{"id":35429,"date":"2018-11-13T19:50:36","date_gmt":"2018-11-14T00:50:36","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/rent-the-runway-wants-you-to-clean-out-your-closet-permanently\/"},"modified":"2018-11-13T19:50:36","modified_gmt":"2018-11-14T00:50:36","slug":"rent-the-runway-wants-you-to-clean-out-your-closetpermanently","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/rent-the-runway-wants-you-to-clean-out-your-closetpermanently\/","title":{"rendered":"Rent the Runway Wants You to Clean Out Your Closet\u2026Permanently"},"content":{"rendered":"

If the meteoric rise of ecommerce ushered in the death of traditional retail, Rent the Runway is poised to be the nail in the coffin. The company\u2019s value proposition of variety and affordability is perfectly aligned with societal shifts towards a more experiential, social media-driven, and sharing-focused economy. An emphasis on data and machine learning has driven RTR\u2019s success thus far; however, the company has a grand vision for the future and needs to advance these capabilities to capitalize on the opportunity ahead. According to RTR internal research, the average American woman buys 68 new pieces of clothing a year, 80% of which are rarely worn [1]. RTR plans to solve this problem, envisioning a future in which closets contain the bare necessities and are supplemented with its \u201cUnlimited\u201d subscription model. The company would effectively become the \u201cNetflix of fashion\u201d [2]. To achieve this lofty goal, it must double down on its data capabilities.<\/p>\n

The use of data and machine learning is important to RTR, because the company\u2019s success is based on its understanding of two complex concepts \u2013 logistics processes and consumer preferences \u2013 and its ability to respond quickly. First, the profitability of the business is based on the unit economics of its inventory and how many times a dress \u201cturns over\u201d [3]. If a dress is sitting idle, it is not making money. Therefore, the company must continuously be iterating on its processes of shipping, dry cleaning, and inventory management to ensure low turnaround time [4]. In fact, its dry-cleaning warehouse is the world\u2019s largest, processing 2,000 items per hour [5]. With each dress rental, RTR\u2019s data systems get smarter, understanding what is coming in and out of the warehouse, predicting dry cleaning and repair time, prioritizing the order of events, and understanding when inventory is likely to go out of service [6]. Regarding the consumer, RTR must understand and appeal to changing preferences. Fashion is particularly fickle, with major trends shifting every few months. The ability to analyze its customer base, predict fashion trends, and manage inventory is key to acquiring and retaining customers.<\/p>\n

\"\"<\/a>
Jennifer Hyman, CEO, Source: The New York Times<\/figcaption><\/figure>\n

According to its founders, RTR is a \u201cfashion company with a technology soul.\u201d As the business has scaled, it has collected a wealth of information from its consumers and embraced data as a key tenet of the business [7]. First, RTR has access to what their consumers are searching for, filtering by, and favoriting. They then use this data to personalize the online or in-app experience, increasing the likelihood of a rental. The business \u201ccontinually tweaks algorithms to identify pieces that customers might like and highlight them on the app\u2019s home screen, similar to recommendations offered by Amazon and Netflix\u201d [8]. Additionally, over 50% of consumers provide reviews after each rental, commenting on satisfaction, fit, and experience [8]. RTR applies these customer reviews to its inventory categorization, using an algorithm to crawl the text and understand which dresses are favored for specific events. Most importantly, RTR uses this data to inform its available inventory. Based on consumer searches, it preemptively understands when styles are changing and provides what is \u201cin style.\u201d In summary, CEO Jennifer Hyman states, \u201cWe\u2019re collecting hundreds of thousands of data points each week: style, fit of the garment, what occasion you wore it to, what else is going on in your life, who are you? We\u2019re creating one-to-one style personas for every user we have, which is helping us create an individual home page for you\u201d [8]. Chief Analytics Officer Vijay Subramanian foresees taking this capability further in the future. Instead of scrolling through the app and seeing a few items you may like, he wants to target the consumer so effectively that the first two personalized suggestions will likely convert to rentals [7].<\/p>\n

I agree, and I believe that RTR must further enhance its personalization to become a \u201ccloset in the cloud\u201d [1]. RTR has created a new category and needs to convince consumers to forgo their purchases in favor of rentals. To be successful, it must find a way to limit the overwhelming choice available to users and reduce the time spent browsing the expansive inventory. With enhanced personalization, RTR may be able to mirror Stitch Fix\u2019s capabilities, sending personalized packages on a bi-weekly basis based on the company\u2019s knowledge of an individual\u2019s fashion profile. Additionally, I foresee RTR becoming fully ingrained in the consumer\u2019s life, accessing personal calendars, viewing work and social events, and providing specific items tailored to the consumer\u2019s schedule. Data could become the new personal stylist.<\/p>\n

\"\"<\/a>
Source: Brand Channel<\/figcaption><\/figure>\n

What is the future of subscription fashion? Will the RTR Unlimited subscription see widespread adoption? Can fashion be data-driven, or will it always need a personal touch? (775 words)<\/p>\n

[1] Alexandra Schwartz, \u201cRent the Runway Wants to Lend You Your Look,\u201d The New Yorker, October 22, 2018, https:\/\/www.newyorker.com\/magazine\/2018\/10\/22\/rent-the-runway-wants-to-lend-you-your-look<\/a>, accessed November 2018.<\/p>\n

[2] Kyle Jones, \u201cHow this entrepreneur launched the \u2018Netflix of fashion,\u2019 a company now worth nearly $1 billion,\u201d Women in the World, January 5, 2018, https:\/\/womenintheworld.com\/2018\/01\/05\/how-this-entrepreneur-launched-the-netflix-of-fashion-a-company-now-worth-nearly-1-billion\/<\/a>, accessed November 2018.<\/p>\n

[3] Steven Bertoni, \u201cThe Secret Mojo Behind Rent the Runway\u2019s Rental Machine,\u201d Forbes, August 26, 2014, https:\/\/www.forbes.com\/sites\/stevenbertoni\/2014\/08\/26\/the-secret-mojo-behind-rent-the-runways-rental-machine\/#567ed4aa38bf<\/a>, accessed November 2018.<\/p>\n

[4] Sam Hollis, \u201cHow Rent the Runway built an $800 million business in a mom-and-pop industry,\u201d September 17, 2018, https:\/\/jilt.com\/upsell\/rent-the-runway-logistics\/<\/a>, accessed November 2018.<\/p>\n

[5] The Economist, \u201cRent the Runway is taking clothes-sharing mainstream.\u201d June 7, 2018, https:\/\/www.economist.com\/business\/2018\/06\/07\/rent-the-runway-is-taking-clothes-sharing-mainstream<\/a>, accessed November 2018.<\/p>\n

[6] Renee Boucher Ferguson, \u201cRent the Runway: Organizing Around Analytics,\u201d MIT Sloan Management Review, March 24, 2014, https:\/\/sloanreview.mit.edu\/article\/rent-the-runway-organizing-around-analytics\/<\/a>, accessed November 2018.<\/p>\n

[7] Deciding by Data, \u201cNever Do Laundry Again: How Rent the Runway is Creating a \u201cCloset in the Cloud\u201d That Lets Data Choose Your Outfit,\u201d June 1, 2018, podcast, https:\/\/decidingbydata.com\/2018\/06\/01\/rent-clothes-rent-the-runway-chief-analytics-officer-vijay-subramanian\/<\/a>, accessed November 2018.<\/p>\n

[8] Sheila Marikar, \u201cThe Transformational Bliss of Borrowing Your Office Clothes.\u201d The New York Times, October 12, 2018, https:\/\/www.nytimes.com\/2018\/10\/12\/business\/rent-the-runway-office-clothes.html<\/a>, accessed November 2018.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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