What Does Your Brand Actually Look Like in Consumers' Minds? This Company Uses AI to Read 7 Million Documents a Day to Find Out

I came across a fascinating case study recently, and I couldn't resist sharing it with you.

You know what keeps brands in fashion, beauty, and lifestyle up at night?

It's not that they can't make good products. It's not that they can't afford ads.

It's that they have absolutely no idea how consumers actually perceive them.

A Brand Manager's Nightmare

Think about it.

You're the marketing lead at a fashion brand. You've spent a fortune on celebrity endorsements, influencer seeding （Chinese marketing slang for influencer-driven product discovery）, brand collaborations. And then? Then you sit in your office, staring at an endless scroll of comments, posts, short videos, trying to figure out one thing:

In consumers' minds, what does our brand actually stand for?

It sounds simple. In reality, it's enough to make anyone crack.

Because every day, billions of people are talking about brands online. Read them one by one? Impossible. Run a survey? Lagging, small sample, and inaccurate. What you get is always yesterday's data, describing what happened last week.

The message the brand wants to send, and the image consumers actually feel — there's an entire galaxy between them.

There's a company called Launchmetrics that does exactly one thing: helping fashion, beauty, and lifestyle brands understand what they really look like in consumers' minds.

What Does It Mean to Read 7 Million Documents a Day?

Launchmetrics built a data lake on AWS, using Amazon S3 as the underlying storage.

How many documents do they process every day?

7 million.

Every single day.

What's in those 7 million? Blog posts, news articles, social media posts, comments, product reviews. If it's a public online discussion about a brand, they want it.

Then they use machine learning models to chew through all of it, extract keywords, and figure out what people are actually saying when they talk about a brand.

For example, say you're a footwear brand. Launchmetrics' system notices that in the past three months, online discussions about your "sole durability" have jumped 40%.

OK, here's the real question.

It went up 40% — so what? How much does this "durability" discussion matter to your brand's core identity? Is it just noise, or is it a genuine trend worth paying attention to?

In the past, this kind of judgment call needed a data scientist. Now, generative AI has arrived.

What Does It Mean to "Turn Data into Stories" with Generative AI?

When AWS launched Amazon Bedrock, Launchmetrics was among the first to jump in. What is Amazon Bedrock? Simply put, it's a fully managed service on AWS that lets you directly call various large language models.

Their CTO, Pau Montero Parés, said something that really stuck with me.

He said that with generative AI, here's what they did:

Two steps.

Step one: machine learning models extract keywords and trends from those 7 million documents. Which words are getting mentioned a lot? Are the frequencies going up or down?

Step two: the extracted trends are handed off to large language models for scoring. What kind of scoring? How relevant is this trend to your brand's different dimensions? Is it product quality? Design style? Brand identity? The LLM helps you quantify the connection.

And then, the most critical step.

Generative AI takes those cold numbers and relationships and translates them into language that marketers can actually understand.

What does that mean?

Before, a data scientist would hand you a spreadsheet — a screen full of numbers that just left you overwhelmed. Now, the AI tells you directly: "In the past 30 days, among discussions about Brand X, buzz around the 'comfort' dimension rose 22%, concentrated mainly on Xiaohongshu （China's Instagram-like social platform） and Weibo （China's Twitter-like platform）, highly aligned with the brand's 'accessible luxury' identity. Recommend highlighting comfort as a selling point in summer new product promotions."

You don't need to understand data science to make a decision from this.

This is where AI delivers its real value. It helps you see what you could never see on your own.

From Five Months to a Few Weeks

What results did this bring?

Launchmetrics said that before, developing a new prototype solution could take up to 5 months.

5 months. Half a year, practically gone.

After adopting generative AI? A few weeks.

How could it get that much faster? Because large language models are inherently good at understanding the semantics of natural language. Work that used to require labeling data, training custom models, and endlessly tuning parameters — a lot of it can now be done directly by an LLM.

And here's something even more impressive.

Some new brands have almost no discussion data online. What do you do? Launchmetrics uses generative AI to create synthetic datasets, simulating the kinds of discussions a brand might generate in different scenarios, and then uses that synthetic data to bootstrap the analysis.

The CTO said: "This gives us the ability to scale from managing hundreds of brands to thousands of brands."

Hundreds to thousands. A 10x leap in scale.

AI Can Now Understand "Femininity"

One last point that I found especially interesting.

Launchmetrics discovered that generative AI could actually understand concepts that are incredibly vague and abstract.

What do I mean?

Think about the word "femininity." How do you define it? Elegance? Gentleness? Strength? Confidence? Everyone understands it differently.

If you asked a programmer to write an algorithm to determine "does this text convey femininity," they'd have a breakdown on the spot.

But a large language model can do it.

Because it has read massive amounts of text, it understands what "femininity" means in different contexts. By the same logic, "masculinity," "creativity," "avant-garde" — the concepts that brands most want to convey but are the hardest to quantify — an LLM can give you a solid judgment on all of them.

The CTO said: "These were concepts we couldn't touch at all just a few months ago. This is a complete game-changer."

So What?

After reading this case study, a few thoughts.

The biggest value of generative AI in marketing is helping you listen to consumers. Helping you write copy or make posters? That's all surface-level. Helping you hear what consumers are actually thinking from 7 million documents a day? That's the killer feature.

What makes a platform like AWS truly powerful is that it has lowered the barrier to using large language models to almost nothing. Amazon Bedrock lets a company of Launchmetrics' scale skip building models and infrastructure — just call the API and get access to the best AI capabilities in the world.

And there's an even bigger trend: brand perception is shifting from "intuition-driven" to "data-driven." Before, you relied on a brand manager's experience and gut. Now, you rely on real-time analysis of 7 million documents a day.

Maybe before long, when you're making brand decisions, sitting next to you won't just be an experienced director — it'll also be an AI that has already read every single online discussion about your brand from that day.

It won't make the decision for you.

But it will lay out the real opinions of consumers worldwide, right in front of you.

Wouldn't you say that's a game-changer?