businessproduct-management

Pricing Analysis

Conduct pricing analysis — evaluating competitive pricing, willingness-to-pay, packaging options, and revenue impact modeling to produce pricing recommendations with supporting data.

pricinganalysiscompetitivewillingness-to-payrevenue-modeling

Works well with agents

Pricing Strategist Agent VP of Product Agent

Works well with skills

Experiment Design Metrics Framework

pricing-analysis/

SKILL.md

Markdown

1
2	# Pricing Analysis
3
4	## Before you start
5
6	Gather the following from the user. If anything is missing, ask before proceeding:
7
8	1. What are you pricing? (New product, repricing, add-on feature, new tier)
9	2. Who is the target customer? (Persona, company size, budget authority)
10	3. What is the current pricing? (If repricing — model, tiers, average deal size)
11	4. Who are the competitors? (Direct competitors and public pricing, indirect alternatives)
12	5. What is the value metric? (What unit the customer pays for — seats, usage, projects)
13	6. Do you have willingness-to-pay data? (Surveys, win/loss data, sales feedback, churn reasons)
14	7. What are the business constraints? (Margin requirements, revenue targets, positioning)
15
16	If the user says "just tell me what to charge," push back: pricing without data on customer value perception, competitive landscape, and unit economics is guessing. This analysis produces a recommendation grounded in evidence.
17
18	## Pricing analysis template
19
20	### 1. Value Metric Assessment
21
22	Identify the unit of value that aligns price with customer outcomes. The right value metric scales with the value the customer receives.
23
24	```
25	\| Candidate Metric \| Aligns with Value? \| Predictable Cost? \| Recommendation \|
26	\|------------------\|--------------------\|-------------------\|--------------------\|
27	\| Per seat/user \| Moderate \| Yes \| Good default \|
28	\| Per API call \| High \| Low (spiky) \| Consider caps \|
29	\| Per project \| High \| Yes \| Strong for SMB \|
30	\| Flat rate \| Low \| Yes \| Only if homogeneous\|
31	\| Per GB stored \| Moderate \| Moderate \| Common for infra \|
32	```
33
34	Selection criteria: The metric should scale with customer value (paying 10x = getting ~10x value). Customers must be able to predict their cost before committing. The metric must be explainable in one sentence — if sales cannot articulate it, deals stall.
35
36	### 2. Competitive Pricing Landscape
37
38	Map competitor pricing. Include direct competitors and the "do nothing" alternative.
39
40	```
41	\| Competitor \| Model \| Entry Price \| Mid-Tier \| Differentiator \|
42	\|------------------\|----------------\|-------------\|-------------\|-------------------------\|
43	\| Competitor A \| Per seat/month \| $29/seat \| $79/seat \| Market leader, full suite\|
44	\| Competitor B \| Usage-based \| Free tier \| $0.01/req \| Developer-focused \|
45	\| Competitor C \| Flat rate \| $199/month \| $499/month \| All-inclusive, simple \|
46	\| Open-source alt. \| Self-hosted \| $0 (+ ops) \| $0 (+ ops) \| Free but costly to run \|
47	\| Status quo \| Manual \| Staff time \| Staff time \| "Free" but slow \|
48	```
49
50	Note where competitors cluster and where gaps exist. Gaps can signal differentiation opportunities.
51
52	### 3. Willingness-to-Pay Analysis
53
54	Use available data to estimate price sensitivity.
55
56	```
57	\| Data Source \| Method \| Finding \|
58	\|---------------------\|-------------------------------------\|----------------------------------\|
59	\| Van Westendorp \| Survey: too cheap/cheap/expensive \| Acceptable range: $35-$75/user/mo\|
60	\| Win/loss analysis \| CRM data on deals won vs. lost \| Lost on price: 18% of losses \|
61	\| Sales feedback \| AE interviews (n=8) \| Price rarely an issue below $60 \|
62	\| Churn analysis \| Exit survey + cancellation data \| Price cited in 22% of churns \|
63	```
64
65	If WTP data is unavailable, flag this as a risk. Without it, any price recommendation is a hypothesis.
66
67	### 4. Packaging and Tier Design
68
69	Design tiers serving distinct segments with clear upgrade triggers.
70
71	```
72	\| Tier \| Target Segment \| Price \| Upgrade Trigger \|
73	\|------------\|-------------------\|----------------\|------------------------------------\|
74	\| Free \| Individual devs \| $0 \| Need collaboration or >3 projects \|
75	\| Team \| Small teams (5-20)\| $49/user/month \| Need SSO, audit logs, analytics \|
76	\| Business \| Mid-market (20-100)\| $89/user/month\| Need SLAs, dedicated support \|
77	\| Enterprise \| Large orgs (100+) \| Custom \| N/A — top tier \|
78	```
79
80	Rules: Each tier needs a distinct target customer, not just more features at higher price. Upgrade triggers should be natural growth inflection points. Free tiers must demonstrate real value while creating conversion pressure. Limit to 4 tiers maximum.
81
82	### 5. Revenue Impact Model
83
84	Model the financial impact of the proposed pricing against alternatives.
85
86	```
87	\| Scenario \| Avg Price \| Conv. Rate \| Customers (Y1) \| ARR (Y1) \| Notes \|
88	\|--------------------\|-----------\|------------\|-----------------\|----------\|-----------------------\|
89	\| Current pricing \| $39/user \| 8% \| 400 \| $780K \| Baseline \|
90	\| Proposed pricing \| $49/user \| 7% \| 350 \| $857K \| +10% ARR, -12% volume \|
91	\| Aggressive pricing \| $69/user \| 5% \| 250 \| $863K \| High churn risk \|
92	```
93
94	Model at least 3 scenarios. For each, estimate impact on acquisition, conversion, and churn. Highlight assumptions explicitly.
95
96	### 6. Recommendation
97
98	Synthesize findings into a clear recommendation with rationale, risks, and next steps.
99
100	```
101	Recommended pricing: $49/user/month (Team), $89/user/month (Business)
102	Value metric: Per seat, monthly billing with annual discount (20%)
103	Rationale: Within WTP range, 25% below Competitor A, +10% ARR vs. current
104	Risk: Conversion rate assumption needs validation
105	Next step: Pricing experiment on 10% of new signups for 4 weeks
106	```
107
108	## Quality checklist
109
110	Before delivering a pricing analysis, verify:
111
112	- [ ] Value metric is identified with rationale — not just defaulting to "per seat"
113	- [ ] Competitive landscape includes at least 3 competitors and the "do nothing" alternative
114	- [ ] Willingness-to-pay uses real data, or gaps in data are explicitly flagged as risks
115	- [ ] Tiers have distinct target segments and natural upgrade triggers
116	- [ ] Revenue impact model includes at least 3 scenarios with explicit assumptions
117	- [ ] Recommendation includes rationale, risks, and a validation plan
118	- [ ] Pricing aligns with positioning — premium pricing for a budget positioning is contradictory
119	- [ ] The analysis addresses both acquisition (new customer) and retention (existing customer) impact
120
121	## Common mistakes to avoid
122
123	- Cost-plus pricing. Customers pay for outcomes, not your infrastructure bill. Cost sets the floor — value sets the price.
124	- Copying competitor pricing. Matching a competitor assumes identical value proposition and cost structure. Price based on your value, positioned relative to competitors.
125	- Too many tiers. Three tiers plus enterprise custom is the proven pattern. More creates decision paralysis.
126	- Artificial limitations on free tiers. Crippling free tiers breeds resentment. Free should deliver real value — paid should deliver meaningfully more.
127	- Ignoring existing customers during repricing. A price increase that churns 20% of existing customers is a net loss. Model retention impact.
128	- No validation plan. Run pricing experiments on a subset first. Launching to 100% on day one is an all-or-nothing bet.
129