The Wall Street Journal published an article titled “Behind the Making of an ‘It’ Dress”. It’s the story of how fashion designer Michelle Smith tried to create a viral dress, for lack of a better word.
Here’s an excerpt from the article:
What happened next illustrates the changes currently roiling the fashion industry. Social media, online sales and its own store network let Milly move quickly and circumvent long-held fashion industry practices. The result: [the dress] became a best seller.
The article is written for a fashion-focused audience, not an engineering audience. Still, it’s easy to guess how thoroughly Ms. Smith relied on data during the manufacturing and marketing processes.
For example, after she showed the dress at a fashion show, Ms. Smith noticed the dress receiving more social media mentions than expected. She posted about the dress a couple of times herself, which further stoked interest.
That anecdote seems obvious. Of course a dress at a fashion show will receive attention on social media. The key is the data. I’m sure Ms. Smith and her team have expectations for the receptions their new releases will receive on social media. Either the frequency of mentions, or the kinds of people mentioning this new dress, or the specific networks on which conversations were unfolding, gave clues to the dress’ skyrocketing popularity.
Ms. Smith then faced a serious manufacturing challenge: her preferred fabric came from Italy. It would take months for a new order to arrive. Fortunately, the team found some fabric samples in their office and used them to create some dresses to meet emerging demand.
The fashion lifecycle is short. Trends can appear and disappear in a matter of weeks or months. Consumers are also sensitive to specific types of fabric, and specific colors. If any one element is off, an otherwise hot dress might immediately go cold.
Now you get into some serious issues of business strategy. Remember how Richard Rummelt breaks down the kernel of strategy? It all starts by clearly defining the challenge your company faces. For Ms. Smith, her core challenge may indeed be responding swiftly enough to changing fashion trends.
Now, it’s possible some other challenge is more profound. Maybe she can’t reliably design fashionable dresses. Maybe she can’t reduce her costs to compete for the customers she targets. Maybe she can’t form the relationships she needs to get into the retail outlets she wants to be in. Who knows.
Let’s use the challenge around a swift response to fashion trends as an example, though. If that’s the case, she has several options available, in terms of how to respond. She could find a way to manufacture the fabric she needs locally. She could simplify the dress manufacturing process, such that she could complete several time-consuming steps before settling on a final design.
The possibilities are practically endless. This is another opportunity for the use of data. She could measure how quickly she moves from dress design to first dollar of revenue. She could break the process down and measure from design to prototype, or from prototype to fashion show, or from fashion show to retail store.
I don’t know nearly enough about the fashion industry to be helpful here. The point is that even something as artistic as fashion can pull a ton of value from data. It’s easy to see something like fashion, and assume you must rely on experience, gut instinct, and taste. All of that might be true. But even in this arena, data matters. A lot. And stories like these can help us remember that.