Glossier: Changing the Face of the Beauty Industry by Crowdsourcing the Magic Formula

Glossier became the “first socially driven brand” by crowdsourcing innovation and soliciting real-time feedback from its millennial cult-following [1].

Disrupting the Traditional Beauty Industry

In 2014, Emily Weiss founded Glossier, a direct-to-consumer beauty company, as an extension of her successful beauty blog, Into the Gloss (ITG), which she founded four years prior. Through the blog, Weiss identified a gap in the beauty industry. She believed there was a need for a brand that recognized consumer preferences rather than traditional beauty brands that hired experts to tell consumers what they need or don’t need [2].

At launch, Glossier adopted the open innovation approach of crowdsourcing innovation and soliciting real-time feedback from its millennial cult-following through its blog (1.5+ million unique views per month [3]) and social media presence (1.5+ million Instagram followers). Due to its customer-centric approach, Glossier became the “first socially driven brand” that has successfully disrupted the beauty industry [4].

Crowdsourcing is the magic formula

Consumers, especially female millennials, discover beauty products through friends [5]. Based on this recent trend, Weiss created Glossier with three things in mind:

  1. Frame customers as experts [4]. When developing new products, Glossier reaches out to its community for feedback on colors, textures, fragrances, shades, and price points early in the process, before launching on its e-commerce site and select stores [4]. From soliciting opinions from its blog and social media beauty influencers and followers, Glossier positions and empowers each consumer as the expert. Further, this is far reaching and comprehensive – the company does not discriminate as it solicits live feedback from everyone, irrespective of social status, background, or geography. For example, after posting a question on Instagram regarding a red lipstick, within 20 minutes, they easily rack up thousands of responses about shade preferences [4]. These responses range from a teenage girl with only 10 Instagram followers to Makeup by Mario, Kim Kardashian’s makeup artist with 10 million followers [4].
  2. Create buzz [4]. Keeping ongoing dialogue, closely monitoring, and continually improving this customer-centric feedback loop with its audience enable Glossier to ultimately create a consumer-led product that young women actually want to purchase [4]. This then encourages these customers to share with other customers, resulting in a rippling “buzz” effect.
  3. Position Glossier as an approachable friend and peer [4]. Glossier created an affiliate marketing program of “reps” to sell products to friends and family. Reps are given a discount code, which are then shared with their networks of friends and family to receive special credit and commissions for each sale. With an added trust element to discovering products from friends, ideas are more easily shared within each network. These concepts are then escalated through the reps and consumers to Glossier’s product development team, further motivating innovation and improvements [6].

As an example of one such successful crowdsourced beauty product is the Glossier Milky Jelly face cleanser. In 2015, Emily posted a blog titled, “What’s Your Dream Face Wash?” and leveraged the 400 comments generated from the post to create its Milky Jelly face cleanser [7], which is still its best-seller four years later.

More recently, the company looks to connect the dots of movement between and through customer data cross-domain analytics tools that allow the company to track people visiting both platforms [8]. This has helped them gain a better understanding of consumer buying patterns and cater to their preferences and needs with new product launches, further driving loyalty to the brand.

There are proven synergies with and The below traffic histories prove that traffic from the former converts into the latter. According to CTO Bryan Mahoney, ITG readers are 40% more likely to purchase products from Glossier’s e-commerce website than other consumers [9].

What’s next?

In order to successfully scale its business, it is imperative that Glossier stay true to its ability to listen to and speak directly with its customers. However, there are limitations where the company stands today. For example, most of its data analysts still parse through comments left by consumers on ITG, website, and social media accounts [8]. Further, it depends on third-party cookies, which does not allow full control of what is shown to customers [8].

Below are some recommendations for Glossier:

  1. Invest in technology upgrades to automate the processing of qualitative data and feedback from customers that have been the core to success with developing innovative products [8]
  2. Adopt a machine-learning “Stitch Fix” model where the company better identifies patterns and provides a personalized experience for each customer who visits the site [8]

Though these proposed operational and technological improvements can help Glossier scale, there are important questions to consider. What is the appropriate level of machine learning to incorporate but still allow Glossier to remain true to its brand? Will machine learning detract from Glossier’s value proposition of personalized interactions with its consumers? How will consumers react to these changes?

Word Count: 795


  1. McKinsey & Company. (2018). What beauty players can teach the consumer sector about digital disruption. [online] Available at: [Accessed 13 Nov. 2018].
  2. (2018). Glossier engaging customers by spurring interaction, communication. [online] Available at: [Accessed 13 Nov. 2018].
  3. Forbes. (2018). Emily Weiss On Glossier And Into The Gloss In 2015. [online] Available at: [Accessed 13 Nov. 2018].
  4. (2018). How socially driven beauty brand Glossier sustains 600% annual growth. [online] Available at: [Accessed 13 Nov. 2018].
  5. Larocca, A. (2018). The Magic Skin of Glossier’s Emily Weiss. [online] The Cut. Available at: [Accessed 13 Nov. 2018]. Racked. (2018). Glossier Is Going After New Customers With an Army of Reps. [online] Available at: [Accessed 13 Nov. 2018].
  6. Medium. (2018). How Crowdsourcing Helped Set Glossier Apart – Mistress Agency – Medium. [online] Available at: [Accessed 13 Nov. 2018].
  7. Digiday. (2018). How Glossier uses data to make content and commerce work – Digiday. [online] Available at: [Accessed 13 Nov. 2018].
  8. CB Insights Research. (2018). We Analyzed 9 Of The Biggest Direct-to-Consumer Success Stories To Figure Out The Secrets to Their Growth — Here’s What We Learned. [online] Available at: [Accessed 13 Nov. 2018].
  9. Reserved, C. (2018). Glossier: We’re creating the new beauty essentials: easy-to-use skincare and makeup that form the backbone to your routine. shop exclusively at [online] Available at: [Accessed 13 Nov. 2018].



Building the Way to Battlefield Domination: The United States Marine Corps and Additive Manufacturing


Bechtel Corporation: Responding to Additive Manufacturing in Construction

Student comments on Glossier: Changing the Face of the Beauty Industry by Crowdsourcing the Magic Formula

  1. I think “What is the appropriate level of machine learning to incorporate but still allow Glossier to remain true to its brand?” is a great question to pose. Glossier’s unique business model “crowdsources the magic formula” as you mentioned, yet all of the crowdsourcing is happening prior to the product development phase in order to influence product development. Glossier also prides itself on taking a long time on product development in order to release the best product, and usually only offers one. For example, the milky jelly cleanser is the only face wash they offer. I wonder if Glossier has ever collected reviews on products once they have launched and has ever considered changing their formula. They could gather this data and utilize machine learning to create product improvements, however, that may be against Glossier’s DNA as I’ve never seen them edit a product once it hits the market before. If this were the case, I’m sure Glossier could still utilize machine learning to aid with product development and take away some of the manual scraping of social media comments, etc. Since Glossier is gathering a ton of data prior to product development, I think they can definitely build in machine learning to their advantage.

  2. I really enjoyed reading this article. In particular, I think that your second question, “will machine learning detract from Glossier’s value proposition of personalized interactions with its consumers?” is thought-provoking and the right question to be asking. In response, I think it is imperative that Glossier continue to listen to and speak directly with its customers. This strategy has proven to be successful for the Company, and I would argue that consumers now expect this level of collaboration from Glossier. That said, I think it is possible for Glossier to continue crowdsourcing and co-developing products with customers, while using ML to automate the processing of feedback from consumers. As the Company scales, it feels unrealistic to assume that data analysts will have the time to parse through comments left on ITG. ML could allow the Company to be more efficient when deploying its crowdsourcing tactics, allowing it to scale while remaining true to its core values.

  3. Great article – very enjoyable to read. I do think that Glossier should consider investing in machine learning that would operate in a way similar to the crowdsourcing model that the firm currently employs. As Glossier continues to scale, it would be too manual for employees that Glossier to sift through all of the comments from customers, although the accuracy would be better than utilizing machine learning in the beginning. I agree with you on the idea of adopting a “stitch fix” model – finding patterns, large qualitative trends, and potentially personalizing the recommendation would be important to have as the company continues to grow – Glossier definitely should start investing in those capabilities now if the firm would like to remain competitive. However, to your other question, I wouldn’t worry about machine learning threatening Glossier’s brand image – if anything it would allow the brand to potentially focus more on developing personalized recommendations for all of its customers as opposed to devoting most of its time sifting through the data to develop product that is more “one product fits all” based on the majority of opinions. I believe that customers would react very postiviely to these changes.

  4. Interesting post GlossierMasker. The data is clear – these crow sourcing efforts have done an effective job of creating engagement around the the brand and generating buss for Glossier. I’d be interesting in the financial impact of these efforts. How have crowd sourced ideas performed relative the rest of the portfolio?

  5. What strikes me as interesting about Glossier is that they are predominantly an online retailer — they opened their first retail outlet just last weekend. This seems relatively uncommon in the beauty business, and I imagine poses certain challenges as its customers attempt to determine if Glossier’s products are right for them. This suggests to me that Glossier could really leverage machine learning to better understand the preferences of their customers, and develop more personalized recommendations.

    I think there are several ways Glossier could approach this. First, they could use natural language processing to parse the blog comments to reduce the time it takes to sift through all the feedback that customers share. Second, they could develop online quizzes for new customers — similar to StitchFix — to get a sense for the person’s aesthetic, preferences, or skin type before making recommendations for products to sample or purchase. This would allow Glossier to remain true to its value proposition of providing personalized recommendations as they grow and scale.

  6. Thank you for organizing your thoughts so well! While crowdsourcing is Glossier’s “magic formula” and they are known to designate “consumers as experts”, I wonder whether this will still be Glossier’s advantage over time. Does crowdsourcing actually dilute the brand given it allows unchecked input from anyone — for example, mixing experts with amateurs, blending different customer segments, etc., and if so, how can Glossier combat this? How would Glossier’s approach of crowdsourcing for product development compete with that of an established beauty brand, which may have experts leading the way but use crowdsourcing to collect feedback to refine products further?

  7. Interesting article about a brand that I love! I think Emily Weiss did a great job of spotting a gap in the beauty market and fulfilling it for consumers.
    As someone who had to manually read through samples of feedback from customers at my old job, I agree with your recommendation to invest in ML for parsing through feedback and aggregating it. Having highly skilled labor such as data scientists (or PMs in my case) sifting through droves of feedback can be very costly for the company when the analysis and recommendations are what they should be spending time on. I do not think that ML detracts from the “personalized” customer experience, but actually helps further that mission. By going through feedback more efficiently, Glossier will be able to tackle a larger volume of it and drive further insights for product development.

  8. Really interesting article! What I find most striking is that Glossier was born out of a media publication. It was incredibly smart of Emily Weiss to harness her significant consumer following and data that revealed customers wants/needs into a product line. I bet if Glossier had launched its products before launching Into The Gloss, it would not have been nearly as successful. It will be interesting to see if Glossier continues to be successful as a business, or if it will be surpassed by another D2C product line.

Leave a comment