matrix
Horizontal: AI replacement risk. Vertical: human value / differentiation.
Routine data entry & processing
Automation pressure is high and buyers see little human differentiation. Expect price compression; do not build acquisition strategy on this category.
Scheduling & inbox coordination
Automation pressure is high and buyers see little human differentiation. Expect price compression; do not build acquisition strategy on this category.
Bookkeeping & month-end close
Automation pressure is high but judgment and accountability still matter. Re-price around outcomes and let tooling absorb the routine volume.
Regulated / compliance-bearing work
Low automation exposure with high human differentiation. This is where pricing power and expansion should be concentrated.
Embedded client-team operations lead
Low automation exposure with high human differentiation. This is where pricing power and expansion should be concentrated.
Work categories
Score 1–10. No claims are made about any specific staffing role beyond what is entered here.
Executive recommendation
Generated entirely from the current model state. Change any assumption and this page rewrites itself.
Illustrative recommendation generated from the current assumptions. This becomes a validated recommendation only after discovery replaces assumptions with company data.
Model-indicated architecture
- Model-indicated primary ICP
- Lower Mid-Market — score 60/100
- Model-indicated secondary ICP
- Mid-Market — score 57/100
- Model-indicated primary channel
- Referrals — 68.8x LTV:CAC, 1-month payback
- Model-indicated secondary channel
- Outbound — score 62/100
- Expected 3-year revenue
- $90.5M (91% of target)
- Gap to $100.0M
- $9.5M shortfall
- Highest-risk assumption
- Referrals capacity of 45 deals per year against required volume
- Strongest economic driver
- Referrals economics: 68.8x LTV:CAC at $2,444 CAC and 1-month payback.
- AI positioning implication
- Concentrate positioning and pricing on Regulated / compliance-bearing work, Embedded client-team operations lead. Deprioritize acquisition investment behind Routine data entry & processing, Scheduling & inbox coordination.
- Referral opportunity
- Referrals model to $1.6M of new ARR, covering 17% of the remaining gap at $4,000 CAC.
- Capacity constraint
- Required 258 annual wins exceeds entered capacity of 90 by 168 deals — roughly 23 additional heads.
Invest
- Referrals as the primary motion (score 76/100)
- Lower Mid-Market as the primary ICP (score 60/100)
- Structured referral program — 19.5x modeled ROI at $4,000 CAC
- Delivery and sales capacity: roughly 23 additional quota-carrying heads implied
Test
- Outbound as the secondary motion before committing headcount
- Mid-Market with a bounded pilot to confirm CAC and cycle length
- Outcome-based pricing for Bookkeeping & month-end close
Do not scale yet
- Inbound / Marketing-Led — 3.3x LTV:CAC and 10-month payback do not yet justify scale investment
- Upper SMB — ranked 3 of 4 on blended ICP score
- Small SMB — ranked 4 of 4 on blended ICP score
- Capacity expansion in Routine data entry & processing, Scheduling & inbox coordination while AI replacement risk stays high
The recommendation is only as strong as the assumptions. Week 1 is designed to replace assumptions with evidence.
4-week workplan
How this framework is used inside a four-week engagement: the model is the working artifact, and each week replaces another block of assumptions with evidence.
Diagnose
- Review existing GTM playbook
- Analyze customer and revenue cohorts
- Segment customers
- Analyze retention and expansion
- Review acquisition history
- Interview founders and key team members
- Establish baseline economics
- Identify missing data
Validated baseline + hypotheses to test
Pressure test
- Secret-shop competitors
- Analyze competitive positioning
- Test ICP hypotheses
- Model channel economics
- Analyze sales and marketing capacity
- Evaluate AI disruption by service category
- Identify channels that fail economically
ICP + channel economics model
Design
- Select primary and secondary acquisition motions
- Design demand-generation model
- Design referral architecture
- Define resource requirements
- Establish KPIs
- Model conservative / base / aggressive scenarios
Recommended GTM architecture
Operationalize
- Finalize investment priorities
- Define 90-day experiments
- Assign owners
- Establish decision gates
- Build KPI scorecard
- Document assumptions
- Create handoff for permanent revenue leader
Executable GTM operating plan
How do we generate more leads?
Which growth system can repeatedly acquire the right customers at economics that support a $100M company?