How important are new fashion items in your wardrobe? So important you’d be willing to take suggestions from a computer to update your closet? So important you’d be willing to rent used clothing to stay on-trend? Rent The Runway (RTR) thinks so, and they’re utilizing machine learning insights to give consumers a unique shopping experience.
In retail personalization has become increasingly important. In the crowded fashion market a generic experience is no longer acceptable. Companies have to differentiate their interactions, and they can utilize machine learning driven off the recent explosion of customer data to create highly personalized interactions.  and improve satisfaction, loyalty and ultimately customer lifetime value. In addition to enhancing revenues, machine learning can decrease costs. Machine learning can analyze customer data to identify trends, predict preferences, and optimize inventory planning.
Rent The Runway is a relatively new entrant in retail and they’re aiming to significantly change consumer behavior. RTR wants you to rent clothing from their selection of designer items rather than purchasing it directly from designers.  Without existing brand-value RTR needs to create a seamless and memorable experience so that consumers are willing to take a risk on both the RTR brand and the experience of clothing rentals. I think machine learning is essential for RTR to establish itself amongst shoppers.
RTR founders (and HBS graduates) Jennifer Hyman and Jennifer Fleiss recognized the depth of data they gather on consumers and have continually put it to use. As a technology-enabled rental application RTR collects a wealth of internal customer data on rental selections, designers, and shopper tendencies (regional, age-specific) then utilizes this data to create a unique shopping experience. RTR interprets supervised learning outputs with the aid of stylists to predict consumer preferences, suggest rentals, and push add-ons.  In addition, RTR uses reinforced learning to analyze unreported customer behavior on their interface. Through unreported and reported customer data RTR can identify trends to influence their future assortment decisions, focused on increasing both the quantity and quality of items available to renters, and glean insights for innovations that could improve their back-end interface.
In the short-term RTR is also focused on minimizing their risk in the rental market. They use machine learning to identify correlations amongst consumers that have lost, stolen, or damaged rentals that allows them to screen for potential abuses and minimize their exposure going forward.  In the long-term machine learning insights drive RTR’s core projects and new product offerings. Chief Analytic’s Officer Vijay Subramanian highlighted the role of machine learning and data at RTR “Giving you insights, giving you a pulse of what’s happening and why, and ascertaining how to change what we’re doing right now, and what we need to do longer-term…data has a very, very big influence on what we actually work on as a company.”  The Company’s introduction of a subscription-based option and their recent move into brick-and-mortar, in which customers can exchange rentals, were both driven by machine learning enabled customer-insights and are continually improved upon utilizing data .
Since inception RTR has strategically leveraged data and their machine learning insights have improved over time as more users have entered the platform and higher quality customer data has become available . However, in my prior experience in business development in the retail industry I witnessed the unique approach of stylists and creative directors and their value in shaping both the brand and product offerings. Ultimately, I believe machine learning has to be interpreted by a human to draw out the most relevant actionable insights. I worry RTR may rely too heavily on data and lose the human touch in their rental model. I would caution them to be wary of collecting too much irrelevant data and allowing past consumer choices to over-influence their decisions. RTR needs to keep a forward-looking eye on the fashion industry.
As RTR grows their brick-and-mortar footprint I think they should expand their use of analytics to customize their instore experience. Different regions and consumer psychographics heavily affect retail preferences. RTR can benefit from relying on data analytics to tweak their retail layouts, in-store inventory, and the tenets of their subscription model as they continue to roll out these initiatives.
I was fascinated by RTR’s use of machine learning to predict rental abuses. They certainly are at risk by lending out expensive products. How do you feel about them making potentially unfair conclusions about consumers to protect their downside? Is this different than banks approving or rejecting individuals for loans?
So much of brand-value and consumer behavior remains a mystery and is influenced in a multitude of ways – how valuable do you think the ‘human touch’ remains in creative industries (such as retail)? Do you think machine learning can replace creatives? (790 words)
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