acquisition-channel-advisor▌
deanpeters/product-manager-skills · updated Apr 8, 2026
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Evaluate acquisition channels by unit economics, customer quality, and scalability to decide whether to scale, test, or kill.
- ›Assesses three core dimensions: unit economics (CAC, LTV, payback period, LTV:CAC ratio), customer quality (retention, churn, NRR by channel), and scalability (magic number, addressable volume, CAC trends)
- ›Delivers one of four recommendations: scale aggressively (LTV:CAC >3:1, payback <12mo), test and optimize (marginal economics with fixable problems), kill
Purpose
Guide product managers through evaluating whether to scale, test, or kill an acquisition channel based on unit economics (CAC, LTV, payback), customer quality (retention, NRR), and scalability (magic number, volume potential). Use this to make data-driven go-to-market decisions and optimize channel mix for sustainable growth.
This is not a channel strategy framework—it's a financial lens for channel evaluation that helps you avoid scaling unprofitable channels or killing channels with fixable problems. Use when deciding how to allocate marketing budget across channels.
Key Concepts
The Channel Evaluation Framework
A systematic approach to evaluate acquisition channels:
-
Unit Economics — What does it cost to acquire, and what's the return?
- CAC (Customer Acquisition Cost)
- LTV (Lifetime Value)
- LTV:CAC ratio
- Payback period
-
Customer Quality — Do customers from this channel stick around and expand?
- Cohort retention rate (by channel)
- Churn rate (by channel)
- NRR (Net Revenue Retention by channel)
- Expansion rate
-
Scalability — Can this channel sustain growth at the volume you need?
- Magic Number (S&M efficiency)
- Addressable volume (TAM of channel)
- Saturation risk (diminishing returns)
- CAC trend (increasing, stable, decreasing)
-
Strategic Fit — Does this channel align with your go-to-market strategy?
- Customer segment match (SMB vs. enterprise)
- Sales motion compatibility (PLG vs. sales-led)
- Brand positioning alignment
Decision Matrix
| LTV:CAC | Payback | Customer Quality | Scalability | Decision |
|---|---|---|---|---|
| >3:1 | <12mo | Good retention | High volume | Scale aggressively |
| 2-3:1 | 12-18mo | Average retention | Medium volume | Test & optimize |
| <2:1 | >18mo | Poor retention | Low volume | Kill or fix |
Anti-Patterns (What This Is NOT)
- Not vanity metrics: "We got 10,000 signups!" means nothing if they churn in 30 days
- Not CAC-only thinking: Low CAC with terrible retention is worse than high CAC with great retention
- Not ignoring payback: 5:1 LTV:CAC with 36-month payback is a cash trap
- Not scaling broken channels: Pouring money into inefficient channels accelerates failure
When to Use This Framework
Use this when:
- Evaluating whether to scale a new channel (content, paid, events, etc.)
- Deciding how to allocate marketing budget across channels
- Assessing whether to kill an underperforming channel
- Comparing channels to optimize ROI
- Planning annual marketing budget allocation
Don't use this when:
- Channel is brand-new (<3 months, <100 customers) — not enough data
- You're testing channel fit (this is for evaluation, not experimentation)
- Strategic channels (e.g., enterprises require field sales regardless of CAC)
- You don't have channel-level data (need to track CAC, retention by source)
Facilitation Source of Truth
Use workshop-facilitation as the default interaction protocol for this skill.
It defines:
- session heads-up + entry mode (Guided, Context dump, Best guess)
- one-question turns with plain-language prompts
- progress labels (for example, Context Qx/8 and Scoring Qx/5)
- interruption handling and pause/resume behavior
- numbered recommendations at decision points
- quick-select numbered response options for regular questions (include
Other (specify)when useful)
This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.
Application
This interactive skill asks up to 4 adaptive questions, offering 3-5 enumerated options at decision points.
Step 0: Gather Context
Agent asks:
"Let's evaluate this acquisition channel. Please provide:
Channel details:
- Channel name (e.g., Google Ads, content marketing, outbound sales, partnerships)
- How long have you been using this channel? (months)
- Current monthly spend on this channel
Customer acquisition:
- Customers acquired per month (from this channel)
- CAC for this channel (if known, otherwise we'll calculate)
Business context:
- Blended CAC (across all channels)
- Blended LTV
- Current MRR/ARR
- Target growth rate (% MoM or YoY)
You can provide estimates if you don't have exact numbers."
Step 1: Evaluate Unit Economics
Agent calculates (if not provided):
CAC = Monthly Spend / Customers Acquired per Month
Agent asks:
"Now let's compare this channel's unit economics to your blended metrics.
Channel Unit Economics:
- Channel CAC: $___
- Blended CAC (all channels): $___
- Channel LTV: $___ (if known; otherwise we'll use blended LTV as proxy)
- Blended LTV: $___
Questions:
-
Do customers from this channel have similar LTV to other channels?
- Similar (use blended LTV)
- Higher (they upgrade more, stick around longer)
- Lower (they churn faster or are smaller deals)
- Unknown (need to analyze cohort data)
-
What's the payback period for this channel?
- We can calculate: CAC / (Monthly ARPU × Gross Margin %)
- Or you can provide it"
Based on answers, agent calculates:
- LTV:CAC ratio for channel
- Payback period
- Comparison to blended metrics
Agent flags:
- ✅ If LTV:CAC >3:1 and payback <12 months: "Strong unit economics"
- ⚠️ If LTV:CAC 2-3:1 or payback 12-18 months: "Marginal unit economics"
- 🚨 If LTV:CAC <2:1 or payback >18 months: "Poor unit economics"
Step 2: Assess Customer Quality
Agent asks:
"How do customers from this channel perform compared to other channels?
Retention & Expansion:
-
What's the churn rate for customers from this channel?
- Lower than blended (they stick around longer)
- Same as blended (no difference)
- Higher than blended (they churn faster)
- Unknown (need cohort analysis)
-
What's the NRR for customers from this channel?
- Higher than blended (they expand more)
- Same as blended (no difference)
- Lower than blended (they contract or churn more)
- Unknown (need cohort analysis)
-
What's the customer profile from this channel?
- Ideal customer profile (ICP) — perfect fit
- Close to ICP — mostly good fit
- Off ICP — many poor-fit customers
- Unknown"
Based on answers, agent evaluates:
- ✅ High quality: Lower churn, higher NRR, ICP match
- ⚠️ Medium quality: Similar to blended, mostly good fit
- 🚨 Low quality: Higher churn, lower NRR, off ICP
Agent flags:
- If high quality: "Premium channel—customers are better than average"
- If low quality: "Quality problem—customers aren't sticking or expanding"
Step 3: Evaluate Scalability
Agent asks:
"Can this channel scale to meet your growth targets?
Efficiency & Volume:
-
What's the S&M efficiency for this channel (Magic Number)?
- Calculate: (New MRR from channel × 4) / Channel S&M Spend
- Or provide if known
-
What's the addressable volume for this channel?
- Large (can scale 10x+ from current spend)
- Medium (can scale 2-5x)
- Small (near saturation, maybe 1.5x)
- Unknown
-
What's the CAC trend for this channel?
- Decreasing (getting more efficient over time)
- Stable (consistent CAC)
- Increasing (diminishing returns, saturation)
- Unknown (too early to tell)
-
How much growth do you need from acquisition?
- We'll calculate: Target growth - expansion/retention growth = acquisition gap"
Based on answers, agent evaluates:
- ✅ Highly scalable: Magic number >0.75, large volume, stable/decreasing CAC
- ⚠️ Moderately scalable: Magic number 0.5-0.75, medium volume, stable CAC
- 🚨 Not scalable: Magic number <0.5, small volume, increasing CAC
Step 4: Deliver Recommendations
Agent synthesizes:
- Unit economics (LTV:CAC, payback)
- Customer quality (retention, NRR, ICP fit)
- Scalability (magic number, volume, CAC trend)
- Strategic fit
Agent offers 3-4 recommendations:
Recommendation Pattern 1: Scale Aggressively
When:
- LTV:CAC >3:1 AND
- Payback <12 months AND
- Customer quality good or better AND
- Magic Number >0.75 AND
- Addressable volume large
Recommendation:
"Scale this channel aggressively — Excellent economics + scalability
Unit Economics:
- CAC: $___
- LTV: $___
- LTV:CAC: ___:1 ✅ (>3:1 threshold)
- Payback: ___ months ✅ (<12 months)
Customer Quality:
- Retention: [Better than / Same as / Worse than] blended
- NRR: [Higher / Same / Lower]
- ICP Fit: [High / Medium / Low]
Scalability:
- Magic Number: ___ ✅ (>0.75 = efficient)
- Addressable Volume: Large
- CAC Trend: [Stable / Decreasing]
Why this is a winner:
- Every $1 spent returns $__ in LTV
- Payback in under a year = fast cash recovery
- [Customer quality insight]
- Can scale 5-10x from current spend
How to scale:
- Increase budget by 50-100% next month
- Current: $___ /month → Target: $___ /month
- Monitor key metrics weekly:
- CAC (should stay <$___)
- Magic Number (should stay >0.75)
- Customer quality (retention, NRR)
- Scale until:
- CAC increases >20% (saturation signal)
- Magic Number drops <0.75 (efficiency declining)
- Volume caps out
Expected impact:
- Current: ___ customers/month
- Target (2x spend): ___ customers/month
- MRR impact: +$___/month
- Payback: Still ~___ months even at 2x scale
Risk: Low. Strong unit economics support aggressive scaling."
Recommendation Pattern 2: Test & Optimize
When:
- LTV:CAC 2-3:1 OR
- Payback 12-18 months OR
- Customer quality average OR
- Magic Number 0.5-0.75
Recommendation:
"Test & optimize before scaling — Marginal economics, fixable
Current State:
- CAC: $___
- LTV: $___
- LTV:CAC: ___:1 ⚠️ (2-3:1 = marginal)
- Payback: ___ months ⚠️ (12-18 months)
- Magic Number: ___ ⚠️ (0.5-0.75 = acceptable, not great)
Customer Quality:
- Retention: [Same as blended / Slightly worse]
- NRR: [Same / Lower]
- Issue: [Specific problem, e.g., "Higher churn in first 90 days"]
Diagnosis: [One of these:]
- High CAC: Spending too much to acquire
- Low LTV: Customers churn too fast or don't expand
- Poor targeting: Attracting off-ICP customers
- Inefficient conversion: High cost-per-click but low conversion rate
How to optimize:
If CAC is the problem:
- Improve conversion rate (optimize landing pages, offer, onboarding)
- Reduce cost-per-click (better targeting, ad creative)
- Shorten sales cycle (faster qualification, better demos)
If LTV is the problem:
- Improve onboarding for customers from this channel
- Target higher-value segments within channel
- Add expansion plays (upsell, cross-sell)
If targeting is the problem:
- Narrow audience (exclude poor-fit segments)
- Improve messaging (attract better-fit customers)
- Add qualification step (reduce poor-fit signups)
Timeline:
- Spend 4-8 weeks optimizing
- Track CAC and LTV weekly
- Target: LTV:CAC >3:1, payback <12 months
- If you hit targets: scale
- If you can't fix it: consider killing
Don't scale yet: Current economics are break-even at best. Fix first, then scale."
Recommendation Pattern 3: Kill or Pause
When:
- LTV:CAC <1.5:1 AND
- No clear path to improvement
Recommendation:
"Kill this channel (or pause) — Economics don't support investment
Why:
- CAC: $___
- LTV: $___
- LTV:CAC: ___:1 🚨 (<2:1 = unsustainable)
- Payback: ___ months 🚨 (>18 months = cash trap)
Problem:
- You're spending $___ to acquire a customer worth $___
- [Losing money / Barely breaking even / Taking too long to recover cost]
Customer Quality:
- Retention: [Worse than blended]
- NRR: [Lower]
- ICP Fit: [Poor]
What's broken: [Specific diagnosis:]
- CAC too high (spending $___ vs. blended $___)
- LTV too low (customers churn at ___% vs. blended ___%)
- Both (bad unit economics from both sides)
Should you fix or kill?
Fix if:
- You have a hypothesis to improve CAC by 50%+ (better targeting, conversion)
- You have a hypothesis to improve LTV by 50%+ (better onboarding, ICP focus)
- This is a strategically important channel (e.g., enterprise requires field sales)
Kill if:
- No clear path to 3:1 LTV:CAC
- Better channels available (reallocate budget there)
- Small addressable volume (not worth fixing)
Recommendation: Kill and reallocate budget
Reallocate to:
- Channel X (LTV:CAC = ___:1, can scale)
- Channel Y (Magic Number = ___, efficient)
What to do with budget:
- Current channel spend: $___/month
- Reallocate to [top-performing channel]
- Expected impact: [better CAC, better LTV, faster payback]
Exception: If this channel is <10% of total S&M spend, just pause it. Not worth fixing."
Recommendation Pattern 4: Invest to Learn (Strategic Channel)
When:
- Poor unit economics BUT
- Strategic importance (enterprise channel, brand building, long-term)
Recommendation:
"Continue, but cap investment — Strategic value > short-term ROI
Financial Reality:
- CAC: $___
- LTV: $___
- LTV:CAC: ___:1 (below 3:1 threshold)
- Payback: ___ months (long)
Why continue despite poor economics:
- [Strategic reason: e.g., "Enterprise segment requires field events, but deals are 12-month sales cycles"]
- [Brand building: e.g., "Conferences build brand awareness that drives inbound long-term"]
- [Market positioning: e.g., "Need to be present in this channel for credibility"]
How to manage:
- Cap spend — Don't scale until economics improve
- Current: $___/month
- Cap at: $___/month (hold steady)
- Track leading indicators — Don't just look at short-term CAC/LTV
- Pipeline influence
- Brand awareness lift
- Referral rate from this channel
- Re-evaluate quarterly
- If economics improve (LTV:CAC >3:1): scale
- If economics stay poor: reconsider strategy
Timeline:
- Give it [6-12 months] to show results
- If no improvement: kill or reduce drastically
Risk: You're subsidizing growth. Make sure it's worth it."
Step 5: Compare Across Channels (Optional)
If user has multiple channels, agent can generate:
| Channel | CAC | LTV | LTV:CAC | Payback | Magic Number | Quality | Recommendation |
|---|---|---|---|---|---|---|---|
| Google Ads | $500 | $2,000 | 4:1 | 8mo | 0.9 | High | Scale |
| Content | $200 | $1,500 | 7.5:1 | 4mo | 1.2 | High | Scale |
| Outbound | $10K | $50K | 5:1 | 18mo | 0.6 | Medium | Optimize |
| Events | $15K | $30K | 2:1 | 24mo | 0.3 | Low | Kill |
Budget allocation recommendation:
- Scale: Content (highest efficiency)
- Scale: Google Ads (strong economics)
- Optimize: Outbound (improve magic number)
- Kill: Events (reallocate budget)
Examples
See examples/ folder for sample conversation flows. Mini examples below:
Example 1: Scale (Content Marketing)
Channel: Organic content (blog, SEO)
- CAC: $200
- LTV: $3,000
- LTV:CAC: 15:1
- Payback: 3 months
- Magic Number: 1.8
- Customer quality: High (lower churn, higher NRR)
Recommendation: Scale aggressively. Exceptional unit economics, fast payback, high-quality customers. Increase content spend 2-3x.
Example 2: Optimize (Paid Search)
Channel: Google Ads
- CAC: $800
- LTV: $2,000
- LTV:CAC: 2.5:1
- Payback: 14 months
- Magic Number: 0.6
- Customer quality: Lower (higher churn in first 90 days)
Recommendation: Test & optimize before scaling. CAC is high, onboarding is weak for this segment. Improve landing page, target higher-intent keywords, better onboarding for paid customers.
Example 3: Kill (Trade Shows)
Channel: Industry events
- CAC: $20,000
- LTV: $30,000
- LTV:CAC: 1.5:1
- Payback: 30 months
- Magic Number: 0.2
- Customer quality: Low (off-ICP, many tire-kickers)
Recommendation: Kill. CAC too high, payback too long, poor customer quality. Reallocate budget to content and paid search.
Common Pitfalls
Pitfall 1: Scaling Broken Channels
Symptom: "Let's 10x our Google Ads spend!" (LTV:CAC is 1.5:1)
Consequence: You accelerate cash burn without improving unit economics. Lose money faster.
Fix: Only scale channels with LTV:CAC >3:1 and payback <12 months. Fix broken channels before scaling.
Pitfall 2: Ignoring Customer Quality
Symptom: "CAC is only $100!" (but customers churn in 30 days)
Consequence: Low CAC means nothing if LTV is also low. You're acquiring churners, not customers.
Fix: Track cohort retention and NRR by channel. Low CAC + high churn = bad channel.
Pitfall 3: Celebrating Vanity Metrics
Symptom: "We got 10,000 signups from this campaign!" (5% convert to paid)
Consequence: Signups don't pay bills. CAC is calculated on paid customers, not signups.
Fix: Track CAC on paid customers only. Ignore vanity metrics like signups, impressions, clicks.
Pitfall 4: Averaging Across Channels
Symptom: "Blended CAC is $500" (but hiding that one channel is $10K CAC)
Consequence: Bad channels hide in blended metrics. You don't know which channels to kill.
Fix: Track CAC, LTV, payback by channel. Compare channels individually.
Pitfall 5: Short-Term CAC Optimization
Symptom: "We reduced CAC 50%!" (by targeting low-intent, low-LTV customers)
Consequence: CAC dropped but so did LTV. Unit economics got worse, not better.
Fix: Optimize for LTV:CAC ratio, not CAC alone. Higher CAC with higher LTV is better.
Pitfall 6: Ignoring Payback Period
Symptom: "LTV:CAC is 6:1, this channel is amazing!" (payback is 48 months)
Consequence: You run out of cash before recovering CAC. Great ratio, terrible cash flow.
Fix: Pair LTV:CAC with payback period. 3:1 with 8-month payback beats 6:1 with 36-month payback.
Pitfall 7: Killing Channels Too Early
Symptom: "This channel didn't work after 2 weeks"
Consequence: Channels need time to optimize. Killing too early wastes learning.
Fix: Give channels 3-6 months and 100+ customers before evaluating. Track trends, not snapshots.
How to use acquisition-channel-advisor on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add acquisition-channel-advisor
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches acquisition-channel-advisor from GitHub repository deanpeters/product-manager-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate acquisition-channel-advisor. Access the skill through slash commands (e.g., /acquisition-channel-advisor) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★60 reviews- ★★★★★Dhruvi Jain· Dec 16, 2024
acquisition-channel-advisor is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Mateo Okafor· Dec 8, 2024
Keeps context tight: acquisition-channel-advisor is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aditi Chawla· Dec 8, 2024
Registry listing for acquisition-channel-advisor matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Lucas Gill· Dec 4, 2024
Useful defaults in acquisition-channel-advisor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sofia Sanchez· Nov 27, 2024
acquisition-channel-advisor has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Mia Flores· Nov 27, 2024
acquisition-channel-advisor reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Valentina Garcia· Nov 27, 2024
I recommend acquisition-channel-advisor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Isabella Yang· Nov 23, 2024
acquisition-channel-advisor is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Oshnikdeep· Nov 7, 2024
Useful defaults in acquisition-channel-advisor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Lucas Johnson· Nov 7, 2024
Solid pick for teams standardizing on skills: acquisition-channel-advisor is focused, and the summary matches what you get after install.
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