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This AI Trick Called RAG Is Turning Fashion Marketing on Its Head

by Alex GenoveseJanuary 9th, 2025

Too Long; Didn't Read

RAG is a way to give the AI access to a massive library of information. It makes creating highly targeted and relevant material much easier. RAG is helping fashion retailers speak directly to their customers.

featured image - This AI Trick Called RAG Is Turning Fashion Marketing on Its Head

First, let's get clear on what RAG is. Imagine you have a super-smart AI that's great at writing, creating, and communicating. But this AI, like a lot of us, doesn't know everything. It can't keep all the details about every product, trend, and customer in its head.

It's a way to give the AI access to a massive library of information. When the AI needs to generate a response or create content, it first "retrieves" the relevant bits of info from this library and then uses them to inform its output.

Think of it as giving your creative writer an incredibly well-organized and detailed style guide to make sure everything they produce is spot-on.

Why is This a Big Deal for Fashion Retail?

The fashion industry is notoriously fast-paced and detail-oriented. Trends change in the blink of an eye, product lines are constantly updated, and customers have increasingly specific tastes. This means generating relevant content, whether for ads or email campaigns, can be a real challenge.

Traditional methods, where you might rely on human copywriters to keep up with all these shifts, can be slow, expensive, and prone to errors. RAG offers a more efficient and precise way to manage this content creation. By providing the AI instant access to updated information about inventory, customer preferences, and the latest trends, RAG makes creating highly targeted and relevant material much easier.

So, forget generic mass emails and ads that don't quite hit the mark. RAG is helping fashion retailers speak directly to their customers, offering personalized experiences that drive engagement and sales.

Let's look at some practical examples in Advertising and Email Marketing.

1. Hyper-Personalized Advertising:"The Outfit You Didn't Know You Needed"

Imagine a customer named Sarah. She's been browsing your online store and has looked at a few bohemian-style dresses, some sandals, and a couple of straw hats.

With a traditional advertising approach, you might just send her a generic ad about your summer collection.

With RAG, the system can analyze her browsing history, pull in details about her past purchases, and compare it to inventory.

Instead of a generic ad, Sarah might see an ad that says:

"Hey Sarah, we think you'll love this new sundress we just got in. It pairs perfectly with those sandals you were looking at last week, and we've even got a new straw hat that matches perfectly. Complete your summer look now."

The AI-generated this ad by retrieving the information about her specific browsing history, cross-referencing with the product catalogue, and then suggesting a cohesive outfit. This kind of hyper-personalization creates a more compelling experience for the customer.

2. Dynamic Email Newsletters:"Your Style, Curated Just for You"

Generic email newsletters are often ignored. The problem is that many receive emails unrelated to our tastes or purchases.

With RAG, fashion retailers can create emails tailored to a customer's unique style based on past purchases, browsing habits, and the latest trends.

Instead of "Check out our New Arrivals", the subject of the email might be

"Hey [customer_name], we've handpicked some pieces we think you'll love!". And the body could have recommendations like: "Based on your love for vintage dresses, we think you would be thrilled with our latest drop from our sustainable collection, a beautiful floral dress. We also recommend a look at our new shoes in similar styles". The email goes further by referencing previous purchases and suggesting similar items.

These emails are not just personalized; they are dynamic, updating automatically as new products come in or the customers' preferences evolve.

3. Location-Based Advertising: "Bundle Up for the Weather"

Let’s say you're a fashion retailer selling in several cities, and it’s getting colder in the northeast while the sun is still shining in California. With RAG you can create localized ads that adapt to the weather.

For example, customers in New York could see ads featuring your new line of coats, scarves, and hats. The AI could pull real-time weather data, cross-reference that with customer location, and serve ads with the right products at the right time.

The ad might read: "Winter is coming to NY! Stay warm with our new collection of wool coats. We also have great options of scarves and hats for you".

Meanwhile, someone in Los Angeles would see a completely different ad:

"California sun is calling! Check out our new summer collection including these amazing dresses and sunglasses"

This location-specific approach makes your advertising feel more relevant.

4. Responding to User Queries with Real-time Inventory Information

Imagine a customer asking through your website's chat: "Do you have that red dress in size medium in stock?"

Without RAG, a customer service agent might need to manually check the inventory system, which could take a few minutes.

With RAG, the AI chatbot can instantly retrieve real-time data from the inventory database and respond accurately. It can even suggest alternatives or complementary items if the dress is out of stock, or provide photos, user reviews, and related content.

A chatbot response might look like this: "Hello! Yes, we have that red dress in size medium in stock. Would you like to order it now? We also have a beautiful red bag that will perfectly match the dress!".

This real-time, accurate information creates a much smoother and more satisfying customer experience.