29 October 2019, Amsterdam – Suburbia, a technology company specializing in alternative data solutions, has introduced its second offering that leverages millions of anonymized transactions to predict the performance of luxury brands in the beauty and personal care space.
Suburbia has partnered with companies in the payments ecosystem to collect receipt line-level data from multiple sources. This unique dataset tracks sales of luxury cosmetics and fragrances in France, the largest market for this segment in Europe and the fourth largest in the world, with a total value of three billion euros.
“France is not just a large market for the world’s biggest luxury companies and beauty brands, it is also a trendsetter and tastemaker,” said Hamza Khan, CEO of Suburbia. “By collecting accurate sales across the country, our product is a powerhouse for what is popular in France, and a strong indicator of what will be popular globally.”
The new dataset delivers daily signals on publicly listed and private companies, including key players in the industry such as L’Óréal Luxe, Coty Inc., Estée Lauder Companies and LVMH. It tracks over 100 brands including Dior, Chanel, Hermès, Hugo Boss, Kenzo, Lancôme and YSL. The data product has been built specifically for investors who want granular insights into how these companies’ main revenue drivers are performing, whether by brand, category or product.
According to a recent report, among all luxury goods sectors, cosmetics and fragrances have witnessed the highest sales growth.* The market is expected to grow annually by 3.3% (CAGR 2019-2023).**
Suburbia’s proprietary technology is capable of processing millions of consumer purchases. No personal information is ever used or shared in the process. This data is updated on a daily basis with a one-day lag, so investors can get up-to-date insights for making decisions faster.
Other highlights of this product include:
- Ticker mapping to easily see performance of publicly traded companies over time
- Granularity such as EAN, brand name, item pricing, product category, basket composition, geography and time of transaction. Anonymized merchant ID is provided in order to compare same-store or like-for-like sales.
- Historical coverage, with over three years of data available for backtesting