Data/TECHNOLOGY – Can The Fashion Industry Handle It?

Historically, data and fashion don’t mix, but times are changing and a new intersection between the tech and fashion industries is emerging. The industry has become increasingly dependent on data to make informed decisions on consumer choices and market predictions, yet it continues to lack a deep understanding of the “good” data from the “bad” data.

How can data bridge the gap between technology and fashion? 

Of late, I’ve observed fashion retailers and brands increasingly turning to data analytics. Prominent examples such as Amazon and Zalando have introduced personalized search options to adapt offerings for each customer, indicating that data-driven fashion is not a trend, but a significant industry shift toward digitalization and a more customer-centric approach. 

Data is now available not only from consumers’ search histories but from websites, social media and apps. That said, I’ve seen that the amount of data available from non-sales-related sources has increased dramatically in the last couple of years. Retailers are even starting to use cognitive computing to discover more about what customers might want and deliver the desired experience, but what is next?

Photo by Andrea Piacquadio

Good data, bad data? Reliable data. 

Marketing professionals, especially on the business side of fashion, love to see data but are often unsure of how to best approach it. In my work as a CMO in the fashion industry, I’ve seen that this space is just beginning to collect, clean, enrich and transform data in order to draw insightful conclusions, with the sole goal of improving decision-making. However, not every data set is designed to do this; as consumers spend more online and engage more with brands, there is a large amount of so-called bad data around — not qualified, not specified and simply misinterpreted. Here is an example of how data can be misunderstood:

When digital fashion weeks became a new norm, my company reported instances where media impact value decreased for some events and brands. Of course, when you compare the media buzz of all the shows from the last year to this year, the numbers might look negative. But, anyone who knows about data analysis knows that context is key in order to have a true understanding of the bigger picture. For example, even with a much more restricted guest list, Victoria Beckham was able to generate $9.6M in MIV, simply by opting for a “hybrid” model that featured both a physical event with VIP attendees and a digital screening. Given the pandemic’s restrictions, this is a noteworthy outcome.

In Paris, we had even bigger surprises. While the overall MIV decreased from the previous Fashion Week, top brands that took a strong “hybrid” approach experienced an increase in their MIV, including Dior, Louis Vuitton and Chanel. How did this increase happen? In some instances, it was thanks to celebrities, who had the most influence in Paris. However, overall it can be attributed to their stronger digital presences, which the brands amped up this season.

So, what’s the lesson to be learned? Data analysis is a strategic and at times challenging field, but there are some insightful learnings that I am happy to share:

Photo by Roberto Nickson

Know your strategic goals in order to look at the right data.

The way in which brands approach the data tells a lot about their strategies. To start, you might want to cut through the noise and focus on the most important topic. Understanding what kind of data you are looking for first helps you hone in on what matters.

Moreover, it’s important to not try to navigate a million data points at once; instead, direct your attention toward the KPIs that will allow you to extract valuable insights and conclusions more easily. You could also sort the data sets by ranking the importance of media attention or number of placements per season, or the number of views of the latest show — but even so, this only tells you part of the story.

Understand the importance of historical data.

To continue discovering what else lies behind the data, it’s key to consider comparisons; that’s why using historical data is important. Looking at the numbers from last year might actually be a useful, insightful exercise to see the entire dynamic of how a brand is evolving during longer periods of time. Additionally, context is essential, as it provides perspective on the past while allowing you to make decisions on the future of your strategy.

Photo by Ali Pazani

What’s the story within?

Lastly, to complete the story and add depth to your context, consider the current climate. The landscape is changing rapidly and brands are making adjustments to respond to hybrid presentation formats. Even without real crowds in attendance, hosting an event in person and live could produce a large amount of buzz, especially for lesser known brands. The number of attendees or stories mentioning the show is important, of course — but so is the quality of conversation within the given context. 

In trying times, maintaining brand equity is fundamental and, as the landscape changes, consumers and brands are looking for reliable information. While data analysis has been on the radar for the fashion industry over the last few years, now it is time to uncover the mystery and examine the practical implications of technology-driven solutions. Data is not something we should be afraid of; strategic decision-making in navigating your brand through uncertain times depends on it. 

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