Portfolio optimization

Portfolio optimization proposes an optimal product line-up for a given market. Foodpairing’s machine learning portfolio maximization tools will tell you what SKUs will work best, for what customers, in what markets.

Why do it?

  • To get the line-up that generates the largest reach and prevents cannibalization.  

Questions

  • Should I extend or reduce my product line?
  • Should I extend or reduce my product line?
  • Which new SKUs should be launched?
  • Which new SKUs should I launch to avoid cannibalization on my core range?
  • What is the best way to counter competitive product lines?
  • What is the product I should launch to reach a new target audience?
  • Can I reach consumers with one flavor profile or should I develop multiple products within a flavor territory?
  • Which existing SKUs and flavors should I launch in a new target market?
  • Which existing flavors will have the highest liking in a new target market?

What do you get?

  • Optimal SKU’s line-up:

    Optimal product line-up for existing markets

  • Success probability

    Identify existing flavors that have highest success probability for new markets

  • Value proposition / Solution USP

    Goals:

    > Optimize the number of SKUs versus the percentage of consumers for whom at least one of the concepts in a particular combination is the preferred concept

    > Generate and rank CFI Scores for target audiences outside the original briefing or target group

    Key measures:

    > Predicted trial

    Configuration:

    > Select number of samples in line-up

    > Select commercial products in line-up

    > Select flavor territories

    Data sources

    > Analytical aroma data via GC-MS

    > Analytica taste data via HPLC and photometric methods

    > Commercial products from e-commerce websites

    > Recipe websites, blogs

    Social media

    Capabilities

    > Trial and Liking prediction

    > (Predicted) TURF on millions of concepts

    Innovation cycle

    Insights & portfolio strategy – Early stage insights

    loop

     

    Target audience

    > R&D

    > Product managers

    Want to discover more?