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CriticalRiver Boosts FMCG Retail with NBA Predictions and Cross-Selling

  • The Customer

    The Customer

    A leading FMCG retail company with a strong presence across the US.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg
      • To predict the Next Best Action (NBA) of shoppers efficiently and accurately.
      • To devise a recommendation engine that facilitates both cross-sale and up-sale strategies.
      • Ensuring the digital display captures maximum attention from shoppers, considering the diverse grid patterns including small grids and 3×4 (12) small grids.
  • The Solution

    The Solution

    • Developed a sophisticated algorithm that, based on the shopper’s product interest, predicts their NBA.
    • Engineered a recommendation system that suggests products for up-sale and cross-sale, leveraging the NBA prediction outcome.
    • Implemented a unique display prediction algorithm that determines the optimal arrangement of up-sale and cross-sale products on the digital screen. This algorithm specifically manages the complexities of various grid patterns to ensure maximum shopper engagement.
  • Solution Components

    Solution Components

    NBA Prediction Algorithm, Recommendation Engine, Display Prediction Algorithm

  • The Results

    The Results

    • Currently under observation in one of the US’s largest shopping mall chains, boasting around 1600 stores nationwide.
    • Anticipated significant growth in the top-line due to enhanced shopper engagement and intelligent product recommendations.

The Customer

A leading FMCG retail company with a strong presence across the US.

The Challenge

3D-Priniting-Challenges-Img.jpg
    • To predict the Next Best Action (NBA) of shoppers efficiently and accurately.
    • To devise a recommendation engine that facilitates both cross-sale and up-sale strategies.
    • Ensuring the digital display captures maximum attention from shoppers, considering the diverse grid patterns including small grids and 3×4 (12) small grids.

The Solution

  • Developed a sophisticated algorithm that, based on the shopper’s product interest, predicts their NBA.
  • Engineered a recommendation system that suggests products for up-sale and cross-sale, leveraging the NBA prediction outcome.
  • Implemented a unique display prediction algorithm that determines the optimal arrangement of up-sale and cross-sale products on the digital screen. This algorithm specifically manages the complexities of various grid patterns to ensure maximum shopper engagement.

Solution Components

NBA Prediction Algorithm, Recommendation Engine, Display Prediction Algorithm

The Results

  • Currently under observation in one of the US’s largest shopping mall chains, boasting around 1600 stores nationwide.
  • Anticipated significant growth in the top-line due to enhanced shopper engagement and intelligent product recommendations.

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