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Deploying an advanced analytics engine to prioritize SKUs, minimize OOS and maximize Economic Value from every store visit
01. Business Challenge

A Fortune 100 company in Latin America was facing huge challenges w.r.t. minimizing OOS and driving organic growth – 

    • 50% customers were barred – small outlets
    • Dual RTM Structure
    • Loose Bottle Sales
    • SFA Transition
02. Approach
03. Analytics Methodologies
  • Full Portfolio Coverage – Solution recommended orders for all SKUs (Not just must-sell)

  • Retailer Cash-flow Model – Model to predict invoice value and optimize SKU recommendation

  • Self-Learning Model Improves based on performance of past predictions

  • SKU Buying Behavior Model – Prioritized SKUs with Growth potential

  • Competition Intensity Model – Prioritized SKUs based on relative strengths to gain share

  • Contingency Protocols – Implemented fail-safe protocols to manage data issues that arise in transactional systems
03. Impact
  • 3.5 Points OOS drop vs. Control (20% reduction in OOS)
  • 11% revenue growth swing in high compliant customers
  • 3% incremental transactions generated by loose bottle conversion
Key Highlights