Case Studies
    Automation & AI

    AI-Powered Analytics Platform

    A custom analytics platform unifying demand forecasting and store scorecards for a multi-location retail chain.

    Industry: RetailService: Data & AI
    -35%
    Stock-out Rate
    9%
    Forecast Error (WAPE)
    4x
    Decision Speed
    6.4M TRY
    Annual Impact

    We consolidated store, e-commerce and CRM data into a single warehouse and shipped a decision platform that tracks demand forecasts and campaign impact in real time.

    The Challenge

    Data was fragmented across systems. Forecasts lived in spreadsheets, campaign decisions took weeks to validate, and stock-outs co-existed with over-ordering.

    Our Approach

    • Inventory of data sources (POS, ERP, e-commerce, CRM) and shared data dictionary.
    • Baseline demand forecasting at store × SKU × week granularity.
    • MLOps pipeline: model versioning, monitoring and feedback loops.
    • User interviews and KPI simplification workshop for the decision panel.

    The Solution

    • Central data warehouse on BigQuery with dbt transformations.
    • Prophet + gradient boosting ensemble for demand forecasting.
    • Role-based Looker Studio dashboards (store manager, category, finance).
    • Automated anomaly alerts via Slack and email.

    Results

    • 35% reduction in stock-out rate.
    • Forecast error (WAPE) down from 18% to 9%.
    • Campaign decisions from ~12 days to 3 days.
    • Annual operational uplift around 6.4M TRY.
    "Category meetings now start with data, not guesses. Our decision rhythm fundamentally changed."
    — Category Director, Retail Chain

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