Our work / Case study

Amazon Sales Monitoring & Demand Planning

We automated inventory forecasting and nightly reporting for an Amazon seller—cutting overstock and reclaiming capital tied up in dead stock.

E-commerce · Retail

Data analysis dashboard for e-commerce

25%+ overstock reduction

Tighter forecast accuracy

Nightly inventory reporting

Reported outcomes from this engagement; not projected results for a new pilot.
Inventory planningExample

Stock coverage against expected demand

Seal kitsReorder soon
FiltersReview demand
CouplingsStock available

Reorder decisions in one view

Concept illustration with sample data.

Results

After rollout, the seller delivered:

  • 25%+ overstock reduction by aligning inventory to forecasted demand.
  • Fewer stockouts from timely reorder signals instead of reactive buying.
  • Lower ACoS / TACoS by cutting spend on unprofitable keywords and campaigns.
  • Clearer SKU profitability after fees and ads—so weak products got fixed or cut.
  • Hours back every week by replacing manual data pulls with overnight reporting.

The Challenge

An Amazon seller was losing margin to stockouts, overstock, and opaque ad spend. Their operation struggled with:

  • Inventory Stockouts & Overstocking: Demand swings left shelves empty or capital locked in excess inventory.
  • Inefficient Ad Spend: Campaigns ran without clear attribution or profitable keyword focus.
  • Manual Forecasting: Spreadsheets couldn't keep up with seasonality and promotions.
  • Data Silos: Sales, ads, and inventory lived in separate systems with no single source of truth.
  • Dynamic Marketplace: Competitor moves and fee changes required faster decisions than weekly manual pulls allowed.

What We Built

We delivered an integrated analytics and automation platform:

  1. Automated Data Extraction: Pulled sales, inventory, FBA, advertising, and settlements via Amazon SP-API and Advertising API.
  2. Centralized Data Hub: Consolidated streams in an Microsoft Fabric lakehouse with cleaned, analysis-ready models.
  3. Performance Dashboards: Built Power BI views for sales trends, days of cover, stockout risk, ACoS/TACoS, SKU profitability, and forecasts.
  4. Demand Forecasting: Deployed time-series models accounting for seasonality, promotions, and ad impact.
  5. Inventory Optimization: Added reorder alerts and quantity recommendations tied to forecasts and lead times.
  6. Ad Spend Analysis: Surfaced keyword and campaign ROI so budget moved to what actually converted.

Tighten Amazon inventory and ad ROI

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