How to leverage AI-powered assortment planning in 2024
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For every fashion retail team, no time of year is without its challenges.
All year round, brands and retailers are trading over 3 timelines at once: current season performance, past season analysis, and future season planning.
No matter your role, your brain is always across three zones — and the challenge we face is the complexity of managing all of that simultaneously.
When you buy any given product, just for that one single product you automatically have many crucial decisions to make:
What are you going to price it at? How many units? The locations, the size runs, the margin targets… and that’s just the high-level decisions.
Your task then, is to perfectly balance that assortment. Wherever you are on the globe, you want to make sure that you’re sending the right seasonal product to the right place, and in the right category mix for the different consumers.
Then, you must consider the colors, the price bands, the silhouettes, etc.
Yes — it’s a lot — which is why teams are looking to simplify retail’s biggest challenges with advanced technology.
For buying and merchandising teams the challenge has always been the level of data that you could plan at, but can’t because you're being slowed down with BI tools, spreadsheets and pivot tables.
The struggle is not being able to keep up with the multitude of changes: dates, attributes, fabrics, and delivery schedules with the need to consolidate sheer volumes of data — and just not having the tools to do it.
The biggest challenges facing fashion teams working without advanced system processes are:
For all teams, it’s clear data points are expanding exponentially with every new plug-in and platform.
So, how do you make sense of all that data?
Artificial intelligence should be used to solve a lot of the heavy lifting of data consolidation, analysis and decision-making.
Right now, teams and businesses see the value of AI on a scale of 0-10, with 0 being complete hype and not useful for my day-to-day, and 10 being deeply valuable and role-enhancing.
Most businesses sit around the middle mark of 5, with the issue being they are unsure how to test for value.
The key is for teams to use AI processes that are practical and auditable.
To measure the value of AI platforms and tools, businesses can come to their own conclusions by asking and discerning:
Deep tagging capabilities offer insights that go beyond being able to see what’s been selling from specific tags and attributes.
It can deploy highly targeted and rich product attribution at a scale and speed that is not humanly possible.
This kind of dynamic product tagging trawls deeply into historical and current style data and imagery, identifying a unique and enhanced set of style attributes for every product — hours of work crunched in seconds.
The advantage of Style Arcade’s deep tagging for example, is the ability to consolidate all of these attributes and detect overall demand opportunities you otherwise would not have had access to — and apply these learnings to adjust your purchasing decisions to meet this demand.
Generative AI has paved the way for retailers to start to look at real-life applications, and we’re seeing that not only on the technology side but on the B2B side and for the customers. This is beginning to take shape in the following ways:
Originality and creativity are still key to your content. When you get the partnership between your brand’s creative vision and technological proficiency right, you can continually maintain a competitive edge.
Want more in-depth knowledge about the opportunities of Ai-powered assortment planning?
Watch the webinar here.
Image credit: Rachel Koukal