Product Recommendations

Product Recommendations forecast product popularity, augmenting product information with layers of information ranging from general to specific sensory descriptions and temporal dominance of sensation. FlavorID® can also be deployed in online recommendations. Building a successful recommender is challenging and highly complex. Being accurate is not enough. Success = the balance between serendipity (discovery or novelty) and familiar. Foodpairing is able to deliver on this.

Why do it?

  • Increase consumer experience on e-commerce platforms by offering flavor navigation or flavor recommendations, guiding your customers towards products they will like.
  • Increase product discovery by serendipity.
  • Get better insight into your consumer by collecting preferences and dietary requirements.

Questions

  • What products should I try?
  • How do I explain the flavor of my product at dummy or expert level?
  • What is the music fitting my product?
  • What is the color matching my product?

What do you get?

  • Product recommendations

    Based on customer data

  • Serendipity

    Understand chance flavor combinations and likability rankings

  • Value proposition / Solution USP

    Increase customer experience by providing good food and drink recommendations. Increase personalization by recommending and/or ranking food products for an individual consumer. Increase basket size with smarter recommendations.

    Goals

    > Deliver a customer centric and engaging flavor or product recommendation tool.
    > Provide intuitive flavor matches based on an open ended set of simple flavor led user inputs.
    > Extend a user’s flavor experience by including products that sit outside their immediate preferences but are similar enough to spark interest.
    > Get better insights into the flavor preferences of your core consumer.
    > Augment product information.

    Key measures

    > FlavorID®

    Configuration

    > Connect to the FlavorID® platform, which is GDPR compliant for storing customer data.

    Data sources

    > FlavorID
    > Analytical aroma data via GC-MS
    > Analytica taste data via HPLC and photometric methods

    Capabilities

    > Aroma analysis
    > Liking prediction on all possible variations within a specific flavor camp

    Innovation cycle

    Launch & activation

    loop

    Target audience

    > Marketing

    Want to discover more?