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.

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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?

01

Optimal SKU’s line-up:

Optimal product line-up for existing markets

02

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

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