Competitive Price Optimization: Spreadsheet to AI Agent

AI Agents with LasaAI · Intermediate ·📊 Data Analytics & Business Intelligence ·3mo ago

About this lesson

How a pricing analyst at a mid-size ecommerce retailer replaces five-hour competitor spreadsheets with automated, margin-safe SKU-level recommendations and projected revenue impact. What you'll see in this demo: - Competitor price collection from live pages including JavaScript-rendered pricing - SKU-level recommendations with margin floor enforcement (22% minimum) - Elasticity modeling using four quarters of sales history with seasonal adjustments - Projected revenue lift per product ($1,321 total across 5 SKUs) - Constraint validation: max increase/decrease limits, .99 price endings, margin floors Who this is for: Pricing analysts, revenue managers, and ecommerce category managers who manually track competitor prices across multiple retailers and want margin-safe, SKU-level pricing recommendations with full rationale and revenue projections. Timestamps: 0:00 -- Full pricing recommendations matrix built automatically 0:15 -- Five SKUs, three competitors each, and the Tuesday morning grind 0:35 -- Competitor prices change mid-analysis, spreadsheets go stale 0:55 -- Why formulas, integrations, and dashboards fall short 1:15 -- Live competitor data to margin-safe recommendations in one pass 1:35 -- Executive summary, pricing matrix, and competitive position analysis 2:00 -- Generic pricing template vs. tailored SKU-level recommendations 2:20 -- See what competitive price optimization looks like for your catalog FAQ: Q: How does the AI agent handle pricing constraints like margin floors and price endings? A: Every recommendation is validated against the full constraint set before it appears in the report. The agent enforces the margin floor (22% in this example), maximum price increase (15%) and decrease (25%) limits, and price ending rules (.99). If a recommendation would violate any constraint, the agent adjusts the price to the nearest compliant value. Q: Can this work for industries beyond ecommerce product pricing? A: The same structure applies to any competi

Original Description

How a pricing analyst at a mid-size ecommerce retailer replaces five-hour competitor spreadsheets with automated, margin-safe SKU-level recommendations and projected revenue impact. What you'll see in this demo: - Competitor price collection from live pages including JavaScript-rendered pricing - SKU-level recommendations with margin floor enforcement (22% minimum) - Elasticity modeling using four quarters of sales history with seasonal adjustments - Projected revenue lift per product ($1,321 total across 5 SKUs) - Constraint validation: max increase/decrease limits, .99 price endings, margin floors Who this is for: Pricing analysts, revenue managers, and ecommerce category managers who manually track competitor prices across multiple retailers and want margin-safe, SKU-level pricing recommendations with full rationale and revenue projections. Timestamps: 0:00 -- Full pricing recommendations matrix built automatically 0:15 -- Five SKUs, three competitors each, and the Tuesday morning grind 0:35 -- Competitor prices change mid-analysis, spreadsheets go stale 0:55 -- Why formulas, integrations, and dashboards fall short 1:15 -- Live competitor data to margin-safe recommendations in one pass 1:35 -- Executive summary, pricing matrix, and competitive position analysis 2:00 -- Generic pricing template vs. tailored SKU-level recommendations 2:20 -- See what competitive price optimization looks like for your catalog FAQ: Q: How does the AI agent handle pricing constraints like margin floors and price endings? A: Every recommendation is validated against the full constraint set before it appears in the report. The agent enforces the margin floor (22% in this example), maximum price increase (15%) and decrease (25%) limits, and price ending rules (.99). If a recommendation would violate any constraint, the agent adjusts the price to the nearest compliant value. Q: Can this work for industries beyond ecommerce product pricing? A: The same structure applies to any competi

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Chapters (8)

Full pricing recommendations matrix built automatically

0:15 Five SKUs, three competitors each, and the Tuesday morning grind

0:35 Competitor prices change mid-analysis, spreadsheets go stale

0:55 Why formulas, integrations, and dashboards fall short

1:15 Live competitor data to margin-safe recommendations in one pass

1:35 Executive summary, pricing matrix, and competitive position analysis

2:00 Generic pricing template vs. tailored SKU-level recommendations

2:20 See what competitive price optimization looks like for your catalog

6-Phase SQL Roadmap 2026 | Data Analytics & Engineering | #shorts