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Consumer Goods

Driving Growth for a Major FMCG Brand in India

AI-powered analytics platform optimized retail operations, inventory management, and promotional strategies, boosting recall, precision, and sales performance across India’s diverse markets.

80% recall, 50% precision, 85% coverage, 50% precision, 10% lines increase

Improvement

The Problem

A leading FMCG brand in India needed to increase lines per store sales and retain consistently selling SKUs while optimizing promotional spending across 500,000 retail outlets.

Key Challenges

  • Boost incremental sales by increasing the number of product lines per store.
  • Ensure consistent availability of high-performing SKUs to strengthen loyalty.
  • Optimize incentive schemes and payouts to reduce promotional wastage.
  • Segment stores effectively across diverse Indian markets (urban vs rural, kirana vs supermarkets).
  • Aggregate store-level forecasts into regional/state-level targets aligned with growth objectives.

Our Solution

Deployed an AI-powered analytics platform integrating predictive modeling, store-level inventory optimization, incentive scheme uplift modeling, and targeted forecasting for growth alignment.

  • AI-Powered Analytics Platform
  • Predictive Modeling
  • Uplift Modeling
  • Retail Data Integration

Implementation Approach

  1. Store-Level Inventory Recommendations: forecasted demand at SKU level for high-margin products like shampoos, detergents, and packaged foods.
  2. Upselling Opportunities: identified cross-sell and upsell SKUs using historical trade and sales data.
  3. Store Segmentation: categorized outlets by sales growth, geography, and consumer behavior patterns.
  4. Incentive Scheme Optimization: used uplift modeling to evaluate scheme impact and reduce promotional overspend.
  5. Final Target Setting: rolled up store-level forecasts to regional and state levels for strategic alignment.

Results & Impact

  • 500,000 outlets optimized across India with sales recommendations for 200+ FMCG products.
  • Increased hit rate by 30%, ensuring stronger performance of targeted SKUs.
  • Increased lines per store by ~10%, driving higher product diversity and sales.
  • Reduced promotional wastage by aligning offers with true demand patterns.