How Profasee Uses Large Language Models to Identify Competitors on Amazon

The Amazon marketplace isn’t always easy to navigate. Sellers are constantly trying to outsmart their competition and traditional methods aren’t always enough to stand out.

At Profasee, we harness the power of large language models (LLMs) to pinpoint your competitors with accuracy.

Why Traditional Methods Aren’t Always Enough

Most Amazon sellers rely on tools like reverse ASIN lookup to identify competitors. While this method can provide a list of products that are similar to yours, it often misses the mark in several key areas.

First, reverse ASIN lookup tends to be quite limited. It only shows you products that share the same ASIN, which doesn’t always capture the full scope of your competition. Many competitors might have similar products under different ASINs that won’t show up in these searches.

Secondly, this method lacks depth in analyzing the competitive landscape. It doesn’t consider other factors like search terms, product descriptions, and user reviews that could provide a more comprehensive picture of who your real competitors are.

How Profasee Does It Better

We’ve developed a multi-step process that leverages LLMs to go beyond the basics to deliver a thorough analysis of your competition. Here’s how it works:

Step 1: Scraping Your Listings

The journey begins with us scraping the names and descriptions of your product listings. We gather all the relevant information necessary to understand exactly what you’re selling. This step is crucial because the details in your listings hold the key to identifying accurate search terms.

Step 2: Identifying Search Terms

Next, we deploy our LLMs to sift through the scraped data and pinpoint the most relevant search terms. These models are trained to understand context and nuances in language, which means they can identify keywords and phrases that a traditional keyword tool might overlook.

Think of it like having a conversation with a highly knowledgeable friend who knows all the ins and outs of the industry. Our LLMs can pick up on subtleties and suggest search terms that are more likely to lead to competitive insights.

Step 3: Feeding Search Terms to Amazon SERP

Armed with a robust set of search terms, we then turn to Amazon’s Search Engine Results Page (SERP). We input the search terms to gather a comprehensive list of all the names and descriptions of products that compete against those terms. This approach ensures we’re casting a wide net and capturing a full spectrum of potential competitors.

Step 4: Identifying Similar Products

This is where the magic happens. Once again we use our LLMs to analyze the products retrieved from the Amazon SERP. The models compare these products against your listings, looking at various factors such as product descriptions, features, and specifications to identify which ones are most similar to yours.