Rent The Runway: Organizing Around Analytics

Treating analytics as a strategic asset at the senior level has enabled a fashion-focused startup to innovate.

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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Dolce and Gabbana.

Vera Wang.

Narciso Rodriguez. Hervé Léger.

If you know them and love them but don’t have the budget to buy their fabulous fashions, there’s a solution: Rent a designer dress.

Rent The Runway enables just that: The “collaborative consumption” startup rents designer “occasion” dresses and accessories — for weddings, parties, black tie affairs — at a fraction of what it would cost to buy them. The company, founded by two Harvard Business School classmates, caters to its savvy, fashion-forward clientele: It offers a free second size (a must); on-call stylist advice; and, a new feature on the site, thousands of customer submitted reviews and photos of designer dress moments that (this is important) can be parsed through individual customer measurements.

These customer experience services are, in no small measure, what sets Rent The Runway apart from its small pool of competitors. And they’re driven by data and analytics — insights that are, according to Vijay Subramanian, chief analytics officer at Rent the Runway, no easy task to operationalize.

I spoke with Vijay Subramanian as part of our fourth annual look into how data and analytics are impacting global management practices [see our interim report]. He explained how he got started with Rent The Runway (a Bain Capitol investor connected him with the CEO), how his role evolved to a C-suite position (executives realized the value of data insights) and the ways in which Rent The Runway organizes around analytics to maximize customer experience.

To understand how all of these pieces come into play, it’s important to know how Rent The Runway operates. Like any traditional e-commerce business, customers visit the site and select products — dresses and accessories. They reserve these products, pay through an online checkout mechanism, and the products are shipped to the customer.

Where Rent The Runway’s process differs from others is on the back end: fulfillment, logistics — and especially, brand-building.

This is where analytics comes in.

Unlike traditional e-commerce companies that fulfill orders as they come in and process returns separately, Rent The Runway’s inventory is not just in a warehouse; it could also be already out to the customer or in transit or at various stages of being processed. The back-end (homegrown) system has to know not only what’s coming in and going out, but also how to prioritize the order of events, factoring in dry cleaning and repairs (missing sequins and beads, for example, must be replaced before a dress is shipped to its next wearer).

The company’s business model rides on an awesome customer experience — and waiting impatiently isn’t part of that. So the company works to achieve a one-day turnaround for merchandise.

To accomplish both, Rent the Runway has organized around analytics in two distinct ways: First, Subramanian’s position is at the C-level. As such, he plays a pivotal role in strategic decision making at the company. Secondly, data analysts are part of cross-functional teams, known as pods, which define and deliver on key projects.

“Everything that we do that’s strategic and meaningful, we have a cross-functional team,” says Subramanian. “For example, on a four-person team, I sponsor two people. I help figure out broadly what they should be working on fundamentally, and then the team works on challenging and modifying that vision, translating it and developing that product, then implementing it, testing it, learning from it.”

By marrying the two components of his role — being a C-level strategist and, with the heads of product and engineering, establishing the projects and products on which the company executes— Subramanian is able to utilize deep data insights to influence the organization.

What is a key ingredient in this approach? Subramanian believes it is treating data as an asset, at the senior level.

“There’s a lot of hype around big data,” he said. “A lot of organizations end up focusing on the technology aspects of data — how do you retrieve data? How fast are my reports refreshed? Largely those are important but technology problems. The biggest problem in all of this is how do you use data to change the company? How do you make decisions day in and day out?

“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 — that’s really what I mean by treating data as an asset at the senior level,” says Subramanian. “Because data has a very, very big influence on what we actually work on as a company.”

Rent The Runway’s approach to data jibes to a large extent with our research findings. When we asked the 2,500 respondents to our recent data & analytics survey if their organization treats data as a core asset, 51% of Analytical Innovators strongly agreed, versus 15% of Analytical Practitioners and just 6% Analytically Challenged.

We defined Analytical Innovators as those that strongly agreed that the use of analytics has enabled their organization to better compete and to innovate.

And what are some measures of success for Rent The Runway?

According to Forbes, since its founding in 2009, the company has raised $54.4 million in investments. But a more visible sign of its success, perhaps, is that “its inventory dressed 85% of the ladies who attended President Obama’s second inauguration.”

Talk about a red carpet.

Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

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