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Fashion marketers are under pressure to do more with less, while staying ahead of fast-changing consumer trends. This recap from Fashion Decoded reveals four key strategies to stay profitable and relevant.
Net Profit Margins Decline in Fashion Retail from 2019 to 2024
The traditional fashion business model is no longer fit for purpose. Margins are shrinking, returns rates are higher than they were pre-pandemic, product discovery is poor and experiences are undifferentiated. The customer experience is fragmented; search is broken, there is a massive disconnect when landing from appealing social content to mismatched product pages and the experience is rarely personalised to each individual customer.
The next hard truth: the only growing segment in Fashion Retail is the resale market – one that is even more personal, contextual, and data-hungry than traditional eCommerce. Resale success depends on knowing the “why” behind the buy just as much as the “what.”
To benefit from this boom, brands should consider diversifying into the resale market – but this requires even better product attribution, as stock is often limited to one product and one size only.
Retailers are under pressure to move beyond reactive tactics and into intent-led, emotionally fluent retail. Fashion isn’t just about style anymore; it’s about expressing identity and values. Resale, sustainability, and discoverability are no longer nice-to-haves. They are prerequisites for staying relevant.
Gartner predicts that by 2026, 25% of traditional search traffic will be replaced by AI chatbots and virtual agents. These LLMs allow us to be more human with our language, moving from robotic keywords to using natural language. In this new environment, your product and contextual data isn’t just a backend resource. Jordan Lustig from Ann Taylor put it very well at Fashion Decoded :“You need to start treating your data like an asset. Why aren’t we looking at product data as an asset?”, but this requires understanding what this means in the context of each customer and each retailer’s experience.
When AI acts as a shopping agent, it reads contextual product content. It is not scanning for plain product tags that are distorted from human talk. Instead, it is interpreting intent. The nuance, tone, and structure of your product data will determine whether you get discovered, recommended, or ignored.
As LLMs are able to understand us so fluently, so customers are using natural language to converse with the models. This is about making you data speak the language of your customer, not the internal language of fashion retail departments. Good data lets you work smarter and boosts your discoverability. It enables teams to move faster, reduces time spent on repetitive tasks, and allows more space for creativity. But bad data? It feeds noise into every touchpoint.
We’re no longer talking about AI as a tool among others. Instead, AI is becoming a foundational layer – agentic, autonomous, and increasingly intuitive. With OpenAI launching GPT Agents and discovery integrations, the retail journey is being reshaped in real time.
Today, product data needs to read like a conversation. Models like ChatGPT don’t rely on keywords, but a whole story around your products. If your brand is known for a silhouette, a palette, or a styling attitude, that consistency becomes a signal AI can trust.
Discovery is being redefined. Customers have stopped browsing and are prompting instead. And AI is interpreting those prompts with astonishing speed and nuance. Brands that want to be surfaced need to sound consistent and clear across product feeds, PDPs, captions, and marketing.This isn’t just about AI understanding us. It’s about distinguishing our own, very human language that matches AI’s energy.
And, it turns our that stores really matter. The presence of a physical store positively impacts online conversion. Building your data structure to enhance your omnichannel strategy is key. Every action or omission has a ripple effect across the your entire retail operation.
Mairi also spoke about internationalisation, a key topic for most fashion retailers. She pointed out that brands can no longer scale in one geography. According to Mairi at Fashion Decoded, you need to be active in 8-12 markets just to sustain momentum. And with AI moving into territory once dominated by search engines, the urgency for a connected, coherent retail strategy is growing.
“You can never be too early – but you can be too late.” Azeem Azhar
The good news is that it is not too late. Now is the time to rethink your retail operating model and the underlying data that supports your business. With LLMs, it all starts with the data so you need to get your data house in order first. That data needs to flow consistently through all your systems. Once that data is in place, you will be able to predict intent, mission and the mindset of the customer which will allow for a seamless and delightful customer experience across all your channels. Get the right data aligned across all channels (yes, also in UCG and Youtube descriptions), and your brand will be discoverable across all channels and your experiences will be aligned regardless of how the customer chooses to interact with you.
Mapp Fashion sits at the intersection of everything the future demands: human stylist expertise, fashion-specific AI, structured data in the language of your customers, and intuitive experience design. It’s not a replacement for creativity – it’s an enabler for fluent, intact customer journeys.
The future of fashion retail isn’t just about selling more. It’s about selling smarter, in ways show the human behind tune buying decision. It’s about sounding relevant, and responding with precision.
Want to go deeper? Sign up to get our upcoming State of Fashion report and see how fashion-first data, intent-driven AI, and curated creativity can transform your retail stack.
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