Advanced Solution

White Space Analysis

A White Space Analysis allows you to understand the purchase intent of different consumer demographics and how this relates to your products, while analysis of white spaces provides insight into the potential of currently untapped flavor spaces.

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Why do it?

  • To identify white space flavor territories with favorable predicted liking scores.
  • To get insights into the novelty of concepts.
  • To get insights into the link between demography and purchase intent.
  • To get insights into the aroma similarities between different SKUs.


  • What is the predicted liking of flavor territories where there are currently no (or very limited) products on the market?
  • What flavor white spaces should you invest in first?
  • Which concepts should you launch to increase your brand equity?
  • How does the consumer profile influence purchase intent for specific concepts?
  • What concepts have the highest purchase intent for the specific demographic you are targeting?
  • What audience should you target for a concept you are preparing to launch?
  • Which products have a similar flavor profile to a product I need to replace (due to regulation or other restrictions)?

What do you get?


White space:

A matrix of the predicted liking for each flavor territory.
Existing products on the market are mapped on the matrix to uncover the white spaces.


Concept classification:

Novelty versus predicted trial visualization to check which concepts will generate more excitement / brand equity.


Consumer segmentation:

The concepts with the highest trial are identified based on a selection of demographic traits or attitudes that characterize your target audience.


Similarity matrix:

Identify how similar products in a portfolio are though a matrix comparison of their sensory profile.

Value proposition / Solution USP

Increase successful launches:

> Be the first to launch new products in a successful, but as yet unexplored, flavor space.
> Know what concept will be the most successful for each demographic trait and attitude lined to your target audience.
> Get instant feedback on the trial of new concepts.


> To provide insights into the shared profiles of products within the portfolio.
> To provide insights into the flavor space coverage.
> To identify white spaces and underdeveloped flavor territories.

Key measures

> Predicted liking
> Predicted trial


This solution combines the results from core, competitor product and market insight analysis with learnings from the trial and liking model. You should first initiate these solutions before you can execute a portfolio mapping and white space analysis.

Data sources

> Analytical aroma data via GC-MS
> Analytical taste data via HPLC and photometric methods
> Commercial products from e-commerce websites
> Recipe websites, blogs
> Social media


The ability to build machine learning models for trial and liking.

Innovation cycle

Insights & portfolio strategy – Early stage insights



Target audience

> R&D
> Product management
> Portfolio management


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