Pricing Analytics AI Prompts

Analyze the effectiveness of discounts and promotions and optimize the promotional strategy. Promotion data: {{promotion_data}} (promotion type, discount %, products, period, orders, revenue) Baseline data: {{baseline_data}} (same period last year or non-promotional baseline) 1. Promotion performance metrics: For each promotion: - Revenue during promotion vs baseline (lift) - Gross margin during promotion vs baseline (margin impact) - Orders count and AOV during vs baseline - New vs returning customer mix during promotion 2. Revenue vs margin trade-off: - Revenue lift (%) from promotion - Gross profit change (%) from promotion (may be negative even if revenue is up) - Break-even volume: how much incremental volume is needed to offset the margin give-away? Break-even lift = Discount % / (Gross Margin % - Discount %) Example: 20% discount on a 40% margin product requires 100% volume increase to maintain gross profit 3. Promotion types comparison: - % off: drives broadest participation, margins most exposed - BOGO (Buy One Get One): volume driver, effective for high-margin products - Free shipping threshold: AOV driver (set threshold at 30% above current AOV) - Gift with purchase: drives specific product trial, protects revenue - Flash sale (time-limited): urgency driver, but trains customers to wait for discounts 4. Customer behavior post-promotion: - Are customers acquired during promotions at lower LTV than non-promotion customers? - Do promotional buyers return at similar or lower rates than full-price buyers? - Are existing customers simply shifting their planned purchases to discount periods? 5. Discount addiction signals: - Is there a trend toward increasing discount frequency to maintain revenue? - What % of revenue is generated at a discount? - Are customers waiting for promotions before purchasing? (Signal: conversion rate declines in weeks before promotions) 6. Optimized promotional calendar: - Based on analysis: which promotion types drive the best net margin impact? - Recommended promotional cadence: how often and at what depth? - Products that should never be promoted (inelastic, premium positioning) Return: promotion performance table, break-even analysis, type comparison, post-promotion behavior analysis, discount addiction signals, and promotional calendar recommendation.

Design a data-driven dynamic pricing framework for this e-commerce business. Product catalog: {{catalog}} Demand data: {{demand_data}} Competitor pricing feed: {{competitor_prices}} (if available) Objective: {{objective}} (maximize revenue, maximize margin, protect market share) 1. Dynamic pricing applicability: Not all products are suitable for dynamic pricing. Score each category: - High suitability: commodity products, seasonal products, high price sensitivity, active competitor pricing changes - Low suitability: branded products, luxury, items where price stability builds trust 2. Demand-based pricing rules: Adjust price based on current demand signals: - High demand (sell-through rate > target): small price increase to protect margin - Low demand (sell-through rate < target): small price decrease to drive velocity - Near out-of-stock: price increase to ration remaining inventory - Overstock: price decrease to accelerate sell-through 3. Competitive pricing rules (if competitor data available): - Price matching rule: stay within 5% of the lowest verified competitor price for A-category products - Price leadership rule: for unique or differentiated products, ignore competitor pricing - Floor price: never go below the minimum margin threshold (cost + minimum margin %) 4. Time-based pricing: - Day-of-week elasticity: are there purchase patterns that suggest different price sensitivity by day? - Seasonal pricing: pre-season vs peak season vs clearance pricing for each category - End-of-season markdown schedule: structured markdown cadence as season ends 5. Guardrails and controls: - Maximum price change per day: ± 10% to avoid customer perception issues - Price floor per SKU: ensures margin is never destroyed - Minimum price stability: some products (premium, gift) should not fluctuate frequently - Human review threshold: any suggested price change > 20% requires human approval 6. Testing and measurement: - A/B test dynamic pricing vs static: randomize products into test and control - Measure: revenue per visit, gross margin %, sell-through rate - Be aware: customers on the same product may see different prices (manage perception risk) Return: product category suitability matrix, demand-based and competitive pricing rules, time-based strategy, guardrails, and testing framework.

Analyze price sensitivity and identify optimal pricing for key products. Sales and pricing data: {{pricing_data}} (product, price, units_sold, date) Promotion history: {{promotion_data}} Competitor pricing: {{competitor_prices}} (if available) 1. Price elasticity estimation: Price Elasticity of Demand (PED) = % change in quantity / % change in price - PED < -1: elastic demand (price decrease drives proportionally more volume) - PED > -1: inelastic demand (price changes have limited volume effect) - PED = -1: unit elastic Estimation approach: - Collect price change events (price increases, promotions, sales) for each product - Compare volume before and after each price change - Control for seasonality by using same period YoY or a holdout comparison 2. Revenue-maximizing price: At the estimated elasticity, the revenue-maximizing price is: Optimal Price = Marginal Cost / (1 + 1/PED) - Calculate for products with reliable elasticity estimates - Compare optimal price to current price: are products over or under-priced? 3. Margin-impact analysis: - If we raise price by 5%: at what elasticity does revenue decrease? - If revenue decreases but volume decreases proportionally less: gross profit may still increase - Calculate: (Price lift) x (Volume loss) = Net margin impact at different elasticity assumptions 4. Promotional discount analysis: - Which products historically show the highest volume lift from promotions? - What discount depth is needed to move volume without destroying margin? - Products where discount lift is < 20% volume increase: promotions destroy margin without compensating volume 5. Competitive pricing analysis: - Price index: our price / lowest competitor price per product - Products where we are more than 15% above the lowest competitor price and showing low conversion - Products where we are below average market price: potential for modest price increase 6. Pricing recommendations: - Products to test a price increase on (inelastic, below optimal price) - Products to review pricing strategy (elastic, above optimal price) - Promotional calendar recommendations based on elasticity Return: elasticity estimates per product, revenue-maximizing price calculations, margin impact analysis, discount effectiveness, and pricing recommendations.