In Amazon’s marketplace of over 350 million SKUs, tens of thousands of sellers search daily for the next winning product. Yet the harsh reality is that most sellers end up trapped in red ocean competition—their chosen products are either dominated by major sellers, profit margins compressed by price wars, or simply lack sufficient market demand. The root cause of this predicament lies in a fundamental truth: traditional product research methods only reveal the tip of the iceberg, while genuine blue ocean opportunities often hide in places you cannot see.

A seller with three years of Amazon experience once shared his journey: for the first two years, he constantly searched for products in Best Seller lists and popular keywords, only to find fierce competition every time he launched. It wasn’t until he adopted systematic data mining methods—traversing the entire category tree and conducting multi-dimensional analysis—that he discovered a golden opportunity in an inconspicuous subcategory: monthly search volume of 5,000+, average first-page reviews under 200, and gross margin exceeding 50%. This product generated over $80,000 in revenue within three months of launch, with competitors remaining scarce to this day.

This case clearly illustrates a fundamental fact: in Amazon product research, the scope of data you can access directly determines the quality of opportunities you can discover. If you rely solely on Best Seller lists, popular keyword tools, or competitor analysis, you’ll forever be limited to sharing scraps in areas others have already discovered. True blue ocean products—those with moderate search volume, low competition, large profit margins, and clear growth trends—often hide deep in an ocean of millions of products, discoverable only through systematic full-scale data collection and intelligent analysis.

The core value of Amazon blue ocean product finder data lies precisely here: it doesn’t make you search for gaps in known red oceans, but helps you discover blue ocean territories that most sellers haven’t yet noticed. Through category traversal technology, you can systematically scan every corner of Amazon; through big data analysis, you can precisely identify high-potential opportunities matching blue ocean characteristics from massive product datasets. This data-driven product research approach is becoming standard equipment for professional and successful sellers.

What Are True Blue Ocean Products? 5 Golden Metrics Explained

Before diving into how to mine blue ocean products, we first need to clarify: what qualifies as a true “blue ocean”? Many sellers have misconceptions about blue oceans, thinking that low competition alone defines one, or that high profit alone makes it worthwhile. In reality, genuine blue ocean products must simultaneously meet multiple golden standards across different dimensions—none can be missing.

Golden Metric 1: Moderate Search Volume (Monthly 1,000-10,000)

Search volume is the most direct indicator of market demand validation. Many novice sellers tend toward two extremes: either choosing super keywords with hundreds of thousands of monthly searches, only to find competition too fierce to establish a foothold; or selecting niche keywords with only dozens of monthly searches, discovering after launch that nobody buys. True blue ocean products should have core keyword monthly search volumes between 1,000 and 10,000—this range means the market has sufficient demand to support your sales, yet won’t attract too many major sellers’ attention.

Note that this search volume refers to total searches for core keywords, not single keywords. A blue ocean product typically has 3-5 related keywords whose combined search volumes should fall within this range. For example, a portable coffee grinder might have “portable coffee grinder” with 3,000 monthly searches, “manual coffee grinder travel” with 2,000, and “camping coffee grinder” with 1,500, totaling 6,500—an ideal search volume range.

Golden Metric 2: Low Competition (First Page Average Reviews <500)< /h3>Competition assessment shouldn’t focus solely on competitor quantity but also competitor strength. An effective measurement standard: after searching core keywords, check the average review count of first-page products (top 20 organic search results). If average reviews exceed 500, the market already has mature sellers operating, making it difficult for newcomers to gain sufficient exposure; if average reviews fall between 200-500, it’s moderate competition requiring operational capability and resource investment; if average reviews are below 200, especially under 100, this typically signals a blue ocean.Beyond review counts, also monitor other first-page product competition indicators: star rating distribution (if most products rate 4.5+ stars, quality competition is fierce), price range (large price variations indicate market immaturity), brand concentration (if 2-3 brands dominate the first page, the market is monopolized). An ideal blue ocean product should show inconsistent first-page product quality, no obvious dominant brands, and dispersed price ranges.

Golden Metric 3: Large Profit Margin (Gross Margin >40%)

One core value of blue ocean products is delivering healthy profit margins. On Amazon, after deducting product costs, FBA fees, advertising expenses, platform commissions, and other costs, if gross margin can reach 40% or above, it’s a worthwhile product investment. This profit margin not only supports daily operations but also provides sufficient budget for advertising, promotions, and product optimization.

When calculating profit margins, pay special attention to hidden costs. Many sellers only calculate product costs and FBA fees, overlooking return rates, storage fees, long-term storage fees, and advertising costs. A complete profit calculation formula should be: Gross Margin = (Selling Price – Product Cost – FBA Fees – Platform Commission – Advertising Cost – Return Losses – Other Expenses) / Selling Price. Only products maintaining 40%+ gross margin under this complete calculation represent truly valuable blue ocean opportunities.

Golden Metric 4: Clear Growth Trend (YoY Growth >30%)

Static blue oceans may only be temporary opportunities; truly valuable blue oceans should be growing markets. By analyzing product category historical search trends, sales changes, new product launch speeds, and other data, you can determine whether this market is growing, stable, or declining. An ideal blue ocean product should show clear growth trends in its subcategory market, preferably with year-over-year growth exceeding 30%.

Growth trend assessment requires combining multiple dimensions: search volume trends (Google Trends, Amazon search trends), new product launch speed (if monthly new product additions are increasing, the market is heating up), top product sales changes (if Best Seller sales are growing, the entire market is expanding), related category performance (if related categories are all growing, it’s a major trend). Only when multiple dimensions all show growth signals can you confirm this is a truly potential blue ocean market.

Golden Metric 5: Controllable Entry Barriers

The final easily overlooked but critically important metric: entry barriers. Some seemingly blue ocean products actually have high entry barriers, such as requiring special certifications (FDA, CE, etc.), patent protection, large MOQ requirements, complex supply chains, or strong seasonality. These barriers significantly increase startup costs and operational difficulty.

An ideal blue ocean product should have relatively controllable entry barriers: no complex certifications needed, no patent disputes, mature supply chain, reasonable MOQ (typically 500-1,000 units minimum order), not season-restricted or with predictable seasonality. Of course, moderate barriers are beneficial as they block some competitors; the key is finding balance between “accessible” and “protected.”

Blue Ocean vs Red Ocean vs Dead Sea: Data Comparison

To more intuitively understand blue ocean product characteristics, let’s examine the differences between blue ocean, red ocean, and dead sea products through a comparison table:

This comparison clearly shows: red ocean products, despite high search volumes, face fierce competition and thin profits; dead sea products, despite low competition, lack market demand entirely; only blue ocean products achieve optimal balance among demand, competition, and profit.

Why Traditional Methods Can’t Find Blue Oceans: Five Fatal Blind Spots

Understanding blue ocean product standards leads to the next question: why do most sellers struggle to find true blue oceans using traditional methods? The answer lies in systematic blind spots inherent to traditional product research methods, causing you to see only a small portion of the market while genuine opportunities hide in places you cannot access.

Blind Spot 1: Manual Category Browsing Inefficiency and Partiality

The most primitive research method involves manually browsing Amazon categories on the frontend, looking for promising products. This approach’s problem: Amazon has over 30 top-level categories, hundreds of second-level categories, thousands of third-level categories—browsing everything, even spending just 5 minutes per category, requires hundreds of hours. Moreover, manual browsing easily suffers from subjective bias, as you might unconsciously focus only on familiar or interesting categories, missing numerous potential opportunities.

More importantly, manual browsing only reveals surface-level products in each category, typically Best Sellers, New Releases, or first few pages. True blue ocean products often don’t occupy these prominent positions—they might be on page 5, page 10 of search results, or in an inconspicuous third or fourth-level subcategory. Without systematic traversal methods, you’ll never reach these hidden treasures.

Blind Spot 2: Best Seller List Lag and Misleading Nature

Many sellers like finding inspiration from Best Seller lists, believing listed products represent market demand. This logic itself isn’t wrong, but the problem: when a product reaches the Best Seller list, it’s typically no longer a blue ocean. Listed products usually have established competitive landscapes, making it difficult for newcomers to share the pie.

Moreover, Best Seller lists exhibit obvious Matthew effects: top-ranked products gain more exposure, generating more sales, further consolidating their rankings. This means listed products grow stronger while new products face increasing difficulty challenging them. If you only focus on lists for product research, you’ll forever be limited to “me too” products, unable to find genuine blue ocean opportunities.

Blind Spot 3: Keyword Tool Coverage Gaps

Many keyword research tools exist in the market, helping you discover popular keywords, analyze search volumes, and assess competition. But these tools share a common limitation: they can only analyze keywords you input, unable to help you discover keywords you don’t even know exist. In other words, if you’re unaware of a certain niche’s existence, you’ll never search related keywords, thus never discovering that niche’s opportunities through keyword tools.

For example: suppose a rapidly growing niche called “collapsible pet travel bowls” exists. If you’ve never heard of this product, you won’t search “collapsible pet travel bowl” keywords, thus unable to discover this opportunity through keyword tools. Category traversal methods differ—they systematically scan all products, including niches you’ve never heard of, helping you discover true blue oceans.

Blind Spot 4: Competitor Analysis Partiality

Another common research method involves analyzing competitors: seeing what successful sellers sell, then doing similar products. This method’s problem: you can only see already successful products, not opportunities not yet fully developed. Moreover, when you discover a competitor worth imitating, dozens or even hundreds of sellers have likely also discovered it, quickly turning the market red ocean.

Competitor analysis has another fatal flaw: it only lets you make “better me too” products, not become a “first mover.” On Amazon, first mover advantage is critically important—the first seller entering a blue ocean market can quickly accumulate reviews, establish brand awareness, and occupy favorable ranking positions. If you always follow others, you’ll forever share leftover scraps.

Blind Spot 5: Data Silos Leading to Decision Errors

Even if you collect some product data through various methods, this data is often fragmented and incomplete. You might know a product’s search volume but not its competition level; you might know a category’s Best Seller but not the entire category’s growth trend; you might know a product’s price but not its true profit margin. These data silos lead to partial judgments, selecting seemingly good products with fatal flaws.

Truly effective product research requires integrating multi-dimensional data: search volume, competition, price, profit, trends, seasonality, supply chain, certification requirements, etc. Only when you can simultaneously see all these dimensions and conduct comprehensive analysis can you make accurate judgments. Traditional research methods struggle with this because manually collecting and integrating so many data dimensions requires enormous effort.

Big Data-Driven Blue Ocean Mining: The Power of Category Traversal

Since traditional research methods have so many blind spots, how can we systematically discover blue ocean products? The answer: through category traversal and big data analysis, build comprehensive, multi-dimensional Amazon blue ocean product finder data. This method’s core philosophy: leave no corner unchecked, let data speak, and let algorithms help you find hidden treasures.

What Is Category Traversal? Why Is It So Important?

Category Traversal refers to systematically visiting Amazon’s entire category tree structure, from top-level categories to second, third, fourth, and even deeper subcategories, collecting all product data under each category. This process is like using a comb to thoroughly comb through the entire Amazon marketplace, ensuring no potential opportunity is missed.

Amazon’s category structure is a massive tree system. Taking the US marketplace as an example, there are 30+ top-level categories (such as Electronics, Home & Kitchen, Sports & Outdoors, etc.), each with dozens of second-level categories, each second-level with dozens of third-level categories… The entire category tree contains thousands of nodes, covering hundreds of millions of products. Without systematic traversal methods, manual browsing of this enormous system is simply impossible.

Category traversal’s value lies in its comprehensiveness and systematization. Through traversal, you can: discover subcategories not on popular lists; find products with moderate search volumes but low competition; identify rapidly growing emerging markets not yet noticed by major sellers; compare opportunity quality across different categories, selecting optimal entry points.

Full-Scale Data Collection: Building Complete Market Maps

Category traversal is just the first step; more importantly, collecting comprehensive product data during traversal. Complete Amazon blue ocean product finder data should include these dimensions:

Product Basic Data: ASIN, title, brand, price, main image, category path, launch date, etc. These are fundamental information helping you understand basic product situations.

Sales Estimation Data: Although Amazon doesn’t publicly disclose sales data, you can estimate sales through Best Seller Rank (BSR), review growth speed, inventory changes, and other indicators. Sales data is the key metric validating market demand.

Competition Data: Review counts, star rating distribution, first-page competitor numbers, advertising competition, brand concentration, etc. This data helps you assess market entry difficulty.

Trend Data: Search volume trends, sales trends, new product launch speeds, price change trends, etc. Trend data helps you determine whether this market is growing, stable, or declining.

Profit-Related Data: Product dimensions and weight (for calculating FBA fees), variant counts, promotion situations, return rates, etc. This data helps you accurately calculate profit margins.

Only when you possess this full-scale, multi-dimensional data can you conduct truly effective blue ocean product screening and analysis.

Pangolinfo Category Traversal: Professional-Grade Data Collection Capabilities

Pangolin Scrape API provides powerful category traversal capabilities specifically designed for Amazon product research scenarios. Its core advantages include:

Complete Category Tree Coverage: Supports traversing complete category trees across all Amazon marketplaces (US, UK, Germany, Japan, etc.), from top-level categories to deepest subcategories, ensuring no opportunity is missed.

Multi-Dimensional Data Collection: Collects not only product basic information but also BSR rankings, review data, price history, variant information, advertising data, etc., providing comprehensive product profiles.

Real-Time Data Updates: Supports scheduled data updates, tracking product dynamic changes, identifying emerging opportunities and decline signals.

Structured Data Output: Returns standardized JSON format data, facilitating subsequent storage, analysis, and visualization without complex data cleaning work.

Through Pangolin’s category traversal capabilities, you can obtain complete data on millions of products in a short time, establishing your own Amazon market database, laying a solid foundation for subsequent blue ocean mining.

Big Data Analysis Models: Mining Gold from Massive Data

Possessing full-scale data is just the beginning; how to quickly find truly blue ocean opportunities from millions of products requires establishing scientific analysis models. An effective blue ocean product screening model typically includes these core algorithms:

Opportunity Scoring Algorithm: Based on the previously mentioned 5 golden metrics (search volume, competition, profit margin, growth trend, entry barriers), calculate a comprehensive opportunity score for each product. Higher scores indicate products more closely matching blue ocean characteristics. The specific scoring formula can be: Opportunity Score = Search Volume Score × 0.25 + Competition Score × 0.30 + Profit Margin Score × 0.25 + Growth Trend Score × 0.15 + Barrier Score × 0.05.

Competition Calculation: Comprehensively considering first-page average reviews, top product reviews, brand concentration, advertising competition, and other dimensions, calculate a 0-100 competition score. Lower scores indicate less competition.

Trend Prediction Model: Based on historical search volumes, sales, new product launch speeds, and other data, use time series analysis methods to predict market trends for the next 3-6 months, helping you proactively position in growth markets.

Through customized product research multidimensional table, you can visualize these analysis model results, quickly screening out the most potential blue ocean products.

Blue Ocean Product Mining: 7-Step Practical Workflow

With theory and tools in place, next comes practice. Below is a proven, replicable blue ocean product mining workflow, from data collection to final validation, with clear operational methods and judgment criteria for each step.

Step 1: Determine Target Category Range

While theoretically you can traverse all categories, in practice, it’s recommended to first determine a general range to improve efficiency. When selecting target categories, consider: fields you’re familiar with (having supply chain resources or industry knowledge), categories with higher profit margins (such as Home & Kitchen, Sports & Outdoors), rapidly growing categories (learnable through industry reports).

Suppose you select “Home & Kitchen” as your target top-level category. This category contains dozens of second-level categories like Kitchen & Dining, Home Décor, Bedding, etc., with each second-level containing more granular third and fourth-level categories. Your goal is to systematically traverse every node of this category tree.

Step 2: Set Filtering Criteria

Before starting data collection, set filtering criteria to filter out products obviously not meeting requirements, improving subsequent analysis efficiency. Common filtering criteria include:

• Price range: $15-$50 (too cheap means low profit, too expensive means hard to promote)
• Review count range: 10-500 (too few means unvalidated, too many means high competition)
• Star rating requirement: >3.5 (don’t consider poor quality products)
• Launch time: within past 2 years (too old products may be outdated)
• BSR ranking: within top 50% of category (don’t consider products with no sales)

Step 3: Batch Data Collection

Use category traversal API for batch data collection. This process may take several hours to days, depending on your category range and product quantity. After completion, you’ll have a dataset containing tens of thousands or even hundreds of thousands of products.

During data collection, note: set reasonable request frequencies to avoid triggering rate limits; use multi-threading or distributed collection to improve efficiency; save data in real-time to avoid data loss from mid-process failures; record collection logs for subsequent tracking and debugging.

Step 4: Data Cleaning and Standardization

Raw data often has missing values, errors, inconsistent formats, requiring cleaning and standardization. Common cleaning tasks include: removing duplicate products (same product may appear in multiple categories); filling missing data (if certain fields are missing, infer from other data); standardizing formats (unify prices to USD, dates to ISO format); filtering anomalous data (obviously erroneous data like price = 0, negative review counts).

Step 5: Multi-Dimensional Scoring and Ranking

Using the previously mentioned opportunity scoring algorithm, calculate comprehensive scores for each product, then rank from highest to lowest. Top-ranked products are the most likely blue ocean opportunities. Additionally, you can rank and filter by different dimensions, such as: ranking by competition to find products with least competition; ranking by growth trend to find fastest-growing products; ranking by profit margin to find highest-profit products.

Through AMZ Data Tracker product research tool, you can view these ranking results visually, quickly locating the most potential opportunities.

Step 6: Manual Verification and Deep Analysis

Algorithm-screened results are only candidate lists; final validation requires manual review. For top-ranked products, conduct deep analysis: view product detail pages to understand product features and selling points; read customer reviews to understand product strengths, weaknesses, and improvement opportunities; analyze competitors to understand competitive landscape and differentiation opportunities; assess supply chain to confirm reliable supplier availability; calculate detailed profits to ensure they meet expectations.

This stage typically screens hundreds of candidate products down to 10-20 products worth further validation.

Step 7: Small-Batch Testing and Validation

The final step is actual testing. Select 2-3 most confident products for small-batch procurement and launch testing. Testing aims to verify whether your analysis is accurate and market response meets expectations. During testing, recommend: small-batch ordering (200-500 units) to control risk; quick launch to seize first-mover advantage; moderate advertising to test conversion rates; closely monitor data and adjust strategies promptly.

If test results are good (conversion rate >2%, ACoS <30%, positive reviews), increase investment; if results are poor, cut losses promptly and return to step six to select other products.

Real Case: Finding 3 Blue Oceans from 100,000 Products

Let me share a real case. A seller used category traversal methods to collect data on approximately 100,000 products under the “Sports & Outdoors” category. After filtering and scoring, he discovered 3 high-scoring products:

Product A: Portable camping shower bag. Monthly search volume ~3,000, first-page average reviews 150, price $25-35, estimated gross margin 45%, YoY growth 40%. He launched at $28, sold 120 units first month, stabilized at 300 units/month by third month, monthly profit ~$4,000.

Product B: Foldable yoga mat storage bag. Monthly search volume ~2,000, first-page average reviews 80, price $18-25, estimated gross margin 50%, YoY growth 55%. He launched at $22, through listing optimization and moderate advertising, reached 200 units/month by second month, monthly profit ~$2,200.

Product C: Outdoor waterproof phone armband. Monthly search volume ~5,000, first-page average reviews 200, price $15-22, estimated gross margin 42%, YoY growth 35%. This product had slightly more competition but larger market capacity. He launched at $18, with higher advertising budget, reached 400 units/month by third month, monthly profit ~$3,000.

Three products combined for monthly profit of ~$9,200, with total investment (product cost + advertising) under $15,000, delivering excellent ROI. More importantly, these three products were all discovered through data mining, with low competition before his entry, giving him ample growth space.

Technical Implementation: API Calls and Data Processing

For technically capable teams, you can directly build your own blue ocean product mining system through APIs. Below is a simplified technical implementation example:

Category Traversal API Calls

import requests
import json

# API Configuration
API_KEY = "your_api_key"
API_ENDPOINT = "https://api.pangolinfo.com/category-tree"

def get_category_tree(marketplace="US"):
    """Get complete category tree"""
    headers = {"Authorization": f"Bearer {API_KEY}"}
    params = {"marketplace": marketplace}
    
    response = requests.get(API_ENDPOINT, headers=headers, params=params)
    return response.json()

def traverse_category(category_id, depth=0):
    """Recursively traverse category tree"""
    print(f"{'  ' * depth}Processing category: {category_id}")
    
    # Get products under this category
    products = get_category_products(category_id)
    
    # Process product data
    for product in products:
        analyze_product(product)
    
    # Get subcategories
    subcategories = get_subcategories(category_id)
    
    # Recursively traverse subcategories
    for subcat in subcategories:
        traverse_category(subcat['id'], depth + 1)

# Start traversal
category_tree = get_category_tree()
for top_category in category_tree:
    traverse_category(top_category['id'])
                

Product Data Analysis

def calculate_opportunity_score(product):
    """Calculate opportunity score"""
    # Search volume score (1000-10000 is optimal)
    search_volume = product.get('search_volume', 0)
    if 1000 <= search_volume <= 10000:
        volume_score = 100
    elif search_volume < 1000:
        volume_score = search_volume / 10
    else:
        volume_score = max(0, 100 - (search_volume - 10000) / 1000)
    
    # Competition score (fewer reviews is better)
    avg_reviews = product.get('avg_competitor_reviews', 1000)
    competition_score = max(0, 100 - avg_reviews / 5)
    
    # Profit margin score
    profit_margin = product.get('profit_margin', 0)
    profit_score = min(100, profit_margin * 2.5)
    
    # Growth trend score
    growth_rate = product.get('growth_rate', 0)
    growth_score = min(100, growth_rate * 2)
    
    # Comprehensive score
    total_score = (
        volume_score * 0.25 +
        competition_score * 0.30 +
        profit_score * 0.25 +
        growth_score * 0.15 +
        50 * 0.05  # Barrier score (simplified to fixed value)
    )
    
    return total_score

def analyze_product(product):
    """Analyze single product"""
    score = calculate_opportunity_score(product)
    
    if score > 70:  # High-scoring products
        print(f"🌟 Found blue ocean: {product['title']}")
        print(f"   Score: {score:.2f}")
        print(f"   ASIN: {product['asin']}")
        print(f"   Price: ${product['price']}")
        print(f"   Reviews: {product['reviews']}")
        
        # Save to database
        save_to_database(product, score)
                

Data Storage and Visualization

import sqlite3
import pandas as pd
import matplotlib.pyplot as plt

def save_to_database(product, score):
    """Save to database"""
    conn = sqlite3.connect('blue_ocean.db')
    cursor = conn.cursor()
    
    cursor.execute('''
        INSERT INTO products (asin, title, price, reviews, score, created_at)
        VALUES (?, ?, ?, ?, ?, datetime('now'))
    ''', (
        product['asin'],
        product['title'],
        product['price'],
        product['reviews'],
        score
    ))
    
    conn.commit()
    conn.close()

def generate_report():
    """Generate analysis report"""
    conn = sqlite3.connect('blue_ocean.db')
    df = pd.read_sql_query("SELECT * FROM products WHERE score > 70 ORDER BY score DESC", conn)
    
    # Generate score distribution chart
    plt.figure(figsize=(10, 6))
    plt.hist(df['score'], bins=20, edgecolor='black')
    plt.title('Blue Ocean Opportunity Score Distribution')
    plt.xlabel('Score')
    plt.ylabel('Count')
    plt.savefig('score_distribution.png')
    
    # Export Top 100 products
    df.head(100).to_csv('top_100_blue_ocean_products.csv', index=False)
    
    conn.close()
                

Conclusion: Data-Driven Blue Ocean Research Is Key to Success

In Amazon’s fiercely competitive marketplace, finding true blue ocean products is every seller’s dream. However, traditional research methods—manual browsing, following lists, analyzing competitors—all have systematic blind spots, letting you see only a small portion of the market while missing numerous hidden opportunities.

The core value of Amazon blue ocean product finder data lies in: through category traversal technology, systematically scanning the entire Amazon marketplace, leaving no corner unchecked; through big data analysis, precisely identifying high-potential opportunities from massive products—those with moderate search volumes, low competition, large profit margins, and clear growth trends; through scientific scoring models, transforming subjective research decisions into objective data analysis, greatly improving success rates.

This article detailed blue ocean products’ 5 golden metrics, traditional methods’ 5 blind spots, big data-driven research methods, 7-step practical workflow, and technical implementation solutions. This methodology has been validated by numerous successful sellers, helping you quickly find truly valuable blue ocean opportunities from millions of products.

Start Your Blue Ocean Mining Journey Now

If you’re still using traditional research methods, still struggling in red ocean markets, now is the best time to change. Through Pangolin Scrape API‘s category traversal capabilities, you can quickly build your own Amazon market database, obtaining comprehensive, real-time, multi-dimensional product data, providing solid data support for your research decisions.

For technical teams, you can directly build customized research systems through APIs, fully controlling data collection and analysis processes; for non-technical sellers, you can use ready-made product research tools, enjoying big data research advantages without programming.

Remember: on Amazon, information gaps equal profit gaps. Sellers who can discover blue ocean opportunities earlier, enter markets faster, and judge trends more accurately often achieve returns far exceeding peers. Don’t let data blind spots become bottlenecks for your business growth—start building your Amazon blue ocean product finder data now and seize the competitive advantage!

Ready to discover your blue ocean products?

Visit Pangolinfo Scrape API to learn about category traversal features, or register for a free trial to start your data-driven research journey.

Related Resources

• Category Traversal API Documentation
• AMZ Data Tracker Product Research Tool
• Customized Product Research Multidimensional Table