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?
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.
See this solution at work
View the storyWhy 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.
What do you get?
01
Product recommendations
Based on customer data
02
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
Target audience
> Marketing