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Oil and Lubricants

Store Recommendation System for Lubricant Brand

AI-powered analytics platform integrating diverse data sources and advanced models to drive store-level inventory recommendations, upselling, cross-selling, and incentive optimization.

50,000 outlets optimized

Improvement

The Problem

The client wanted to boost incremental sales, expand market share in pilot retail and service center locations, and ensure optimal lubricant product availability aligned with sales incentives and business goals.

Key Challenges

  • Boosting incremental sales and market share across diverse store formats.
  • Ensuring lubricant availability at the right time and location.
  • Aligning trade incentives with sales performance goals.
  • Factoring seasonal, regional, and behavioural trends into demand forecasting.

Our Solution

Leveraged an AI-powered analytics platform that integrated historical sales data, external data, and advanced modeling techniques to deliver store-level recommendations, optimize incentive schemes, and set final growth targets.

  • AI-powered analytics platform
  • Machine Learning
  • Uplift Modeling

Implementation Approach

  1. Store-Level Inventory Recommendations: Analysed SKU-level historical sales data and trade targets to forecast demand for lubricant products (e.g., motor oils, industrial lubricants).
  2. External Data Led Cross Selling: Factored in seasonal demand (e.g., vehicle maintenance seasons), regional vehicle types (VAHAN portal), weather patterns, and similar geography sales patterns.
  3. Target Scheme and Incentive Optimization: Segmented stores based on historical growth, location type (urban vs. rural auto shops), and purchasing behaviour. Applied uplift modeling to measure impact of incentive schemes and payout structures.
  4. Final Target Setting: Aggregated demand forecasts from store to regional levels, aligning with overarching geographical growth objectives for the brand.

Results & Impact

  • 50,000 outlets optimized across channels: Sales recommendations for more than 120 products across 50,000 outlets.
  • Improved ROI on trade promotions: Redirected budgets to high-potential stores, enhancing ROI.
  • Reduced promotional wastage: Alignment of offers with store-level demand patterns reduced inefficiencies.