Using AI to Spot eCommerce Trends Before They Go Mainstream

TL;DR

Retailers and marketers can now leverage sales data, AI, and lightweight automation to identify emerging trends before they become established. By identifying anomalies in eCommerce data, pricing, and consumer behavior, it’s possible to surface actionable insights that inform product recommendations, merchandising, and content. In this post, we show how we built a simple system using publicly available Amazon data and GPT to automate trend analysis and generate marketing-ready outputs.

The Challenge: Finding Trends Fast, Before Everyone Else

For many retailers, understanding consumer demand involves examining lagging indicators, such as monthly sales reports, seasonal trends, or anecdotal customer feedback. By the time a trend is clear, competitors have already jumped in. The reality is, most traditional reporting systems show what happened, not what’s about to happen. In a market defined by speed and saturation, brands need tools that can surface early signals and give them a head start.

The core challenge is not just having the data, but having the time and tools to spot what’s different, unusual, or on the rise. You’re not just trying to see what’s selling. You’re trying to understand why, and whether it’s about to snowball.

Opportunity: Tapping Into the Signals Across eComm, Search, and Social

There’s no shortage of signals. Platforms like Amazon, Google, TikTok, and even Reddit provide valuable insights into what people are buying, searching for, and discussing. However, while these sources are individually useful, their real power lies in their combination. The problem? They’re siloed, messy, and often overwhelming. To take advantage of these signals, businesses need a system that can regularly ingest this data, analyze it for meaning, not just volume, and surface trends that are not yet obvious. That’s where AI becomes a force multiplier.

Step One: Use AI to Find What’s Changing

We started with a relatively simple dataset: Amazon bestseller data. This included changes in sales rank over time, price fluctuations, review counts, availability, and product category data. While each metric alone offers limited value, when tracked together over time, they form patterns, some steady, some spiky.

AI helped uncover the initial factors driving demand and behavior. Then, using a basic Python script, we calculated a “trend momentum score” for each product, which is a weighted combination of rank changes over 1, 7, and 30 days. These scores helped us identify products moving faster than normal, flagging items with early momentum.

The next step was to go beyond detection. Once anomalies were identified, we utilized GPT to delve deeper, analyzing product titles, descriptions, and even brand names to understand why certain items might be trending. For example, one cluster of rising products featured terms like “keto,” “bulk pack,” and “low sugar”, clues that health-conscious snacking was gaining traction in specific subcategories.

How We Did It: Analyzing Amazon Data with AI

Using Amazon data, we ran a pipeline that first cleaned and ranked the data using simple anomaly detection. Then, we used GPT to extract keywords and interpret the results. A product with a 75% drop in sales rank over 7 days may seem insignificant on its own, but when paired with language like “high protein,” “gluten free,” and “family size,” it begins to tell a story about a shifting preference.

These aren’t just numbers, they’re starting points for action. AI helped us reduce the noise and spotlight the products and features worth watching. For instance, we found that lesser-known snack brands, such as Weighless and Vincinni, were showing rapid acceleration in sales rank, likely due to favorable pricing and emerging consumer preferences. By clustering these findings by subcategory, we were able to see where growth was concentrated and where opportunities were emerging.

From Insights to Automation: Making the Output Useful

Once trends were flagged, we automated the final steps. With GPT, we generated updated product descriptions using trending language, created headline ideas for blog posts or email campaigns, and even grouped products for bundle recommendations.

What would normally take a merchandising or marketing team hours of manual review became an automated insight engine. Imagine a Slack alert that tells you: “There’s a spike in high-protein cookies among new brands. Consider featuring a Keto Snack Bundle this week.” Or a live dashboard that ranks products not just by sales, but by change velocity—giving your team a playbook for where to act next.

And this approach is expandable. You can connect search trends, TikTok mentions, or inventory signals to the same system. The more inputs you feed, the smarter your outputs become.

Conclusion

This project focused not on creating a recommendation engine, but on developing a decision engine. By leveraging AI to identify anomalies and trends, and automating analysis and content creation, we built a streamlined system that allows brands to act proactively instead of reactively. Whether managing five SKUs or five thousand, the core objective is the same: understand your audience, recognize shifts, and respond faster than the market anticipates.

If you’re curious about building a system like this for your business or want help getting started with AI-powered trend analysis, let’s talk.