52143
Aromoshelf
Perfume’s recommendation service for consumers and retailers
Luxembourg
Market: Artificial Intelligence, Mobile applications
Stage of the project: Prototype or product is ready
Date of last change: 30.04.2021
Luxembourg
Market: Artificial Intelligence, Mobile applications
Stage of the project: Prototype or product is ready
Date of last change: 30.04.2021
Idea
Freemium mobile application for perfume users with built-in recommendation service based on a consistent customer experience dataset. Via our mobile application, we answer what scent, where, and when to wear.
When we have enough data on emerging trends and user preferences, we will launch the B2B SaaS to benefit our business customers. Our solution will help save resources and shorten the path to the end-user.
When we have enough data on emerging trends and user preferences, we will launch the B2B SaaS to benefit our business customers. Our solution will help save resources and shorten the path to the end-user.
Current Status
What was done:
Product idea validations
Primary data collection
MVP for iOS
Consistent dataset
Accepted to the Luxembourg-city incubator
Where we are now:
MVP closed beta
Recommendations features in the beta testing
Further data collection
Customer development
In the incorporation process in Luxembourg
Product idea validations
Primary data collection
MVP for iOS
Consistent dataset
Accepted to the Luxembourg-city incubator
Where we are now:
MVP closed beta
Recommendations features in the beta testing
Further data collection
Customer development
In the incorporation process in Luxembourg
Market
B2C
Member of perfume communities online and offline
Possessor of 5+ bottles on the shelf
Regularly get to know new fragrances
Non-conservative, i.e., not obsessed with one particular fragrance profile
Use case 1
The customer marks in his/her profile 5 perfumes that he/she likes and doesn`t like, and our system recommends him/her 3 scents to test
Use case 2
The customer is choosing which perfumes to take on the trip. Using our filters, he/she can see the list of fragrances with positive ratings in the selected region
Use case 3
The customer sees lots of excellent reviews, but this perfume is not presented in his/her region. The system helps to understand if it is worth to risk to blind buy.
B2B
When the recommendation engine will be finished and Aromoshelf has enough user data available, it will target retailers and fine fragrance manufacturers
We start fr om B2C focus on perfume collectors and further evolve into B2B SaaS for fine fragrance manufacturers and retailers. The total market was about $95 BLN with CAGR of 3.7%, wh ere the Sales & Marketing market constituted $10 billion and we target 1% of that in 7 years, which makes 100$+ of serviceable obtainable market.
Member of perfume communities online and offline
Possessor of 5+ bottles on the shelf
Regularly get to know new fragrances
Non-conservative, i.e., not obsessed with one particular fragrance profile
Use case 1
The customer marks in his/her profile 5 perfumes that he/she likes and doesn`t like, and our system recommends him/her 3 scents to test
Use case 2
The customer is choosing which perfumes to take on the trip. Using our filters, he/she can see the list of fragrances with positive ratings in the selected region
Use case 3
The customer sees lots of excellent reviews, but this perfume is not presented in his/her region. The system helps to understand if it is worth to risk to blind buy.
B2B
When the recommendation engine will be finished and Aromoshelf has enough user data available, it will target retailers and fine fragrance manufacturers
We start fr om B2C focus on perfume collectors and further evolve into B2B SaaS for fine fragrance manufacturers and retailers. The total market was about $95 BLN with CAGR of 3.7%, wh ere the Sales & Marketing market constituted $10 billion and we target 1% of that in 7 years, which makes 100$+ of serviceable obtainable market.
Problem or Opportunity
Perfume market is oversaturated (in 2019 was released 5227 new perfumes comparable with 722 in 2000)
Rise of conscious consumerism
COVID-19 has raised online fragrance buying and accelerated the process of data-driven direct-to-customer (DTC) transformation
Source: according to Aromoshelf’s database (see pitch deck, appendix 1 );
according to Deloitte “Fashion & Luxury Private Equity and Investors Survey 2020” (see pitch deck, appendix 2) and companies 3Q 2020 reports, Cosmetics Business, Firmenich, Personal Care magazine, J.P. Morgan
Rise of conscious consumerism
COVID-19 has raised online fragrance buying and accelerated the process of data-driven direct-to-customer (DTC) transformation
Source: according to Aromoshelf’s database (see pitch deck, appendix 1 );
according to Deloitte “Fashion & Luxury Private Equity and Investors Survey 2020” (see pitch deck, appendix 2) and companies 3Q 2020 reports, Cosmetics Business, Firmenich, Personal Care magazine, J.P. Morgan
Solution (product or service)
Freemium mobile application for perfume users with built-in recommendation service based on a consistent customer experience dataset.
The referral project Vivino
A SaaS product will be built for retailers and fine fragrance manufacturers based on user data and preferences. Our solution will help save resources and shorten the path to the end-user.
The referral project Vivino
A SaaS product will be built for retailers and fine fragrance manufacturers based on user data and preferences. Our solution will help save resources and shorten the path to the end-user.
Competitors
Direct competitors: service for users and collectors Parfumo, Perfumist, Fragrantica and Sommelier du parfum.
Others address specific needs as Basenotes (save details of your perfumes), or Fragrances of the world (perfume info for b2b)
Others address specific needs as Basenotes (save details of your perfumes), or Fragrances of the world (perfume info for b2b)
Advantages or differentiators
Consistent dataset ready to ML. We built our dataset from scratch with the vision of the structure and processing tools we need. It is AI-ready by design, allowing us experimenting, quick iterations, and smooth analytics and recommendation system deployment once we have more data.
The holistic approach to analysis, as we take into account marketing features of the product, fragrant properties, and user experience.
End-user-centric approach as we build our products based on end-user needs, customer behavior, and market insights. We use modern technologies and an effective methodology for working with information; we create products that will facilitate the path to the consumer. This way, we will save resources and, in general, will have a significant social and environmental impact.
Recommendation accuracy for B2C is at the MVP level the same as competitors. As we increase our data science research and development, we will be able to improve our accuracy and, as a result, sustainable competitive advantage.
The holistic approach to analysis, as we take into account marketing features of the product, fragrant properties, and user experience.
End-user-centric approach as we build our products based on end-user needs, customer behavior, and market insights. We use modern technologies and an effective methodology for working with information; we create products that will facilitate the path to the consumer. This way, we will save resources and, in general, will have a significant social and environmental impact.
Recommendation accuracy for B2C is at the MVP level the same as competitors. As we increase our data science research and development, we will be able to improve our accuracy and, as a result, sustainable competitive advantage.
Money will be spent on
Product evolution and support
Data collection
Sales and marketing
General and administrative expenses
Data collection
Sales and marketing
General and administrative expenses
Team or Management
Presentation
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