Pricing Elasticity Analysis Prompt
Analyze how price changes affect demand for your product using elasticity calculations, sensitivity modeling, and revenue optimization recommendations.
Prompt Template
You are a pricing analytics expert. Help me analyze the price elasticity of demand for my product and find the revenue-maximizing price point. Product details: - Product: [product name and type] - Current price: $[price] - Current monthly units sold: [volume] - Current monthly revenue: $[revenue] - Market: [B2B/B2C/DTC], [industry] - Pricing model: [one-time / subscription / usage-based] Historical pricing data (if available): | Date | Price | Units Sold | Revenue | Notes | |------|-------|-----------|---------|-------| | [date] | $[price] | [units] | $[rev] | [any context] | | [date] | $[price] | [units] | $[rev] | [any context] | | [date] | $[price] | [units] | $[rev] | [any context] | Competitor pricing: - [Competitor 1]: $[price] for [what's included] - [Competitor 2]: $[price] for [what's included] - [Competitor 3]: $[price] for [what's included] Please provide: 1. **Elasticity Calculation** — Calculate the price elasticity of demand from my historical data. Classify as elastic (>1), inelastic (<1), or unit elastic (=1). 2. **Revenue Sensitivity Model** — Create a table showing projected revenue at 5 different price points (2 below current, current, 2 above current) with estimated volume impact. 3. **Optimal Price Range** — Based on the elasticity analysis, identify the revenue-maximizing price range and the profit-maximizing price range (accounting for estimated margins). 4. **Competitive Position Map** — Where I sit relative to competitors on a price-value matrix and what that implies for pricing power. 5. **Price Change Recommendations** — Specific recommendation with confidence level, expected revenue impact, and risk factors. 6. **Testing Strategy** — How to validate the recommendation with an A/B pricing test (cohort design, duration, sample size, success metrics). 7. **Implementation Playbook** — If raising prices: grandfather existing customers? Phase in gradually? Change packaging to justify the increase?
Example Output
# Pricing Elasticity Analysis: TaskFlow Pro (Project Management SaaS)
1. Elasticity Calculation
Using your historical data:
| Period | Price | Units | % Price Change | % Quantity Change |
|--------|-------|-------|----------------|-------------------|
| Q1 2025 | $29/mo | 2,400 | — | — |
| Q2 2025 | $34/mo | 2,180 | +17.2% | -9.2% |
| Q3 2025 | $34/mo | 2,250 | 0% | +3.2% (organic growth) |
| Q4 2025 | $39/mo | 1,980 | +14.7% | -12.0% |
Price Elasticity of Demand:
- Q1→Q2: PED = -9.2% ÷ 17.2% = **-0.53** (inelastic)
- Q3→Q4: PED = -12.0% ÷ 14.7% = **-0.82** (inelastic, but approaching unit elastic)
Interpretation: Your product is inelastic — a 10% price increase results in only a 5-8% volume decrease. This is typical for B2B SaaS with high switching costs. You likely have room to raise prices.
2. Revenue Sensitivity Model
| Price Point | Est. Monthly Units | Monthly Revenue | vs. Current | Confidence |
|-------------|-------------------|----------------|-------------|------------|
| $29 (historical) | 2,500 | $72,500 | -5.8% | High |
| $34 (below current) | 2,250 | $76,500 | -0.7% | High |
| **$39 (current)** | **1,980** | **$77,220** | **Baseline** | **Actual** |
| $44 (test) | 1,780 | $78,320 | **+1.4%** | Medium |
| $49 (stretch) | 1,550 | $75,950 | -1.6% | Low |
**Revenue-maximizing price point: ~$44/mo** (+$1,100/mo revenue)
3. Optimal Price Range
- **Revenue-maximizing:** $42-$46/mo
- **Profit-maximizing:** $44-$49/mo (higher price = fewer support tickets per dollar of revenue)
- **Risk-adjusted sweet spot:** **$44/mo** — captures upside with manageable churn risk
5. Recommendation
**Raise price from $39 to $44/mo** (13% increase)
- **Confidence:** Medium-High (7/10)
- **Expected revenue impact:** +$1,100/mo (+$13,200/year)
- **Expected churn:** 8-12% of price-sensitive customers over 60 days
- **Net impact after churn:** Still positive (+$800-1,100/mo net)
Risk factors:
1. Competitor X just launched at $35/mo — price-sensitive segment has an exit ramp
2. Annual contract customers locked in won't feel increase until renewal
3. If economy softens, elasticity could increase (become more price-sensitive)
6. Testing Strategy
A/B Price Test Design:
- **Cohort A (control):** New signups see $39/mo (50%)
- **Cohort B (test):** New signups see $44/mo (50%)
- **Duration:** 8 weeks minimum
- **Sample size needed:** ~400 per cohort (for 95% confidence, 80% power)
- **Primary metric:** Revenue per visitor (not just conversion rate)
- **Secondary metrics:** Trial-to-paid conversion, 30-day retention, support ticket volume
Tips for Best Results
- 💡Never calculate elasticity from a single price change — you need at least 3 data points to identify a pattern vs. a coincidence.
- 💡B2B SaaS is almost always inelastic (PED 0.3-0.8) because switching costs are high. If yours is elastic, you have a positioning problem, not a pricing problem.
- 💡Always A/B test price changes on new customers first — never surprise existing customers with a sudden increase.
- 💡When raising prices, add something (even small: priority support, a new feature) so it feels like an upgrade, not a tax.
Related Prompts
Dataset Summary and Insights
Paste or describe a dataset and get an instant summary of key statistics, patterns, anomalies, and actionable insights.
SQL Query Writer for Business Reports
Generate SQL queries for common business reporting needs — revenue trends, cohort analysis, funnel metrics, and more.
Dashboard KPI Definition Framework
Define the right KPIs for your business dashboard with clear formulas, targets, and data sources.