The challenge: Givaudan was trying to figure out which flavors were liked by the most people
Givaudan has a super efficient way to let panelists evaluate flavors. However, in sensory science if you mixed the same ingredients in the same proportions, but different volumes - the liking results can differ wildly! E.g. I may love a little bit of vanilla, but with twice as much I might hate it.
So they devised the following experiment:
- They took seven ingredients to create a target flavor composition.
- They synthesized a total of 40 flavors varying ingredient volumes.
- They created a focus group of 70 people to evaluate the flavors.
- The subjects would rate them 1 to 9.
- The total data set contained 70x40 evaluations of eight columns - seven ingredients and the main Key Performance Indicator - the liking score.
This method of collecting data was extremely time consuming and expensive. Here are the problems with traditional testing:
- It's expensive to find people and bring them to the lab.
- 3+ weeks to find people.
- Thousands of dollars to accommodate the 70+ test subjects.
- A normal human can only evaluate about 15 flavors before people lose the ability to distinguish between them.
- The subjects are from the target population of consumers, but they are not experts, so there is a lot of “noise” in the data.
- Different people have wildly varying “likes”.
Every flavor company needs to repeatedly do these experiment to gather data on hundreds of flavor compositions. As you can imagine, the time and expense to do this testing is enormous.
Even after all this testing, the company was looking for better ways to outsmart the competition and push new flavors to market faster.
This is where the technology behind DataStories comes in.
Givaudan wanted to see if there was a faster way to devise popular flavors. The process was very easy:
- We sign a non-disclosure agreement.
- You hand us a spreadsheet with experimental data.
- You decide what are the main things you want to know from the data:
- "Which ingredients drive liking?"
- "Which optimal combination of ingredients will make a flavor that many people like a lot?"
DataStories delivers back:
- A full report.
- An interactive presentation.
- Robust predictive models for each subject.
- Segments of people driven by the same ingredients (in the same direction).
- Groups of people segmented by propensity to like things ("easy to please" and the groups who are "hard to please")
What can the company do with this information:
Instead of doing 70-person focus groups every week, and not knowing exactly how to optimally use ALL the data, they can:
|Spend 6 months of the year conducting research in focus groups.||Spend only 2 weeks conducting research in focus groups. (92% time savings)|
|Spend several months of the year figuring out which ingredients drive "liking"||Figure out which ingredients drive "liking" by the next morning|
|Search for 40-80 people to evaluate a new flavor.||Plug the flavor composition into the "predictive models" to get the scores with confidence intervals. Zero test subjects required.|
|Evaluate 13-16 flavors per session per person.||Evaluate 1,000,000,000+ flavors per session.|
How we can help YOU:
So if you are a flavorist, don’t worry that the tools you currently have cannot do this type of advanced modelling.
- We can apply this advanced advanced modelling to your problem.
- We will handle all the data science, so you don’t have to.
- We will find the correlations hiding inside your data.
- We can predict flavors for 20x less money, and 6x less time.
- Give us a call to see what we can do for you: +32 47 638 84 97.