The turn of the century has brought with it a range of technological advancements and innovations which have had a widespread influence on current, long-standing industries. One of these innovations that has gained increasingly more attention is Machine Learning. Machine Leaning, closely related and usually considered as a sub-field of Artificial Intelligence (AI), is the process of automatic detection of usable patterns within data. Such powerful algorithms have the potential of drastically revolutionizing how businesses operate. One of the potential use-cases of machine learning is the creation of personalized fragrances based on consumer preferences. This article will provide an overview of how the Fragrance industry is using data-driven processes to come up with innovative perfumes, tailored to your individual needs.
Data-driven Fragrance Creation
Nowadays, robotic applications are able to take over the tedious task of meticulously blending different perfume ingredients together to produce fully standardized fragrances. However, new technological advancements and an increase in availability of consumer data has enabled software to take over the full process of fragrance creation, from start to finish. This is done by inputting machine learning models with thousands of ingredient blends that have previously proven to result in fragrances that are well-liked by the public. In this way, intelligent software is able to identify blending patterns which have previously led to successful fragrances, and therefore have to potential to result in future successes as well. In addition, since it is a well-known fact that individual fragrance tastes are heavily dependent on demographics, these machine learning algorithms are able to take into consideration regional preferences, allowing the models to come up with ingredient blends fitted to the desires of specific demographic regions.
It is possible to take the use of machine learning models even further, allowing them to propose customized fragrances for individuals rather than certain demographic groups. The fragrances which make it to the market are usually selected by a very small group of people. Software powered by Artificial Intelligence allows to reverse this process, enabling people to order fragrances that are customized to their personal preferences. IBM, for example, used a large dataset – containing information about personal characteristics and taste in fragrance – to create software that is able to predict fragrance desires on a personal level. By inputting the software with your personal preferences, the software is able to able to determine a mix of ingredients which should closely align to your preferences, creating happy customers with every new prediction.
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