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Achieving The Optimal Customer Experience For Fragrance Consumers

One of the biggest challenges for any brand or retailer is building an amazing customer experience. Brands strive to build the optimal journey so consumers will complete a purchase and ensure they come back again. How can algorithms be optimized to increase conversions? How can consumer sentiment play a pivotal role in perfecting the equation?
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  • Topic: Smell
  • Sentiment: 79%
  • Volume of Pos. Reviews: 310,381
  • % of Neg. Mentions: 12%
  • Topic: Bottle design
  • Sentiment: 87%
  • Volume of Pos. Reviews: 17,599
  • % of Neg. Mentions: 30%

That’s exactly what Camila Tomas, the Vice President of Innovation and Technology at PUIG, set out to do. Her career in the fragrance industry spans decades and she and the PUIG team are known for disrupting the fragrance industry. Under the PUIG umbrella she developed Wikiparfum, an algorithm that creates a unique scent profile for consumers for all fragrances, including competitors. We sat down with her to discuss her unique use case.

In this case, Wikiparfum is a technology solution for an entire product category: fragrances. Wikiparfum is the leading algorithm powering the most prominent online fragrance retailers in the USA, LatAm, Europe, the Middle East and China. It collects consumer data to provide the ultimate fragrance recommendation engine. Consumers select fragrances they already love and Wikiparfum’s engine provides recommendations of options that hit the same scent profile.

Optimizing Fragrance Recommendations
Despite all the success of Wikiparfum, Tomas’ challenge remains: how can she get inside fragrance consumers' minds? She believes that consumer sentiment would be the key to unlocking the data hurdle. This would usher in a new stage of the algorithms development.
The solution to her challenge: Revuze’s consumer insights data.
The Data Challenge
Tomas’ data challenge was really to provide the most comprehensive picture of the fragrance category for consumers. This would enable the algorithm to display leading trends for the various perfumes. The missing component, sentiment analysis, made Revuze a natural fit as a solution. Revuze’s AI-powered platform calculates consumer sentiment based on the category, brand, and product level. Her vision is to leverage the Revuze API and integrate the data into the Wikiparfum algorithm to enhance its already robust capabilities, ensuring the most comprehensive solution on the market.
Beyond the sentiment analysis, she plans on integrating the topic term cloud. Why is it so important to Tomas for a consumer centric experience? The lexicon on the site are descriptors that olfactory professionals use. Words like floral, green, aldehydic, may not be the words consumers use to describe fragrances. The two vocabularies together will be consolidated for the most complete vocabulary of how consumers discuss fragrances.

Retailers using Wikiparfum are already selling more than those who do not. The data from Revuze will take the algorithm to the next level.

While Tomas’ ultimate goal is to use data to integrate trends from consumer sentiment analysis, she is also interested in other topics that may not be trending yet to ensure the algorithm stays ahead of the curve.

Sustainability for instance is a buzzword that everyone talks about. Many retailers like H&M make a point of indicating what percentage of the garment is recycled. It pops up in a variety of places. Tomas turned to Revuze to commission a special report to try to understand consumer sentiment around fragrance packaging and ingredients.

The consumer data painted an interesting picture. Brands that highlight that they are sustainable see a lift in sentiment. The sustainability discussion for instance peaked when Thierry Mugler launched its Alien Goddess fragrance line with the refillable bottles in August 2021.

Consumers also appreciate when fragrance products are vegan or cruelty free. They are looking for natural ingredients that are responsibly sourced.

Understanding the value of the information, Tomas integrated the data into Wikiparfum’s recommendation engine. This optimization is just another milestone on the journey to create the ultimate customer-centric experience.

Other Trends on the Horizon
From consulting the Revuze dashboard for emerging trends, commissioning reports, to integrating the API into Wikiparfum, Tomas’ use-case is an amazing one. She leverages all the different data channels to achieve her vision for keeping her finger on the pulse of the fragrance category. Although Wikiparfum was developed for fragrances, there are other industries that are dependent on smells as well. Most notably, the liquor industry. Whether it is wine, whiskey, or cognac, a key parameter is its nose or smell. We can’t wait until Revuze is integrated into Wikiparfum to see what revelations will emerge. Stay tuned & smell good!