How My Beauty Matches Is Using Machine Learning To Disrupt The $445 Billion Beauty Market
My Beauty Matches is known for being the world’s first personalized beauty product recommendation and price comparison website using proprietary machine learning and AI technology. Founded by Nidhima Kohli, My Beauty Matches and its Beauty Matching Engine technology allows users to compare prices on over 400,000 products from over 3,500 brands. But its unique selling point is that it offers consumers personalized and impartial beauty product recommendations, which helps brands and retailers substantially increase their conversion rates. My Beauty Matches has over 170 retail partners, including Aveda, Benefit Cosmetics, bare Minerals, Harvey Nichols and Harrods.
After graduating from Cass Business School in London, Kohli began her career working in the finance industry where she spent over 7 years working 80 hours per week and traveling internationally. Kohli said that finding beauty products was essential because traveling can take its toll, but there weren’t any trustworthy recommendations available at the time. This presented a massive market opportunity for retailers and brands. In order to become deeply familiar with the products that her website recommends, Kohli became certified as an international makeup artist and beautician at the London College of Beauty.
My Beauty Matches’ cutting edge technology offers tailored, impartial suggestions to users when they fill out a quiz for the type of product they are after. Whether it is in skin care, makeup, body, hands & feet, hair or fragrances, My Beauty Matches is able to cater the results to match users goals. You can then select one of the 3 matches based on the Beauty Matching Engine to compare prices and shop from the retailer that you prefer. And if you are on a budget for those items, you can also sign up for Sale Alerts.
A number of other companies have attempted to duplicate the My Beauty Matches personalized product recommendations function, but they do not have the breadth of technology to provide product recommendations and price comparisons from over 400,000 products and nor do they have a unified taxonomy. Rivals usually cover only a few hundreds of brands, half of which are affiliated to one of the big beauty corporations — which customers often trust less. Due to the difficulty of being able to duplicate the technology and remain impartial, many of those competitors now have decided to set up a beauty box subscription business model instead.
The My Beauty Matches algorithm was built using proprietary machine learning and artificial intelligence by crunching data from all of its website users. The Beauty Matching Engine looks at data from the brands, clicks and consumer profiles over the last three-and-a-half years. Plus the advice is tailored by beauty experts. As a result, the recommendations become smarter and smarter every day.
With years of historical data under its belt, coupled with information from over 400,000 products and 100,000s of consumers, the personalization and machine learning technology has created the most powerful Beauty Matching Engine available in the beauty industry.
Because of the strength of its algorithm, many beauty brands and online retailers have approached My Beauty Matches to tap into the Beauty Matching Engine technology, in order to be able to white label it onto their own website for the purpose of increasing their own conversion rates and Average Order Value (AOV).
My Beauty Matches does not have its own products that it tries to push out in its recommendations and it is not affiliated with any big beauty corporation, which ensures integrity in its recommendations and hence its AI platform for retailers and brands. “The idea was to think more long term than short term,” said Kohli in an interview. “My Beauty Matches is the only technology in the world that provides brands the opportunity to connect better with their consumers through offering them personalized matches in a completely impartial way.” My Beauty Matches also takes the privacy of its users very seriously. The company does not share any individual data with anyone. But insights are necessary for the artificial intelligence technology to become smarter.
Forbes reporter Chloe Sorvino pointed out that it is an excellent time to be a beauty entrepreneur because it is a $445 billion industry and expected to reach $675 billion by 2020. And CB Insights data cited sixty-two privately held beauty companies were acquired in 2016, which is 38% more than the previous year and a record since 2012. For example, Shiseido acquired a beauty app that scans selfies to suggest personalized makeup blends called MatchCo in 2017.
To date, My Beauty Matches has raised a total of £650,000 (do we need to convert this in dollars? If yes then it is: $845,000) in funding from angel investors and is expecting to be revenue profitable already by the end of the year through licensing out its Beauty Matching Engine technology. “We are very excited at My Beauty Matches about this new direction. Our partners understand it is very time consuming and costly to build new platforms, hire the right people or agencies, combine the beauty expertise, build a taxonomy and tagging system for over 400,000 products and 3,500 brands, and they also understand that it is only our three-and-a-half years of historical machine learning data across thousands of brands — which will give them the competitive edge they need to increase their conversion rates significantly,” added Kohli.
Retail partners have realized that My Beauty Matches has a trove of machine learning data so it would be more cost efficient to partner with a startup instead of trying to build something in-house. And the retail partners also understand this is the only way they can capture over 3 years of machine learning and AI data across all their brands to increase their conversion rates with no risk to themselves. Plus creating recommendations in beauty can be challenging for retailers because even the smallest ingredients can make major dermal impacts, there is no uniform taxonomy and beauty trends are constantly changing.
The advantage online retailers have working with a startup is that a young business can easily be iterative and do plenty of testing which saves the big corporates time and money from investing into large infrastructures themselves. Most of the large beauty corporations are on legacy platforms, which creates a level of friction in building a new platform. And the My Beauty Matches Beauty Matching Technology offers beauty corporations the innovation they need without taking on those risks.
Online retailers are now looking more and more into personalization and AI to remain innovative and to increase their conversion rates and life-time-value (LTV) of their customers, especially in times when many retailers are constantly challenged by the likes of companies such as Amazon. “It is the companies that are willing to implement and execute innovation fast that will remain ahead of the game, and they know partnering with startups is what will give them the competitive edge which is why some of our partners are looking to roll out our technology before Black Friday. Our machine learning and AI technology will help drive higher conversion rates, LTV, basket sizes for our retail partners and also help them discover new channels to the market and the consumer. It is this personalization technology — which will change how people buy beauty products online forever, helping consumers find the right products for them and helping online retailers with greater margins.” explained Kohli.
My Beauty Matches plans to roll out its new technology licensing later this year.
Article and image originally appeared on Forbes.com.