How to Build a Data Advantage in the Fashion Industry

By Andreas Arentoft, Partner & Go-to-market (Tech) at Precis Digital

It’s easy to collect ever-increasing amounts of data, and to comfort ourselves into believing that more data is always better. In fashion, where data is abundant, the difference between stepping out in a beautiful garment and being caught with your pants down comes down to having a clear vision of what you want to achieve and the knowhow to get there.

Especially in today’s digital landscape, marketing is heavily driven by machine learning and artificial intelligence models operated by large players like Google and Meta. In the past years, the role of data was to enable marketing decisions to be made by humans, nowadays, the role of data is to guide machines to generate the best possible outcomes. Fortunately, for fashion retailers, there’s a wealth of data that can be used to your advantage.

In this article, we’ll delve into the ways that fashion companies can avoid the data version of the emperor’s new clothes and ensure that their data advantage is tangible and drives results that everyone can see for real.

Knowing your collection

You can sell batteries without showing how they look. For clothes, appearance is everything. This makes the products that you choose to show one of the most important decisions in your media strategy.

Building a structured product analytics activation strategy is about identifying the strengths and weaknesses of your collection while balancing short and long term objectives. Measuring this can be done on two axes: ability to generate revenue and ability to capture attention. Accepting that these aren’t always obviously correlated allows a more strategic deployment of your collection to grow your business.

Identifying revenue drivers is quite intuitive, finding attention grabbing products is a bit more obscure. There are multiple ways to measure this:

  • On-page click-through-rate: To gauge the products featured on your website that generate most attention
  • Product ad click-through-rate: To measure the products features off your website (i.e. in ads) that generate the most interest
  • View-to-basket rate: Products that generate intent to purchase

By using revenue and some of the metrics listed above to identify revenue drivers and thumb-stoppers — products that get people’s attention and get them to stop scrolling and pay attention — we can map products into a matrix and use data to take the right action for each type of product so that it generates maximum value.

Future Overstock: If a product neither gets people interested in the brand nor drives significant revenue, you want to find a way to clear stock fast before the next season to avoid heavy discounts on pricing. This may be via more exposure in lower-funnel, product-focused ads or by pairing with best sellers on site to show customers great ways to match it with other products they’re interested in.

Showstoppers: A peculiar trait of the fashion industry is the Showstoppers — products that create buzz, but lack the mass appeal to make it into the basket frequently. Featuring these items in branding material helps customers understand the identity of your collection, and their thumb-stopping power makes them perfect for the low attention span arena of Social Media advertising. Use them strategically to connect with your customers and build your brand identity.

Basics: While your brand and products have a strong identity, your customers will also need to fill their wardrobe with basics. The simpler items in your collection are likely to be major revenue drivers, but don’t need to take centre-stage in your ads. Instead, focus on availability when the customer need arises — both by making them easy to find and also by ensuring sufficient stock coverage.

Trendsetters: These products hit the sweet spot of flying off the shelves and getting people talking. Like the Showstoppers, these items represent who you are and should be highly present in your branding activity. They may not crop up in every season, and you’d better make sure you make the most of it when lightning strikes and learn from their success!

Fashion is identity

We express who we are with the clothes we wear. This means fashion companies usually have a strongly defined customer base, which makes customer analytics that much more powerful. There are a plethora of ways to slice your customer segments, but in many cases you can be successful with four basic personas: new, base, loyal and lost. By differentiating your tactics for each, you can make data work towards your objectives.

New customers have the highest future potential, but are harder to acquire since they haven’t built up any positive brand experiences with you. Building a differentiated strategy in your marketing to optimise towards new customers, for example by excluding existing customers, adjusting creative, and ensuring that you send the right data signals to platforms like Google and Facebook to train them to find new customers, is key to growing your customer base.

For your most loyal customers, you don’t need to nudge them to buy your products. Many will do so anyway and your marketing spend may have a lower incremental effect. Focus instead on forging a stronger relationship and building a community. For example, featuring UGC (user generated content) in Social Media advertising can encourage organic sharing and build a stronger collective. Or learn from Burberry and their best-in-class example of community building by exploring new technology like the Metaverse (see here).

Your base customers are going to be your main revenue driver in the short-run, so you shouldn’t ignore them. The key objective should be efficient media planning and a key focus on attribution to understand the levers that truly drive value. By adopting a testing-first strategy and using your first-party data to validate impact on sales, you can continually improve how you market your collection with this segment.

Similarly, lost customers should have a strong focus on media buying efficiency and you’ll want to reduce ad exposure to this group of customers, as they have already formed an opinion about your brand which may be time consuming and expensive to change. The exception is brand revamps or new sub-brands which may spark new interest. In any case, the main objective should always be efficiency.

Designing a data strategy

If you truly know your customer and products it’s a boon for your business because it’s unique, actionable information only you can take advantage of. But how to get started with any of the tactics mentioned so far?

There are three steps you need to follow if you want to succeed with your data strategy: pick your objectives, build the data foundation, and then activate data. It needs to happen in this order to be focused and thus successful. Going in the wrong order can lead to catastrophic failure. For example, having a stock-clearing campaign may be a solid idea in the case of overstocked products, but if your goal is brand-building and you don’t have the data in place to exclude loyal customers, you’ll at best waste money selling to the wrong people and at worst erode the brand value you’ve worked so hard to create.

Getting started can seem like a monumental task. If your organisation is ill-equipped to take on the job, bringing external expertise can help get things off the ground. A good partner understands the technical requirements and can deliver niche skills you don’t want to develop in-house. But a truly great partner also brings in industry experts to adapt your media buying strategy so that it aligns with your core strategic goals. With the right ingredients, you’ll build a data strategy that delivers on its promise to deliver results, rather than ending up with an invisible garment that promises everything but leaves you exposed and confused.

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