Daina Burnes Linton | Photo: The Lean Startup Conference/Jakub Mosur and Erin Lubin
The Lean Startup method tells you to test your product idea with customers before you spend time and money building something you aren’t sure people want.
But how can you test a product without having the actual product? In her talk at the 2013 Lean Startup Conference, Daina Burnes Linton, CEO of Fashion Metric, explained how her company had done just that. As a result of their approach, Linton said, “Fashion Metric is today is very different from what our original idea was”—and she considered that a very big success. Here’s how Fashion Metric started:
We challenged ourselves to really understand the real problem the customer was experiencing…. We thought clothing shoppers—shoppers in stores and malls—might have a very difficult time deciding what to buy. Maybe it’s because they’re alone, and they’re not with their friends, and they can’t get their opinions. So, we thought, “Well, wouldn’t it be great if there was a mobile app, and you could take a picture of what you’re trying to figure out what to buy, and you can gain access to a personal stylist that can give you advice in real time and help make your purchase decisions?” Sounds like it could be a reasonable idea, right? Of course, friends and family always say, “Oh, yeah, that’s a great idea. Build that. That sounds awesome.” But we weren’t sure if we were solving a real problem, and so, we decided to really understand: Is this a real problem that customers are experiencing when they’re shopping in stores? So, we talked to who we thought our customer was all over the country. We went to malls in Los Angeles, New York City, and San Francisco. We asked, “What’s the biggest problem that you have when you’re shopping for clothes?” Very open-ended. What we found when we did this exercise was that not a single person—not one person—gave us the natural response that they had a hard time deciding what to buy. Not one person. So we almost built an app to solve that problem that didn’t exist.
By doing targeted customer research before building their product, the Fashion Metric team gave themselves 20/20 hindsight.
Of course, if they had stopped there, the company wouldn’t exist today. Instead, they looked carefully at the data they were gathering and realized that 90 percent of the men they spoke with said that finding clothes that fit was a problem. So the team dug deeper and learned that men particularly struggled to find dress shirts that fit well. With a clear trend around a real problem, Fashion Metric now had data to start building. Rather than code up a site, however, the team decided to validate their idea with an MVP that consisted of two parts: 1. a landing page asking for potential customers’ email addresses, and 2. follow-up phone interviews to gather customer sizing information. (Note that Daina is an engineer, so holding off on the coding was no small act of will.)
Before writing a single line of code, all we had was that landing page, and we learned a lot. We had an accelerated understanding of the problem; we were no longer building an app to solve a problem that didn’t exist; we understood the depth of the problem. We were able to see how far customers were willing to go to solve it. We were starting to see some trends in the data, to understand what questions we could ask, and whether or not it was technically feasible to solve the problem. You would think at that point, “Okay, great. Build something,” right? “Build the whole thing.” But we didn’t.
To find out what Fashion Metric did next, scroll down to watch or listen to Daina’s 12-minute talk. We’ve also included the full, unedited transcript below.
In the comments, we’d love to hear your best advice for finding relevant customers to interview before you have a product to show. Please tell us about the idea you were testing and how you found people. B2B and B2C ideas equally welcome! – Eds
Daina Linton is the co-founder and CEO of Fashion Metric, a company that builds technologies to increase sales and reduce returns in apparel e-commerce by improving fit accuracy. Fashion Metric’s comprehensive data-driven technology calculates a customers’ full compliment of measurements based on a seed of information provided by the customer. Fashion Metric then uses this algorithm output to fit customers in made-to-measure clothing or integrated on e-commerce platforms that carry “off-the-rack” sizes. A trained engineer, Daina pursued her degree while simultaneously holding several research internships at university affiliated hospital research labs. Eventually, she left a PhD engineering program at UCLA to parlay her experience in data analytics and image-processing methodologies and ultimately launch Fashion Metric. Follow her on Twitter.
Sarah Gaviser Leslie is a corporate storyteller and executive communications consultant in Silicon Valley.
Includes terms from the talk that we didn’t quote above. If there was a term you didn’t know that we haven’t defined, please let us know—we want to help! Also, if you have a better definition or an addition to a definition, shoot us a note.
MVP. An MVP, or minimum viable product, is an experiment that helps you quickly validate—or often invalidate—a theory you have about the potential for a new product or service. (Although often a stripped-down version of your product, an MVP is different from a prototype, which is intended to test a product itself and usually answers design or technical questions.)
The minimum concept is key because, in order to move rapidly and definitively, you want your experiments to include only features that help you test your current theory about what will happen when your customers interact with your product. Everything else is not only a waste of time and money, but can also cloud your results. The viable concept is key, too, in that MVPs have to actually produce test results you can learn from, which means it has to work on some meaningful level for customers.
The classic example is that if you’re trying to test the demand for a new service, you might do that by putting up a one-page website where people can pre-order the service before you’ve spent any time developing the actual thing or hiring people to make it happen. By the same token, if you’re testing a hypothesis around customer use of a new jet engine, then you have to make sure it will fly safely—in order for it to be truly viable, or feasible.
Landing page. A web page with a form to collect visitor info. HubSpot has an opinionated take on landing pages.
Ideation. Fancy word for brainstorming or idea formation.
Concierge. A type of MVP in which you manually fulfill a service that you’re thinking of automating in your product. A concierge MVP usually doesn’t require building much, if anything, and the high-touch interaction with customers lets you learn a lot.
Intelligence engine. Software that analyzes raw data to create useful insights.