How to Scrape Amazon Without Code (2025)

October 6, 2025•9 min read

If you want product data from Amazon without writing code, this 2025 guide shows you exactly how to capture titles, prices, ratings, reviews, Prime badges, sellers, and links — then export to CSV, Excel, JSON, or Google Sheets in minutes.

Webtable is the best no‑code option for fast, accurate scraping right in your browser, and it's generous for most common jobs. It detects product lists and tables automatically, cleans the data for you, and exports with one click. You stay in control the whole time.

Who this guide is for

This walkthrough is designed for marketers, researchers, ecommerce founders, analysts, and operators who need a reliable way to collect Amazon product data without writing scripts or managing servers. If you’re already familiar with browser extensions and spreadsheets, you can jump straight to the step‑by‑step setup below.

What you’ll learn

Capture key Amazon product fields (title, price, rating, review count, Prime availability, seller/brand, thumbnail URL, product link).
Handle infinite scroll and pagination patterns reliably.
Clean messy fields automatically so data is spreadsheet‑ready.
Export to CSV/Excel/JSON or push directly to Google Sheets.
Troubleshoot common issues like lazy‑loaded prices, sponsored items, and missing rows.
Operate responsibly with a quick review of legal and ethical considerations.

Before you start

Use the latest Chrome or a Chromium‑based browser (Edge, Brave).
Install the Webtable Chrome extension.
Sign into Google if you plan to export directly to Sheets.
Prepare a sample search on Amazon (for example, ‘wireless earbuds’).

Quick start: Scrape Amazon search results (no code)

The fastest way to get clean Amazon results into a spreadsheet is to start on a search results page and let Webtable detect the product list. Here’s the high‑level flow you’ll follow:

Navigate to an Amazon results page (e.g., https://www.amazon.com/s?k=wireless+earbuds).
Open Webtable and choose the detected product list.
Confirm the columns you need and let Webtable clean the data.
Scroll (or enable auto‑scroll) to reveal more items.
Export to CSV/Excel/JSON or push to Google Sheets.

Why this method works well in 2025

Amazon uses modern, dynamic rendering and lazy loading. Visual selection tools in Webtable adapt to these patterns better than hardcoded selectors or brittle scripts. You click once and Webtable generalizes the pattern across similar items, even if the DOM structure shifts slightly during scroll.

Step 1: Open a relevant Amazon results page

Start with a broad search so your first dataset is representative. For example: ‘wireless earbuds,’ ‘kids water bottle,’ or ‘USB‑C hub.’ The broader the query, the more variation you’ll see in titles, pricing, badges, and sellers — which is helpful for testing extraction quality.

Avoid starting with extremely niche queries where results can be sparse.
If you already have a list of specific ASINs, you can begin on a category page or brand storefront instead.

Step 2: Launch Webtable and select the product list

Click the Webtable icon. Webtable scans the page and usually detects one or more candidate lists. Choose the product grid/list that shows item cards with titles, prices, ratings, and images.

If multiple candidates appear, pick the one with the most consistent product rows.
If nothing relevant shows up, switch to Manual Selection and click one product title — Webtable will find similar items and assemble a table.

Step 3: Review and refine columns

Once Webtable builds the table, review the columns in the side panel. Typical columns include:

Title
Price
Rating
Review Count
Prime (boolean or badge)
Seller/Brand
Product URL
Image URL (thumbnail)
Delivery/Shipping snippet (when present)

Use column toggles to hide noise (for example, ad labels, breadcrumbs, or unrelated badges). You can rename headers directly so downstream analysis in Sheets or Excel is easier.

Step 4: Reveal more results (scroll and pagination)

Results often load as you scroll. Turn on auto‑scroll in Webtable to pull more items without clicking. If your results use numbered pagination, navigate to the next page and repeat the selection — Webtable’s selection stays consistent as long as the structure is similar.

Tips for reliable capture:

Scroll slowly if prices or ratings appear a split second late.
Pause briefly when new rows load to let Webtable stabilize the table.
For very long lists, export in batches to keep the browser responsive.

Step 5: Clean the data automatically

Webtable applies automatic cleaning that removes sponsored blocks, merges fragmented text, and drops duplicate or uniform columns. This saves hours of post‑processing. If you want to be strict about what appears, use row filters:

Drop rows missing prices or titles.
Drop rows that include the word ‘Sponsored.’
Keep rows that include ‘Prime’ or ‘Free returns.’

You can also merge columns (for example, combine whole and fractional price parts) and standardize headers to match your analytics templates.

Step 6: Export to CSV, Excel, JSON, or Google Sheets

When you’re satisfied with the preview, export in one click. For spreadsheets, CSV and Excel work well. If your workflow is Google‑first, click ‘Export to Google Sheets’ to create or update a sheet instantly.

CSV is best for quick imports into BI tools.
Excel retains familiar formatting for desktop workflows.
JSON is helpful for developer integrations or custom scripts.
Google Sheets is perfect for collaboration and quick charts.

What data can you capture from Amazon pages?

On search and category pages, you can typically capture:

Product title
Price (current) and sometimes list price
Rating (average) and total reviews
Prime badge and shipping badge
Seller/Brand
Thumbnail image URL
Product detail page URL
Badges like ‘Best Seller’ or ‘Amazon’s Choice’ when present

Note that certain fields, like exact seller names or variant‑specific prices, can be inconsistent on list pages. If you need variant attributes (size/color), product detail pages are better targets. Webtable’s Manual Selection makes this feasible for small to medium batches.

Advanced: Scrape product detail pages (PDPs)

When you need richer attributes — technical specs, bullet points, A+ content snippets, or size/color availability — open a product detail page and build a custom selection:

Start Manual Selection in Webtable.
Click the product title; Webtable highlights a candidate column.
Add more fields by clicking bullet points, the price block, rating summary, and the ‘About this item’ list.
For image URLs, click the main image area; Webtable extracts the source link.
Repeat on a few PDPs to ensure consistency; export in batches.

PDP scraping is powerful for smaller samples or curated catalogs. For thousands of items, begin with list pages to shortlist candidates, then enrich top picks on PDPs.

De‑duplicating and normalizing your dataset

Once you have a few hundred rows, normalize the output so it’s analysis‑ready:

Standardize price fields to a numeric column (strip currency signs and commas).
Parse rating into a decimal (for example, 4.4).
Convert review counts to integers.
Normalize Prime/Shipping to booleans or categorical fields.
Extract ASINs from product URLs when needed for matching.

These conversions are easy to do in Google Sheets or Excel. If you export JSON, you can apply the same transformations in your data pipeline.

Common issues and quick fixes

Prices missing or showing as blank

Scroll a touch slower; give lazy‑loaded price blocks time to render.
Move your cursor briefly over a product card to trigger price visibility in some layouts.
Re‑run Manual Selection focusing on the price element.

Too many sponsored items

Use Webtable’s data cleaning to drop rows that contain ‘Sponsored.’
Add a filter to exclude rows with ad labels.

Ratings or review counts look inconsistent

Ensure you selected the aggregate rating and not per‑variant data.
Normalize review counts by removing commas before parsing as numbers.

Columns drift after scrolling

Toggle the selection coverage setting higher so Webtable remains strict about element signatures.
Export in smaller batches (e.g., 200–400 rows at a time).

The table includes unrelated elements

Deselect noisy columns (breadcrumbs, promos) and re‑apply filters.
Start selection from a more representative element (for example, the product title link).

Tips to get better results on large lists

Plan your must‑have columns first (title, price, rating, link). Add nice‑to‑haves later.
Use auto‑scroll plus brief pauses to keep capture stable.
Export every few hundred rows; append results in your spreadsheet.
Keep consistent header names across batches to simplify merges.
Validate 10–20 random rows before committing to analysis.

Example workflows

Price benchmarking for competitor sets

Build a list of 50–200 overlapping products.
Extract title, current price, rating, reviews, and Prime badge.
Export to Sheets and chart price bands by brand.

Market scanning for new category entries

Run a broad query (e.g., ‘USB‑C hub’).
Extract title, brand, badges, and links.
Sort by reviews and rating; shortlist top contenders for deeper PDP capture.

Listing optimization research

Pull titles and bullets from PDPs for your top 30 competitors.
Analyze term frequency and title structures.
Identify gaps (features, materials, sizes) for your product copy.

Responsible scraping: legal and ethical notes

Always read a site’s terms before collecting data. Scrape only publicly available information and keep request rates reasonable. Respect robots.txt guidance and local laws in your jurisdiction. If you’re unsure, consult counsel before using the data commercially.

Why use Webtable instead of scripts in 2025?

Scripting Amazon reliably requires rotating proxies, robust selectors, and frequent maintenance. Webtable runs locally in your browser, adapts to dynamic content, and produces clean tables fast. For most teams, that means less time wrestling with breakage and more time analyzing data.

No code to write or maintain.
Works directly on the pages you see.
Exports instantly to Sheets, CSV, Excel, or JSON.
Automatic data cleaning saves hours each week.

Troubleshooting checklist

Confirm you’re on the expected results layout (grid vs list).
Try a fresh selection starting from the product title link.
Increase selection strictness to reduce column drift.
Export in smaller chunks and append in Sheets for very long runs.
If a specific column keeps failing (for example, price), capture it on PDPs for your top items only.

Frequently asked questions

Can I export directly to Google Sheets?

Yes. Click ‘Export to Google Sheets’ in Webtable; it will create a new sheet or update an existing one.

How many rows can I capture in the browser?

Most users comfortably export thousands of rows across a few batches. Extremely large jobs are better split or scheduled with complementary tools.

Does Webtable handle infinite scroll?

Yes. Enable auto‑scroll to reveal additional products while your table updates in real time.

What if prices render late?

Scroll more slowly or hover briefly on a product card. If needed, refine your selection focusing on the price element.

Can I capture images and links?

Yes. Webtable extracts image URLs and product links automatically when included in your selection.

Will my data be private?

Yes. Webtable processes data locally in your browser. Your extracted data is not stored on external servers.

Related guides

For deeper skills and adjacent workflows, explore these guides:

Conclusion

You don’t need scripts, proxies, or complex crawlers to collect Amazon data anymore. With Webtable, you click once, watch a clean table appear, and export to your favorite format. Start with a representative search, refine your columns, capture a few hundred rows, and push to Sheets — you’ll have answers in less time than it takes to spec a custom scraper.

Ready to try it? Install the Webtable Chrome extension, open an Amazon search, and build your first dataset today. Explore Features and browse Tutorials when you want to go deeper.