product-led-growth

Name: product-led-growth
Author: beingsmit

39stars7forks

How to install product-led-growth?

beingsmit/technical-product-gtm/product-led-growth

$npx -y skills add beingsmit/technical-product-gtm --skill product-led-growth

Installs into the current project.

Summary

Build self-serve acquisition and expansion motions. Use when deciding PLG vs sales-led, optimizing activation, driving freemium conversion, building growth equations, or recognizing when product complexity demands human touch. Includes the parallel test where sales-led won 10x on

Files · 1

View on GitHub

SKILL.md

1	# Product-Led Growth
2
3	Build self-serve acquisition and expansion motions. But first, figure out if PLG is even the right motion for your product.
4
5	## When to Use
6
7	Triggers:
8	- "Should we build PLG or sales-led?"
9	- "How do we drive self-serve adoption?"
10	- "Freemium to paid conversion isn't working"
11	- "Developer-led adoption strategy"
12	- "Which growth channels should we invest in?"
13	- "How do I know if PLG will work?"
14
15	Context:
16	- Developer tools and platforms
17	- B2B SaaS with self-serve potential
18	- Products where value is obvious without demo
19	- Bottom-up adoption motions
20	- Growth channel prioritization
21
22	---
23
24	## Core Frameworks
25
26	### 1. The PLG Reality Check (Test Before You Commit)
27
28	What I Learned Running Both Motions in Parallel:
29
30	Classic startup debate. PLG camp: "Developers want self-serve." Sales camp: "Enterprises need hand-holding." Instead of arguing, we tested both for 6 months. Same product, two GTM motions, tracked everything.
31
32	The Results:
33
34	PLG: High volume, low ACV (~$5K), fast time-to-revenue, higher churn. Sales-led: Lower volume, high ACV (~$50K), slower time-to-revenue, lower churn. Sales won 10x on dollars despite 10x less volume.
35
36	Why: Product complexity + buyer seniority = sales-led wins. The product required integration with existing infrastructure, change management across teams, and multi-stakeholder alignment. Developers loved self-serve. But they weren't the economic buyer.
37
38	PLG works when:
39	- Value is obvious in first 5 minutes
40	- Implementation is trivial
41	- Individual user gets value without team buy-in
42	- No procurement/legal hurdles
43	- Buyer = user
44
45	Sales-led works when:
46	- Product requires integration/setup
47	- Multiple stakeholders need alignment
48	- Buyer ≠ user
49	- Deal size justifies human touch
50	- Customer needs education to see value
51
52	Before building PLG, test your motion. Don't assume PLG is better because it's trendy. PLG is efficient at volume, but sales-led can be more profitable with complexity.
53
54	---
55
56	### 2. The Growth Equation (Map Inputs to Outputs)
57
58	The Pattern:
59
60	Growth compounds when you systematize the relationship between activities and user acquisition. Not "do more marketing" — map specific inputs to measurable outputs.
61
62	How to Build Your Growth Equation:
63
64	For each channel, define: Activity (input) → Traffic (output) → Conversions.
65
66	- Organic Search: 1 quality blog post → 400 users/month → 5% conversion = 20 new users
67	- Paid Ads: $1K spend at 8% conversion on 100K impressions = 8K clicks → conversions at X%
68	- Community Events: 1 event → 60 attendees → 35% conversion = 21 users
69	- Referral: 1 integration partner → N referred users → conversions at Y%
70
71	Why This Matters:
72
73	Once you validate the equation, scaling becomes math. "I need 200 more users next month" → "I need 10 more blog posts" or "I need $5K more ad spend." Without the equation, you're guessing.
74
75	Testing the Equation:
76
77	1. Start with hypothesis: "If I create X, it drives Y conversion"
78	2. Test with small sample: 1 blog post, measure actual conversion
79	3. Validate: Does reality match hypothesis?
80	4. Scale with confidence: If yes, increase input
81	5. Kill if not: 4 weeks of data is enough to decide
82
83	Common Mistake:
84
85	Guessing at conversion rates without testing. Assuming all users from the same channel are equal quality. Scaling before validating the equation.
86
87	---
88
89	### 3. Channel Economics (Kill Losers, Double Down on Winners)
90
91	The Pattern:
92
93	Every channel has economics. Without tracking them, you over-invest in losers and under-invest in winners.
94
95	Track Per Channel:
96	1. CAC: Total spend / new users
97	2. Conversion rate: Signups → paying
98	3. Retention: 30-day, 90-day by source
99	4. LTV: Revenue over customer lifetime, by channel
100	5. Payback period: How long to recoup CAC
101
102	The Decision Framework:
103
104	- CAC < (LTV × margin) → Scale aggressively
105	- CAC ≈ (LTV × margin) → Optimize, don't scale
106	- CAC > (LTV × margin) → Kill within 4 weeks
107
108	Monthly channel review: Which channels are profitable? Which are drains? Quarterly reallocation: 3x budget to winners, kill losers.
109
110	Critical Insight: Channel Quality Varies
111
112	Cheap CAC doesn't mean good CAC. Organic search might deliver users at $0 CAC with 85% 30-day retention. Paid search might deliver users at $12 CAC with 45% 30-day retention. The "free" channel is 10x more valuable when you factor in retention and LTV.
113
114	Systematic Testing:
115
116	Test 2 new channels monthly. Give each 4 weeks of data. Kill decisively if economics don't work. Document lear

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product-led-growth

39stars7forks

View on GitHub

How to install product-led-growth?

beingsmit/technical-product-gtm/product-led-growth

$npx -y skills add beingsmit/technical-product-gtm --skill product-led-growth

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Product-Led Growth
2
3	Build self-serve acquisition and expansion motions. But first, figure out if PLG is even the right motion for your product.
4
5	## When to Use
6
7	Triggers:
8	- "Should we build PLG or sales-led?"
9	- "How do we drive self-serve adoption?"
10	- "Freemium to paid conversion isn't working"
11	- "Developer-led adoption strategy"
12	- "Which growth channels should we invest in?"
13	- "How do I know if PLG will work?"
14
15	Context:
16	- Developer tools and platforms
17	- B2B SaaS with self-serve potential
18	- Products where value is obvious without demo
19	- Bottom-up adoption motions
20	- Growth channel prioritization
21
22	---
23
24	## Core Frameworks
25
26	### 1. The PLG Reality Check (Test Before You Commit)
27
28	What I Learned Running Both Motions in Parallel:
29
30	Classic startup debate. PLG camp: "Developers want self-serve." Sales camp: "Enterprises need hand-holding." Instead of arguing, we tested both for 6 months. Same product, two GTM motions, tracked everything.
31
32	The Results:
33
34	PLG: High volume, low ACV (~$5K), fast time-to-revenue, higher churn. Sales-led: Lower volume, high ACV (~$50K), slower time-to-revenue, lower churn. Sales won 10x on dollars despite 10x less volume.
35
36	Why: Product complexity + buyer seniority = sales-led wins. The product required integration with existing infrastructure, change management across teams, and multi-stakeholder alignment. Developers loved self-serve. But they weren't the economic buyer.
37
38	PLG works when:
39	- Value is obvious in first 5 minutes
40	- Implementation is trivial
41	- Individual user gets value without team buy-in
42	- No procurement/legal hurdles
43	- Buyer = user
44
45	Sales-led works when:
46	- Product requires integration/setup
47	- Multiple stakeholders need alignment
48	- Buyer ≠ user
49	- Deal size justifies human touch
50	- Customer needs education to see value
51
52	Before building PLG, test your motion. Don't assume PLG is better because it's trendy. PLG is efficient at volume, but sales-led can be more profitable with complexity.
53
54	---
55
56	### 2. The Growth Equation (Map Inputs to Outputs)
57
58	The Pattern:
59
60	Growth compounds when you systematize the relationship between activities and user acquisition. Not "do more marketing" — map specific inputs to measurable outputs.
61
62	How to Build Your Growth Equation:
63
64	For each channel, define: Activity (input) → Traffic (output) → Conversions.
65
66	- Organic Search: 1 quality blog post → 400 users/month → 5% conversion = 20 new users
67	- Paid Ads: $1K spend at 8% conversion on 100K impressions = 8K clicks → conversions at X%
68	- Community Events: 1 event → 60 attendees → 35% conversion = 21 users
69	- Referral: 1 integration partner → N referred users → conversions at Y%
70
71	Why This Matters:
72
73	Once you validate the equation, scaling becomes math. "I need 200 more users next month" → "I need 10 more blog posts" or "I need $5K more ad spend." Without the equation, you're guessing.
74
75	Testing the Equation:
76
77	1. Start with hypothesis: "If I create X, it drives Y conversion"
78	2. Test with small sample: 1 blog post, measure actual conversion
79	3. Validate: Does reality match hypothesis?
80	4. Scale with confidence: If yes, increase input
81	5. Kill if not: 4 weeks of data is enough to decide
82
83	Common Mistake:
84
85	Guessing at conversion rates without testing. Assuming all users from the same channel are equal quality. Scaling before validating the equation.
86
87	---
88
89	### 3. Channel Economics (Kill Losers, Double Down on Winners)
90
91	The Pattern:
92
93	Every channel has economics. Without tracking them, you over-invest in losers and under-invest in winners.
94
95	Track Per Channel:
96	1. CAC: Total spend / new users
97	2. Conversion rate: Signups → paying
98	3. Retention: 30-day, 90-day by source
99	4. LTV: Revenue over customer lifetime, by channel
100	5. Payback period: How long to recoup CAC
101
102	The Decision Framework:
103
104	- CAC < (LTV × margin) → Scale aggressively
105	- CAC ≈ (LTV × margin) → Optimize, don't scale
106	- CAC > (LTV × margin) → Kill within 4 weeks
107
108	Monthly channel review: Which channels are profitable? Which are drains? Quarterly reallocation: 3x budget to winners, kill losers.
109
110	Critical Insight: Channel Quality Varies
111
112	Cheap CAC doesn't mean good CAC. Organic search might deliver users at $0 CAC with 85% 30-day retention. Paid search might deliver users at $12 CAC with 45% 30-day retention. The "free" channel is 10x more valuable when you factor in retention and LTV.
113
114	Systematic Testing:
115
116	Test 2 new channels monthly. Give each 4 weeks of data. Kill decisively if economics don't work. Document lear

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6 picks

Type