Enter a caption (optional)Meghan Maupin, CEO of Atolla, will be speaking at Core77's speaker series during CES 2019, "Reaching Future Customers Using Design and Tech" at 2:00 PM on the Design & Source Stage, Wednesday, January 9th.
The dilemma of finding the right skincare is an age-old rite of passage that often involves buying a deluge of skincare products—some effective, others doomed forever to the back of the medicine cabinet. With the advent of new models for skincare coming from companies both big and small, it's clear the demand for change is sky high. Companies like Atolla, led by CEO Meghan Maupin, believe the magic formula lies in the collaboration between design and science. Developed at MIT during her thesis year of grad school, Atolla is a skincare system that involves a skin test, app analysis, and finally, a customized serum that is recalculated on a monthly basis to deliver optimal skincare based on climate, oil production, and other environmental factors.
After wrapping up with their recent Kickstarter campaign to launch the brand, we recently sat down with Maupin to hear more about the development of Atolla and how she views the future of the skincare industry as more data scientists involve themselves in the field.
In grad school, I began having autoimmune problems and getting rashes on my skin. Despite keeping an extensive diary that tracked everything I ate, what products I used, what the weather was like, I still couldn't figure out what was going on with my skin. I had the realization that I was at the best school to solve my skin puzzle using data and machine learning, and set out to find a more technical person to help me. That's when I met Sid; he's a retail data scientist who has eczema and went through a similar frustrating trial and error process of trying every moisturizer in the aisle to help with his dry skin. We knew there was a better way, and together we started building Atolla.
Enter a caption (optional)The process is simple; you do a short at-home skin analysis, our algorithms design a custom serum for you based on the analysis, and we adapt the formulation each month based on how your skin changes. In the analysis, some of the inputs we use to evaluate an individual's skin include: their physical skin attributes (oil, moisture, pH), environment, lifestyle, diet, other products in their routine, previous sensitivities, and their preferences. We also keep track of user's visual progress through selfies, and are working on applying Computer Vision to calculate the severity of a user's top skin issues, i.e. redness or dark circles. Our machine-learning algorithm recognizes patterns in what's causing your skin to react positively or negatively, and we use this information to always provide users with the best solution.
Enter a caption (optional)We actually designed the process and built the technology first. We were excited to have Dr. Ranella Hirsch, a cosmetic dermatologist, join the team and help in the development of our platform. It was important to think holistically about all the factors that could impact skin health, and how to measure them. We leveraged the AI and ML expertise at MIT to create the automated feedback loop that allows us to learn from every interaction with the customer. And we have amazing chemists we're working with to make data-driven serum formulations.
We first designed the system at a high level; bringing the scientific method to skincare through 3 simple steps of analyzing, formulating, and tracking. For the analysis, we used existing dermatology equipment and an intake survey we developed with Dr. Hirsch. We were able to refine the process down to 10 minutes after testing with several pop-ups! Some interesting learnings came out of our early pop-ups. One key learning was how important it was to show logic of how the algorithm was working, especially why someone matched with particular ingredients. This has been key in thinking about how we educate the consumer at every interaction and give them information to help make better decisions about their skin health.
Enter a caption (optional)Another key learning was how important it was to understand a user's preference, in addition to what will scientifically work best for their skin. You could give someone the most effective product in the world, but if they don't like how it feels or smells, they won't use it. Preference testing is a wonderful, tactile part of the Atolla experience that helps create a truly customized product.
Because we were designing an experience that was unlike anything that existed in skincare, we stayed open and listened to what the customer wanted. We wrote down every question people asked us in the pop-up about their skin and thought about how we could help them answer it through the Atolla system.
I strongly believe good design can make technology accessible to everyday users. Before I was at MIT, I was a designer at Formlabs focusing on making 3D printing more accessible through content and education. Now, concentrating on skincare, I believe the future of health is making personalized products available to the average consumer and educating them about their skin. Most people don't have time or can't afford to see a dermatologist; we want to help them easily manage their skin health day-to-day. To do that was a great design challenge!
Enter a caption (optional)The skin analysis tools had to be precise, yet simple and easy to use. The personalized product had to be at an accessible price point, in order to ensure everyone can get the most efficacious product, no matter who they are. These constraints really motivated me creatively, especially thinking about the impact that a system like this could have—on people's confidence in themselves and in their skincare.
We took a dermatological process and tools, and made a previously inaccessible process available at home, for a price that starts at $20 a month. The greatest feedback I've gotten is that the "science behind Atolla is anything but simple, but the outcome can't be more streamlined or efficient." Taking something complex and making it simple for the customer is the #1 goal. The skincare industry can do better than making a mass product that doesn't really work well for anyone. Instead, let's take a customer-driven approach and allow the user's input to create the best product for them.
It's important for all designers to consider the entire lifecycle of the product they are creating and what impact it will have on individuals, society, and the environment. My thesis was about "The Social and Environment Impact of the Skincare Industry" and I mapped what impact different skincare ingredients had on individual health, as well as the health of our environment once those ingredients enter our water system.
The most sustainable thing we can do is consume less. In skincare, the amount of waste is insane. People buy and throw away almost $2 billion worth of products per year. The way to consume less is ultimately to buy the right things, and at Atolla we're taking that one step further to enable customers to make better, safer decisions by knowing more about their skin.
Additionally, AI and Machine Learning can be applied to innovate on the supply chain side. Predictive modeling can reduce waste in manufacturing , and mass customization ensures that you are only making what needs to exist. Rather than making 10,000 units of one product designed to sit on a shelf for 2 years, you can use better ingredients and make a customer a precise product for them, and continue to adapt based on how their needs change. The future of manufacturing is mass customization and we're excited about moving the skincare industry towards a more sustainable future.
Always start with the user! We interviewed at least 100 people when we were first starting in order to understand what was broken in the skincare industry. We came to the conclusion that fundamentally people didn't understand what was causing their skin issues, so they could never find the right solution. It's a highly emotional problem too- our skin and our appearance is very tied to self-confidence.
For Atolla, it's been important to combine qualitative and quantitative research on the customer and continue to test and learn. We set up micro pop-ups and every test we did to answer a key question to get us towards what Atolla is today. We try to learn what we can from analogous industries as well; the tracking part of the Atolla app today is more akin to what you might find in fitness than anything that has previously existed in beauty. Those types of metaphors help users relate a new experience to something that they know (like other forms of health tracking).
Another learning we've had in thinking about communicating our machine-learning platform is to keep the language simple and focus on the benefit for the consumer. As designers and technologists, we all can easily get lost in the high-tech aspect of what we are doing. But in the same vein of making technology accessible is making people feel like it's for them, and that it doesn't go over their heads. We've asked our earliest users to describe what Atolla is to them, and use the same language in our marketing in order to make what we're doing clear and comprehensive.
Learn more about our CES Event "Reaching Future Customers Using Design & Tech" here.
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How Machine Learning Is Transforming Skincare to be More Effective and Sustainable
Wait until they try to do this at scale - an adjusted formula, monthly, per user, WOW.
Automation!