PredictIQ
An e-commerce company with 2,000+ products was guessing what to reorder and when. We built a system that predicts demand 90 days ahead, spots customers about to leave, and recommends exactly what to stock. Stockouts dropped 62% in the first quarter.
The Problem
An e-commerce company with 2,000+ SKUs made inventory and marketing decisions on gut feeling and basic spreadsheet analysis. They consistently over-ordered slow movers and ran out of best sellers. Seasonal trends were spotted too late to act on. Marketing spend had no data-driven allocation.
What We Built
PredictIQ turns historical business data into actionable forecasts with a live dashboard.
Key Features
- Demand Forecasting: predicts product-level demand at 30, 60, and 90-day horizons using time series analysis and seasonal decomposition.
- Churn Prediction: identifies at-risk customers based on purchase frequency, recency, and engagement patterns. Flags them before they leave.
- Inventory Optimization: recommends reorder quantities and timing based on lead times, demand forecasts, and safety stock calculations.
- Marketing Attribution: tracks which channels and campaigns drive highest lifetime value customers, not just first purchases.
- Anomaly Detection: alerts on unusual spikes or drops in sales, returns, or traffic before they become problems.
- Live Dashboard: real-time Plotly visualizations accessible via web browser. No BI tool subscription needed.
Data Pipeline
Data ingestion from Shopify, Google Analytics, and email platform via API. Nightly batch processing handles cleaning, normalization, and feature engineering. Models retrain weekly on latest data. Monthly accuracy review and recalibration.
Results
Prediction accuracy reached 94% within the first quarter. Stockout incidents dropped 62%. Marketing CAC reduced 28% through attribution-driven reallocation. Decision-making time for inventory and campaigns cut by 70%.
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