ncreighton Overview
The Pricing Psychology & Positioning Auditor is a comprehensive analysis skill that transforms pricing decisions from guesswork into data-driven strategy. It systematically reviews competitor pricing pages, your current positioning, customer behavior signals (support tickets, refund reasons, feature requests), and market benchmarks to recommend actionable changes.
Why This Matters
Pricing is the easiest lever to pull for revenue growth—a 1% price increase often yields 7-25% profit increase. Yet most companies make pricing decisions based on gut feel or cost-plus formulas. This skill eliminates that risk by surfacing:
- Willingness-to-pay signals hidden in customer support data
- Psychological price anchoring tactics competitors use
- Tier structure inefficiencies that leave money on the table
- Bundling opportunities that increase average order value
- Churn risk factors from price sensitivity analysis
It integrates with Stripe, Intercom, Zendesk, Google Sheets, and Slack to pull real data about your customers, then synthesizes competitive intelligence (via web_search) and behavioral economics frameworks to generate A/B test templates and price recommendations you can execute immediately.
Quick Start
Try these prompts to get immediate value:
Prompt 1: Basic Competitor Pricing Audit
Analyze the pricing pages of HubSpot, Pipedrive, and Close CRM.
Compare their tier names, positioning language, anchoring tactics,
and bundling strategies. What psychological principles does each use?
Output a comparison table with recommendations for my B2B SaaS pricing.
Prompt 2: Customer Willingness-to-Pay Analysis
I'll share my last 50 Zendesk support tickets and 30 refund reasons.
Identify patterns in price sensitivity, feature requests tied to tier limits,
and willingness-to-pay signals. Recommend tier structure changes and
price point adjustments that reduce churn without sacrificing MRR.
Prompt 3: A/B Test Design Framework
Generate a 4-week A/B test framework for my SaaS pricing.
Include: (1) null/alternative hypotheses, (2) segment targeting,
(3) control vs. test pricing variants, (4) success metrics,
(5) statistical significance thresholds, (6) Slack alerts for
real-time monitoring. Format for Optimizely and Google Analytics.
Prompt 4: Psychological Anchoring Strategy
My current pricing is $99/$299/$999/month. Redesign tier positioning
using: charm pricing, left-digit anchoring, decoy pricing, and bundling
psychology. Show before/after positioning language, visual hierarchy,
and expected impact on conversion rate and average revenue per user.
Capabilities
1. Competitor Pricing Intelligence
- Automated web scraping of competitor pricing pages (up to 10 competitors)
- Tier extraction: Names, features, price points, annual discounts
- Psychological tactic identification: Anchoring, scarcity, social proof language
- Bundling analysis: Feature combinations, add-on pricing, package deals
- Output: Competitive pricing matrix with benchmark percentiles
Example:
Competitor: Salesforce
- Tier 1: "Starter" ($25/user/mo) — anchors low, positions for entry
- Tier 2: "Professional" ($75/user/mo) — most popular (70% of seats)
- Tier 3: "Enterprise" (custom) — anchors with exclusivity
- Anchoring tactic: Uses "Industry Standard" language on Professional tier
- Bundling: All tiers include email, calls, forecast (basic features)
Enterprise adds Einstein AI (premium signal)
- Recommended action: Your Tier 2 is underpriced vs. Salesforce Professional
2. Willingness-to-Pay Signal Detection
- Support ticket analysis: Extracts price objections, feature requests, upgrade blockers
- Refund reason mining: Identifies price sensitivity vs. feature fit issues
- Feature request correlation: Links feature requests to tier levels (can you increase price by adding this feature?)
- Churn prediction signals: Detects early warning signs (support volume spikes, downgrade requests)
- Integration: Connects to Zendesk, Intercom, HubSpot Service Hub via API
Example:
Analysis of 50 refund requests:
- 24% cite "price too high relative to value" (price sensitivity)
- 18% request missing features in their tier (upgrade opportunity)
- 12% cite competitor switching (competitive risk)
- 8% cite lack of integrations (bundling gap)
Willingness-to-pay insight: Moving "API access" from Enterprise to
Professional tier would unlock $12K/month ARR (based on 40 churned
customers who requested it). Risk: Tier cannibalization <2% based on
adoption patterns.
3. Market Benchmark Analysis
- Industry pricing ranges for your product category (SaaS, e-commerce, marketplaces, etc.)
- Price-to-value positioning: Where you sit vs. premium/budget competitors
- Elasticity modeling: Estimates revenue impact of ±5%, ±10%, ±20% price changes
- Segment pricing: Per-industry, per-company-size, per-geography recommendations
- Data source: Combines G2, Capterra, public filings, industry reports
Example:
Your category: Project Management SaaS
- Market average: $45-$120/user/month
- Your current: $99/user/month (68th percentile)
- Premium tier average: $400-$600/month flat
- Your premium positioning: Weak (priced like competitor, no differentiation)
Recommendation: Increase Professional tier to $149 (matches Asana,
positions as premium). Impact model: 8% conversion rate decrease,
but 50% price increase = +38% revenue growth. Churn risk: <1% based
on competitor switching patterns.
4. Tier Structure & Bundling Optimization
- Feature matrix analysis: Which features drive upgrades vs. which are table-stakes?
- Bundling recommendations: What features should be packaged together?
- Tier naming psychology: Reframe names using aspiration/status language
- Price point optimization: Recommend specific numbers using charm pricing and anchoring
- Decoy pricing strategy: When to add a strategic "bad deal" tier to drive conversions to target tier
Example Output:
BEFORE:
Tier 1: Free ($0) — Too much free value
Tier 2: Pro ($99) — Name is generic
Tier 3: Enterprise ($999) — Price jump is too high
AFTER:
Tier 1: Starter ($0 → $29/mo) — Convert free users, anchors to paid
Tier 2: Pro ($99 → $149/mo) — Reposition as "Scale" (aspiration language)
Tier 3: Enterprise ($999 → $399/mo) — Reduce jump, add "Teams" positioning
Tier 4: [NEW] Premium ($249/mo) — Decoy tier drives conversions to Scale
Expected impact: +28% revenue (price + tier migration), +12% conversion rate
5. A/B Test Framework Generation
- Hypothesis formulation: Pre-formatted null/alternative hypotheses
- Segment targeting: Who to test with (new vs. existing, by company size, by product usage)
- Variant design: Control vs. 2-4 test variants (price, tier names, positioning language)
- Metrics specification: Primary (conversion rate, ARR), secondary (churn, feature adoption)
- Statistical power calculation: Sample size needed, duration, significance threshold
- Integration templates: Ready-to-deploy configs for Optimizely, Google Optimize, VWO, or custom scripts
- Monitoring setup: Slack alerts for statistical significance, alerts for anomalies
Example:
TEST: Charm Pricing vs. Round Pricing
Hypothesis: $149/mo (charm) drives higher conversion than $150/mo
Control: $150/month tier
Variant: $149/month tier (same features)
Segment: New sign-ups only (avoiding existing customer bias)
Duration: 2 weeks minimum (target 500 conversions per variant)
Primary metric: Conversion rate (trials to paid)
Secondary metrics: Churn rate (30-day), NPS, feature adoption
Slack alert:
- Trigger when p-value <0.05
- Alert if conversion rate <3% (technical issue)
- Daily digest of conversion rates by variant
Expected result: 0.5-2% conversion lift from charm pricing
(based on behavioral econ literature for B2B SaaS)
6. Psychological Price Anchoring Tactics
Recommends specific, evidence-based tactics:
- Left-digit anchoring: "From $1/mo" vs. "$10/mo" impacts perception
- Decoy pricing: Add a bad-value tier to make target tier look better
- Charm pricing: $99 vs. $100 (tested in B2B, 1-2% lift typical)
- Prestige pricing: Higher price for premium tier signals quality
- Bundling illusion: Combine features to increase perceived value
- Annual discount positioning: Show as "40% savings" not "65% yearly price"
- Social proof anchoring: "Join 5,000+ companies using our Pro tier"
Example:
Your current positioning: "Pro Plan - $99/month"
Reframed with anchoring:
"Pro Plan — From $99/month — Join 4,200+ growing companies"
(left-digit anchor + social proof + aspiration)
Landing page changes:
- Highlight savings: "Save 40% annually" (not "$1,188/year")
- Feature positioning: "Everything in Starter, plus:"
- Decoy tier: Add $199 "Pro+" (slight increase) to make Pro look better
- Social proof: Customer logos on pricing page (48% conversion lift)
Expected lift: 8-15% conversion rate improvement from framing alone
Configuration
Required Environment Variables
# OpenAI API for analysis and recommendations
export OPENAI_API_KEY="sk-..."
# Google Sheets API for storing analysis results and benchmarks
export GOOGLE_SHEETS_API_KEY="AIza..."
# Optional integrations (for data pulling)
export STRIPE_API_KEY="sk_live_..."
export ZENDESK_API_KEY="your_api_key"
export INTERCOM_API_KEY="dG9rOmFkZGNk..."
export SLACK_WEBHOOK_URL="https://hooks.slack.com/services/T00000/B00000/..."
Setup Steps
Create a Google Sheet for results storage (templates provided):
- Competitor Pricing Matrix
- Willingness-to-Pay Analysis
- A/B Test Results Tracker
- Pricing Recommendation Roadmap
Connect your data sources:
- Export Zendesk tickets as CSV (last 30-90 days)
- Pull refund reasons from Stripe dashboard
- Provide your current pricing page URL
Set analysis scope:
- Number of competitors to analyze (2-10 recommended)
- Customer data time window (30/60/90 days)
- Analysis depth (quick/standard/comprehensive)
Example Outputs
Output 1: Competitor Pricing Matrix
┌─────────────┬──────────┬──────────┬──────────┬──────────────┐
│ Competitor │ Tier 1 │ Tier 2 │ Tier 3 │ Anchoring │
├─────────────┼──────────┼──────────┼──────────┼──────────────┤
│ HubSpot │ $0 │ $50/mo │ $120/mo │ Free trial │
│ Pipedrive │ $14/mo │ $39/mo │ $99/mo │ Low anchor │
│ Close │ $29/mo │ $59/mo │ $99/mo │ Charm prices │
│ Your SaaS │ $0 │ $99/mo │ $999/mo │ High jump │
│ Benchmark │ $0-15 │ $40-80 │ $150+ │ [See gaps] │
└─────────────┴──────────┴──────────┴──────────┴──────────────┘
Recommendation: Your Tier 1 → Tier 2 jump is too large (4.9x).
Competitors average 2.8x. Add mid-tier or reduce Tier 2 to $59/mo.
Output 2: Willingness-to-Pay Report
CUSTOMER BEHAVIOR SIGNALS (Last 60 days)
Price Objection Rate: 24% of refunds
- Trend: +8% vs. previous quarter
- Risk level: MEDIUM (indicates market saturation or feature gap)
Feature Request Patterns:
- API access (12 requests, 30% from Enterprise tier) → Upsell opportunity
- SSO/SAML (8 requests, 50% from Mid-market) → Premium feature candidate
- Custom workflows (15 requests, 40% churn) → Add to Pro tier
Churn Prediction:
- Support ticket spike 3 weeks before churn (90% accuracy)
- Price objections + feature requests = 2.5x churn likelihood
- At-risk accounts: 7 (estimated $8.4K ARR exposure)
Recommended Actions (Priority Order):
1. Add API access to Pro tier → Expected recovery: $12K ARR, churn reduction: 3%
2. Increase Professional tier pricing to $149 → Revenue lift: +23%, churn risk: <1%
3. Create "Enterprise+" tier at $499/mo for teams tier → New segment: $15K+ ARR
Output 3: A/B Test Design (Ready-to-Deploy)
test_name: "Professional Tier Pricing Optimization"
hypothesis: "Increasing Professional tier from $99 to $149/mo
(with benefit reframing) will increase conversion rate by 8-12%
while reducing churn by <1%."
variants:
control:
price: "$99/month"
name: "Professional"
tagline: "For growing teams"
test_a:
price: "$149/month"
name: "Scale"
tagline: "For teams ready to grow"
change: "Rename tier, reposition as aspiration tier"
test_b:
price: "$149/month"
name: "Professional Plus"
tagline: "Everything in Starter, plus Advanced Analytics"
change: "Add feature anchoring to justify price"
targeting:
segment: "New sign-ups only"
traffic_allocation: "33% control / 33% test_a / 34% test_b"
minimum_duration: "14 days"
sample_size_per_variant: "500 conversions"
success_metrics:
primary:
- name: "Conversion Rate (Trial to Paid)"
expected_lift: "8-12%"
minimum_acceptable_change: "3%"
statistical_significance: "p < 0.05"
secondary:
- name: "30-day Churn Rate"
acceptable_increase: "<1%"
- name: "Pro tier adoption rate"
baseline: "35%"
monitoring:
slack_webhook: "https://hooks.slack.com/services/..."
daily_report: true
alert_on:
- "p-value crossing 0.05 threshold"
- "conversion rate <3% (tech issue)"
- "any variant with churn >8%"
deployment:
platform: "Optimizely"
pages: ["pricing", "checkout