The fast fashion revolution is still having a profound effect on the way fashion businesses get their products to market. From giants like Inditex and H&M coming up with innovative ways to get new designs in stores just 24 hours after their conception to younger brands like Boohoo capturing a young and digitally-savvy audience, there’s plenty to learn from these leaders in speed.
The logistical operations required to deliver products from factory to store in such short order are immensely complex and expensive – but when it comes to ecommerce there are thankfully simpler options to improve a fashion brands’ speed to market.
Driving the change
Firstly, we should consider why getting to market quickly is becoming a key objective for businesses in the fashion industry. There are a handful of reasons for this:
The longer a product spends being warehoused or merchandised without a purchase, the slower it is to realise revenue and profit. Cash tied up in stock can’t be used to develop the business in other areas.
The market is moving faster than ever before, in response to shorter attention spans and the breakup of seasonal release cycles thanks to new innovations like capsule collections and ‘drops’. The amount of time a business has to recognise a trend, design and manufacture a product that captures a target market’s desire, and then make it available to them has dropped significantly in recent years.
If one brand isn’t ready with a new look or collection, another is. Getting products to market quickly means beating the competition to sales.
So, how can fashion businesses of all shapes and sizes learn from the digitally native masters and become quicker to release products online?
The key: product data
It’s not the most exciting topic, but the bits and bytes of product data that make up online product listings and adverts are the building blocks and raw materials that go into every digital marketing and sales push. This data is foundational to a successful campaign and can make the difference between massive returns on digital investment and underperformance.
With that in mind, here’s how to make sure that raw material is in the best possible shape to create high quality product representations online, and how to make that happen fast.
Step One: Centralise
Having data in separate systems is a major problem for many brands, particularly legacy brands with a background in bricks and mortar retail. Adapting to digital-first strategies requires a revamp of the data processes in the business.
For an ecommerce or digital team trying to gather all the requisite data to deliver a persuasive and rich experience for customers, it can be like a jigsaw puzzle where the pieces come in 8 different boxes, and some are missing entirely.
Having a single source of truth is the goal for digital teams, but it provides benefits right across the organisation. Equally, it’s the responsibility of everyone to buy into the idea and maintain the standard of data.
Step Two: Enrich
Once the data is in a single environment to be worked on holistically, the team need to know where to focus their time. Which attributes and fields should they spend time adding information to, to best deliver results online?
Understanding the mix of channels is essential here. A team with subject matter experts and dedicated channel masters will have an easier time segmenting and enriching data in a way that gets the most effective changes done first.
The challenge with this approach is that channel experts are hard to come by, and as best practices can be murky and unprovable without hard data, it’s hard to know how right they really are. Luckily, next-generation technologies can deliver unparalleled insights, backed by data, to automatically supply data and recommend the most valuable actions to take during enrichment.
Step Three: Mapping channel structures
Ideally, having a single enriched data set would be all brands need to get to market online. Unfortunately the reality is somewhat different. While most channels look for the same core information, the way they require the data to be structured is unique to every channel.
For example, Amazon and Google accept lots of the same data – sizing, colour, title, description, images, material, country of origin – but when it comes to sending this information to the channel, they need it in completely different structures.
Multichannel tools have gotten around this problem by simply replicating the channel structures, so users would fill out one set of enriched data under an Amazon structure, and then have to figure out how this applied to the Google data schema. This duplicates work, and requires the user to understand the best way to format the information for each channel.
Machine learning technology is able to constantly analyse and understand channel structures, mapping like for like. This means that the same set of data (sizing, colour, title, description, images) can be automatically adapted to fit each channel’s requirements without users needing to translate it. That means much more time for ecommerce teams to spend enriching products and getting them online, and less time duplicating that work for every potential channel.
Technology and culture transformation are vital
As the importance of speed to market continues to increase, more and more fashion businesses are investing in technological solutions that allow them to change their go-to-market process online.
Above all, preparation is essential. For large established brands that are used to processes that evolved during decades of trading exclusively offline, the change can be disruptive and challenging. Creating a culture of responsibility for data and delivering the right solutions to employees are the key ingredients for success in speed-to-market transformation online. Each requires the other - teams need the right tools to deliver outcomes, and they need to understand the importance of those outcomes.