PriceGPT

Continuously scrapes and aggregates pricing data from competitor websites, marketplaces, and public APIs (Amazon, eBay, etc.) using web crawlers and API integrations, normalizing product matches through SKU/GTIN mapping and fuzzy product name matching. The system maintains a time-series database of competitor prices indexed by product and channel, enabling detection of price changes within hours rather than manual daily checks.

Analyzes historical sales volume and price data to estimate price elasticity (how demand changes with price) using regression models or machine learning (e.g., linear regression, gradient boosting). The model learns category-specific elasticity curves and identifies price thresholds where demand drops sharply, enabling recommendations that maximize revenue rather than just matching competitor prices.

Continuously monitors the competitive landscape, detecting new competitors entering the market for specific products or categories and alerting users to shifts in competitive intensity. Tracks competitor entry/exit, identifies emerging competitors with aggressive pricing, and segments competitors by strategy (price leader, premium, niche). Enables proactive strategy adjustments before competitive pressure becomes severe.

Synthesizes competitive pricing data, demand elasticity models, inventory levels, and cost data to generate price recommendations that maximize revenue or profit subject to business constraints (minimum margin %, max/min price bounds, channel-specific rules). Uses reinforcement learning or constraint optimization (linear programming) to balance competing objectives: staying competitive, maintaining margins, and clearing slow-moving inventory.

Automatically applies recommended prices to products across connected sales channels (e.g., Shopify, WooCommerce, Amazon, eBay) via APIs or integrations, with optional approval workflows for high-impact changes. Maintains price consistency across channels while respecting channel-specific rules (e.g., higher prices on own website, lower on marketplace). Includes rollback and audit logging to track all price changes.

Adjusts price recommendations based on inventory age, turnover rate, and stockout risk, automatically suggesting deeper discounts for slow-moving or aging inventory to avoid deadstock. Uses inventory velocity metrics (days-to-sell, turnover ratio) and demand forecasts to identify products at risk of obsolescence, then recommends aggressive pricing to clear inventory before expiration or seasonal shifts.

Visualizes competitive pricing data, price changes, and market trends over time in an interactive dashboard, enabling quick identification of pricing patterns, competitor strategies, and market shifts. Includes trend charts (price over time), heatmaps (price by competitor/channel), and alerts for significant price movements or new competitor entries. Supports filtering by product, category, competitor, and date range.

Automatically matches products across different sales channels and competitor sites using fuzzy string matching, GTIN/SKU lookup, and machine learning-based product embeddings. Handles variations in product names, descriptions, and identifiers (e.g., 'iPhone 15 Pro Max 256GB' vs. 'Apple iPhone 15 Pro Max 256GB Space Black') to ensure price comparisons are accurate. Deduplicates products in the internal database to avoid tracking the same product multiple times.

Groups products into market segments or categories and analyzes pricing patterns, elasticity, and competitive intensity at the segment level rather than individual product level. Identifies which categories are price-sensitive (high competition, low margins) vs. margin-friendly (low competition, high elasticity). Enables category-specific pricing strategies and helps identify underpriced or overpriced categories.

Simulates the expected impact of price changes on revenue, margin, and demand before applying them, using elasticity models and historical data. Supports A/B testing by applying different prices to subsets of inventory or channels and measuring actual impact on sales, enabling validation of elasticity estimates and continuous model improvement. Includes statistical significance testing to determine if observed differences are real or due to chance.

Maintains a time-series database of own prices, competitor prices, and sales data, enabling historical analysis and trend detection. Supports queries like 'show me price trends for this product over the last 6 months' or 'identify products where I've consistently underpriced vs. competitors'. Includes data retention policies and archival to manage storage costs while preserving historical context.