A client needs to model their growth engine, understand which levers matter most, and build projections grounded in real metrics rather than wishful thinking.

---

How it works

1. You provide the client name, business model, current metrics, growth channels, and assumptions
2. The skill maps the growth loops, calculates viral and retention dynamics, models channel contributions, runs sensitivity analysis, and builds 3/6/12-month projections
3. It returns a growth model Kate can use to pressure-test a client's growth plan, set realistic targets, and identify the highest-leverage interventions

---

Prompt

You are building a growth model for a Kate Makrigiannis consulting engagement. Kate uses this to help clients move from gut-feel growth expectations to a quantitative model they can actually steer. The model should be honest about uncertainty and explicit about which assumptions drive the numbers. Before writing, read knowledge/voice-tone-guide.md -- use the client-facing voice.

Inputs I will provide:

• Client: {{CLIENT}} (company name, product, business model)
• Current metrics: {{METRICS}} (users, revenue, MRR/ARR, retention rate, churn, conversion rates, ARPU -- whatever they have)
• Growth channels: {{CHANNELS}} (how they acquire users today -- paid, organic, referral, sales, partnerships, etc.)
• Assumptions: {{ASSUMPTIONS}} (growth rate expectations, planned channel investments, pricing changes, product launches, market size)
• Context (optional): {{CONTEXT}} (stage, funding, team size, competitive dynamics, board expectations)

Step 1: Map the growth loops

Identify and diagram the client's active growth loops. Most businesses have 1-2 primary loops and several secondary ones.

Growth Loop Inventory

For each loop the business has (or should have):

Loop: [Name] (e.g., Paid Acquisition Loop, Viral/Referral Loop, Content/SEO Loop, Sales Loop)

• Trigger: What starts the loop (ad impression, user invite, content publish, outbound email)
• Steps: [Step 1] → [Step 2] → [Step 3] → [back to trigger]
• Cycle time: How long one loop iteration takes (days/weeks/months)
• Current strength: [Strong / Developing / Weak / Not yet active]
• Compounding? Does this loop accelerate over time, or is it linear?

Loop Diagram:

[Acquisition] → [Activation] → [Retention] → [Referral/Revenue] ─┐
      ↑                                                            │
      └────────────────────────────────────────────────────────────┘

Draw a loop diagram for each active loop, showing where value feeds back into acquisition.

Step 2: Viral coefficient calculation

If the client has any referral or word-of-mouth channel, calculate the viral coefficient (K-factor):

Viral Dynamics

• Invitations per user (i): X (average invites/shares per active user per [time period])
• Conversion rate of invitations (c): X% (what percentage of invited people become users)
• K-factor = i x c: X

Show the math: "K = {{i}} invitations x {{c}}% conversion = {{K}}"

Interpretation:

• K > 1.0: True viral growth (each user brings in more than one new user). Rare and powerful.
• K = 0.5-1.0: Viral amplification. Not self-sustaining, but meaningfully reduces CAC.
• K < 0.5: Minimal viral contribution. Growth depends on paid/organic channels.
• K = 0 or unknown: No referral mechanism. Flag as an opportunity.

Viral cycle time: X days (how long from user activation to their referral converting). Shorter cycle times amplify growth dramatically, even at the same K-factor.

Step 3: Retention curve analysis

Model how the client's user base retains over time. This is the foundation of the growth model: acquisition without retention is a leaky bucket.

Retention Model

Cohort Period	Users Remaining	Retention Rate	Notes
Month 0	X (100%)	100%	Acquisition cohort
Month 1	X	X%	[Early drop-off assessment]
Month 3	X	X%	[Activation quality signal]
Month 6	X	X%	[Product-market fit signal]
Month 12	X	X%	[Long-term retention floor]

If the client has cohort data, use it. If not, estimate from their stated churn rate and flag: [ASSUMPTION: retention curve estimated from stated monthly churn of X% -- actual cohort analysis may reveal different patterns (e.g., early churn concentrated in first 30 days)]

Churn and retention math:

• Monthly churn rate = X%
• Monthly retention rate = 100% - churn = X%
• Annual retention = (monthly retention)^12 = X%. Show the calculation.
• Average customer lifetime = 1 / monthly churn rate = X months

Retention quality assessment:

• Flattening curve: Retention stabilizes after initial drop-off. Good sign for PMF.
• Continuous decay: Retention keeps declining with no floor. Warning sign.
• Smile curve: Retention dips then recovers (common with habit-forming products). Strong signal.

Step 4: Channel contribution model

Break down how each growth channel contributes to new user/customer acquisition.

Channel Mix

Channel	Monthly Volume	CAC	% of New Users	Scalability	Payback Period
[Channel 1]	X users	$X	X%	[Linear / Compounding / Saturating]	X months
[Channel 2]	X users	$X	X%	[Linear / Compounding / Saturating]	X months
[Referral]	X users	$X (or $0)	X%	[Depends on K-factor]	Immediate
Total	X users	$X blended	100%	--	X months blended

Payback period calculation: Payback = CAC / (ARPU x Gross Margin). Show the math for each channel.

Channel scalability assessment:

• Linear: Paid channels. More spend = more users, but CAC tends to rise with scale.
• Compounding: Content, SEO, community. Slow start but accelerating returns over time.
• Saturating: Finite audience channels (specific communities, niche platforms). Will hit a ceiling.

Step 5: Sensitivity analysis

Identify the 3-5 variables that have the most impact on growth outcomes and model what happens when they change.

Key Lever Sensitivity

Lever	Base Case	Downside (-20%)	Upside (+20%)	Impact on 12-Month Revenue
Monthly new users	X	X	X	$X / $X
Activation rate	X%	X%	X%	$X / $X
Monthly retention	X%	X%	X%	$X / $X
ARPU	$X	$X	$X	$X / $X
Viral coefficient	X	X	X	$X / $X

Show the math for each sensitivity scenario. For example: "If monthly retention improves from 92% to 95%, 12-month retention moves from 38% to 54%, increasing average lifetime from 12.5 to 20 months and LTV from $X to $X."

Highest-leverage lever: [Identify which single variable, if improved by 20%, produces the largest revenue impact.] This is where the client should focus optimization effort.

Step 6: Growth projections

Build month-by-month projections for 3, 6, and 12 months with three scenarios.

12-Month Growth Projection

Assumptions for each scenario:

• Conservative: Current trends continue with no optimization. Channels grow at X%/month.
• Base case: Planned optimizations succeed at 50% of expected impact. One new channel comes online.
• Aggressive: All optimizations hit full targets. Two new channels contribute. Viral coefficient improves.

Month	Conservative Users	Base Users	Aggressive Users	Conservative MRR	Base MRR	Aggressive MRR
Current	X	X	X	$X	$X	$X
Month 3	X	X	X	$X	$X	$X
Month 6	X	X	X	$X	$X	$X
Month 12	X	X	X	$X	$X	$X

Show the projection math for at least Month 3 in the base case:

• New users from channels: X (Channel 1) + X (Channel 2) + X (Referral) = X
• Retained from prior months: X existing users x X% retention = X
• Total users Month 3: X new + X retained = X
• MRR: X users x $X ARPU = $X

Milestone Check

Milestone	Conservative	Base Case	Aggressive
1,000 users (or relevant milestone)	Month X	Month X	Month X
$X MRR	Month X	Month X	Month X
[Client's stated target]	Month X / Not reached	Month X	Month X

Kate's Talking Points

• The growth model's biggest risk: the single assumption that, if wrong, breaks the plan
• The quick win: which lever has the highest sensitivity with the lowest effort to improve
• The strategic question: is the client's growth plan realistic given retention dynamics, or are they planning to fill a leaky bucket?
• If projections fall short of board/investor expectations, what specifically needs to change

For defining the North Star Metric this model should optimize toward, use /north-star-metric. For evaluating product-led growth readiness, use /plg-readiness-check. For diagnosing specific funnel stages within this model, use /funnel-analysis.

---

[SDR Outreach] → [Demo] → [Activation] → [Expansion (seats)] ──┐
      ↑                                                          │
      └──────────── [Referral to peer practice] ←───────────────┘