#AmazonReviewScraping

[simpatel]

Amazon USA | How Review Scraping Boosted Tech Brand CX

Amazon USA: How Review Scraping Improved Customer Experience for a Tech Brand

Overview

In the competitive tech ecosystem on Amazon USA, customer experience is everything. With over 9.5 million U.S. sellers and thousands of tech products launched every week, standing out requires more than just great specs—it demands continuous improvement powered by real customer feedback.

This case study explores how Datazivot helped a rising consumer electronics brand extract, analyze, and act on Amazon USA reviews to improve product performance, reduce returns, and drive a 27% boost in customer satisfaction.

Client Profile

Brand Name: (Undisclosed for confidentiality)

Category: Consumer Electronics (Headphones, Smart Gadgets, Power Banks)

Primary Market: United States (Amazon.com)

Monthly Review Volume: 15,000+

Engagement with Datazivot: Amazon Review Scraping + Sentiment Analytics

Challenge

The tech brand was facing:

High return rates on newly launched Bluetooth headphones

Customer complaints buried in Amazon reviews not visible through seller central tools

A dip in product ratings from 4.4 to 3.7 stars within 60 days

Inconsistent feedback on battery life, packaging, and fit

They needed a way to listen to their customers at scale, spot common pain points, and make fast improvements to avoid long-term rating damage and revenue loss.

Solution Provided by Datazivot

Sample Scraped Review Data

Findings from Sentiment & Complaint Analysis

Datazivot uncovered 4 major product gaps:

1. Battery Performance Mismatch: 28% of negative reviews mentioned shorter-than-promised battery pfe. Power rating claims exceeded real-world performance.

2. Packaging & Depvery Damage: 1 in 7 complaints cited physical damage due to poor box material or shipping padding.

3. Fit & Ergonomics: Multiple users noted discomfort during workouts or long use. "Spps off" was a recurring keyword.

4. Unclear Setup Instructions: Confusing multi-language guide; several 1 star reviews stated “Can’t connect.”

Actions Taken by the Tech Brand

(Guided by Datazivot Insights)

Product Page Optimization

Updated battery specs to reflect real-world usage

Added a “Fit & Use Case” visual chart to set better buyer expectations

Uploaded unboxing video + clear setup instructions

Product Improvement

Enhanced ear grip design for the next product batch

Reinforced packaging with extra padding for delivery resilience

Improved lithium cell quality to match stated performance

Customer Support Alignment

Created auto-responses for common complaints

Shared personalized setup guides to reduce post-purchase confusion

Prioritized issue-specific resolution for reviews flagged as return risks

Results After 60 Days of Implementation

Impact on Customer Experience (CX)

Higher product trust reflected in customer Q&A and upvotes

Reduced buyer confusion and pre-purchase hesitation

Better engagement on Amazon Brand Store and A+ content

More “Verified Buyer” reviews praised new improvements

Why Review Scraping Works So Well for Tech Products?

Tech buyers are detail-focused and expressive in feedback

Performance metrics (battery, Bluetooth, durability) are often compared with brand claims

Unfiltered reviews often surface real complaints that support teams don’t hear directly

AI-scraped data gives companies a preemptive advantage—fix issues before they tank your ratings

Why the Brand Chose Datazivot?

Client Testimonial

“We thought we knew our customers through support tickets—but Datazivot showed us what they really think. Our product evolution is now based on what matters most to real buyers.”

— CX Director, Consumer Tech Brand (USA)

Conclusion

The Review Revolution is Here :

Amazon reviews are no longer just a rating system—they're a real-time product feedback engine. Brands that listen and act on these signals improve faster, return less, and build loyal fans.

With Datazivot, review scraping isn’t just data collection—it’s customer experience transformation.

Originally published by https://www.datazivot.com/amazon-usa-review-scraping-customer-experience-tech-brand.php

[iwebscrapingblogs]

Websites such as Walmart, Alibaba, Flipkart, and Amazon largely affect the e-commerce industry. Product sellers rely on online platforms to increase their sales and generate revenues. However, it is not just about selling products but receiving products that are in high demand. This e-commerce platform incorporates major data necessary for online businesses.

It is not enough to expect the number of reviews but also consider the star ratings and review texts. As it is mandatory to learn about what the customers are thinking of the products. The star ratings indicate how satisfied the customers are and feedback shows their concept and opinion about the product.

If the product has five star ratings, then most people love the product. On the contrary, if the product is with more reviews but low ratings, then it is better to not consider the product.

Hence, it is necessary to perform product review crawling. Let us dive into the advantages of scraping amazon reviews.

[mobileapp14]

How to Extract Amazon Reviews: Navigating Code and No-Code Solutions

In this guide, we embark on a journey to unravel the intricacies of Amazon review extraction, exploring the depths of coding methodologies and user-friendly no-code alternatives.

know more: https://medium.com/@ridz.2811/how-to-extract-amazon-reviews-navigating-code-and-no-code-solutions-ab4415edb2ef

[iwebdatascraping0]

🛒 Harnessing Customer Feedback: Drive Business Growth with Amazon Review Scraping

In today’s digital-first marketplace, customer opinions are no longer hidden in post-purchase surveys—they’re public, influential, and packed with insight.

At iWeb Data Scraping, we help businesses tap into this rich feedback stream by using #AmazonReviewScraping—a powerful tool to collect, analyze, and act on real-time user sentiment across millions of products.

💡 Why does this matter? Customer reviews aren’t just reflections of past experiences—they’re predictors of future purchasing behavior. By leveraging review scraping, brands can:

🔍 Analyze customer sentiment at scale – Identify patterns in product praise and complaints across thousands of listings. 🛠️ Optimize product design and features – Discover recurring feedback themes that inform updates and new launches. 📊 Refine marketing strategies – Understand what truly resonates with buyers and tailor messaging accordingly. 🏆 Benchmark against competitors – Compare product feedback across competing brands to find your advantage. 📥 Improve inventory and customer service – Monitor reviews for delivery, quality, or packaging issues before they escalate.

🎯 Whether you’re a D2C startup or an established eCommerce brand, Amazon review data offers actionable intelligence to enhance your product lifecycle, from development to retention.

💬 Real feedback. Real-time insights. Real growth.

🔗 Ready to turn Amazon reviews into a competitive edge?

[idataentry01]

Amazon Data Scraping Services

One of the largest online retailers, Amazon is the largest online marketplace in the world, consistently selling more than $200 billion in products each year. With WebScrapingExpert, you can access millions of public records on Amazon that most people will never be able to find on their own.

What is an Amazon Data Scraping Service?

By using a web scraping service, you can easily get access to data that is hidden or difficult to find elsewhere. There are many different types of web scraping services, so it is important to choose the right one for your needs. Amazon web scraping services offers a wide range of Amazon Web Scraping services such as data ingestion, data extraction, and data analysis. With experience in project management, we offer complete solutions for scraping Amazon content. Our software can extract any data you need, from names, product features, and images to category information and reviews.

When your business needs market research or preparation for a directory of products, contact us.

Web-Scraping-Service-Data-Extraction-Data-Scraping-Service

Amazon of Data List

– By Brand – By Category – By Product URLs – By Search Keywords – By SKUs/UPC/ASIN – By Store Name

Amazon Listing of Data Fields

We can scrape following data fields:

– Product Name/Title – Product Description – Product Variants – Brand, Manufacturer – Buy Box Price – List Price – Discounted Price – Offered Price – Buy Box Seller Details – Multiple Seller Details & Prices – ASIN, ISBN, UPC – Bullet Points (Description) – Product Specification – Features – Model Number – Product Type: New & Used – Product Weight & Shipping Weight – Product Images – Merchant Description – Product Reviews & Ratings – Sales Ranking – Shipping Information – Best Seller Ranking (BSR) Under Different Categories

Any type of data can be extracted from a website using WebScrapingExpert - from basic information like titles and URLs to complex content such as product descriptions and customer reviews. Additionally, we can scrape social media sites like Facebook and Twitter to gather valuable market insights.

Amazon ASIN Product Data Scraping

Every Amazon product has an ASIN number, which is a ten-digit numeric code. That is the first identifier assigned when a new product is added to Amazon’s inventory. You must have this number in your account before you can sell on Amazon. The only place you can find it is in the Adding a product page of your Amazon Sellers’ Central account by utilizing the search box on the Amazon page. Find what you’re looking for using the product name, UPC code, or EAN, and make sure it is not already available on Amazon.

Amazon Best Seller Ranks Data Scraping

Best Seller Rank is a measure of total sales on Amazon and shows you where your item sold best in its category.

With WebScrapingExpert, you can easily search lists of tens of thousands of best-selling products in Amazon categories from scraped seller data from Amazon, the United States, and the United Arab Emirates. Furthermore, you can choose which top sellers and which group of sellers you wish to question. You may also specify by category which products you wish to group together.

Amazon Buy Box Price Scraping

Amazon Buy Box is the primary way to purchase items on Amazon. A company that sells products on Amazon for an affordable price will be chosen as an eligible Buy Box vendor. WebScrapingExpert offers professional services related to Amazon's Buy Box system at a reasonable cost. This percentage varies based on each individual writer, and a vendor is chosen from all competing companies.

Amazon Sponsored Listings Scraping

Boost your market presence and increase conversions by using Amazon Sponsored Product Ads.

Amazon Inventory Scraping

We offer a service that tracks Amazon's inventory levels in real time. We'll help you understand what your inventory is, set it up, maintain it, and let you know when your stocks are low. Our Amazon Web Scraping services can help you save time by doing the legwork for you.

Amazon Category Rank Data Scraping

While some sellers may be skeptical of Amazon's category rank, there are a lot of nuances to consider when improving your product or service. A product will rank highly on Amazon at first because its usage will rise. Once sales drop to a new low, its ranking may continue to rise again once the number of sales again rises again. In order to keep your Amazon rankings high, WebScrapingExpert is there to scrape your data from Amazon so you can easily and effectively use it in your ongoing campaigns. Amazon's Category Rank allows you to compare products in the same category with one another. Ranking numbers are not necessarily indicative of sales volume, as is often misunderstood. To attain a higher ranking, a product must sell significantly more than its competitors during a given period of time.

Amazon Seller Data Scraping

Amazon Seller Data allows you to match your products better in your market and beat your competitors by scraping Amazon Seller Data. Using the WebScrapingExpert, you can find Amazon categories, lists, and recent trends, and ask for items that generate the best revenue for your site based on Amazon categories, lists, and trends. From a category, a user can get 100,000 Amazon sellers, or they can select the specific ones they want to mention.

>>Get Your Data in Any File Format

Ecommerce websites scraped data can be sent in following file formats:

XML

JSON

CSV

XLS

Excel

Why choose us?

WebScrapingExpert manages this effortlessly and without any infrastructure to maintain. Sites with limited connectivity or scrapers would have difficulty mining huge amounts of data quickly with a high degree of accuracy. The vast customization options of WebScrapingExpert allow you to complete any data analysis with accuracy and precision. WebScrapingExpert always ensures your query is resolved as soon as possible.

Our team of experts has years of experience extracting data from websites. If you would like to know more about our services or get a quote, feel free to contact us. We will be happy to discuss your project requirements and provide you with a custom solution.

If you are looking for the Amazon Data Scraping services, then email us at: info@webscrapingexpert.com.

[reviewgators]

In this blog, we will extract reviews from amazon.com with how many stars it has, who had posted the reviews, and more.

https://medium.com/@reviewgators/scrape-amazon-reviews-with-scrapy-using-python-28539fedd6ef

[webscreenscraping]

Amazon Data Scraping | Extract Amazon Data For Price, Product, Seller, Inventory, Review, Etc.

Scrape Amazon Product Data

Scraping Amazon product data will allow you to examine the market condition by delivering product reviews, pricing, best-sellers, and other data. Because Amazon is an e-commerce website, it contains necessary information about items and prices. For online data scraping service providers such as Web Screen Scraping, Amazon data scraping is relatively simple and painless. As a competent Amazon data scraping provider, our Amazon data extraction service is the ideal option to meet all of your needs.

 LISTING OF DATA FIELDS

At Web Screen Scraping, we usually scrape the following data fields for Amazon.com

Amazon Of Data List:

By Brand

By Category

By Search Keywords

By SKUs/UPC/ASIN

By Store Name

By Product URLs

Amazon Scrape Products Data :

ASIN To EAN Lookup

ASIN To ISBN

ASIN To MPN

ASIN To SKU

ASIN To UPC

EAN To ASIN Lookup

ISBN To ASIN

MPN To ASIN

SKU To ASIN

UPC To ASIN

Amazon Product Intelligence

Using Amazon product intelligence, you can easily search for top-ranking products, on-demand Amazon products, product details, seller details, shipping details, and many more.

Highlighting key points about Amazon’s highest-selling products.

Helps you monitor products and enhance ranks on Amazon.

Fetch all the product-related information using Amazon data scraping

We assist in analyzing the product patterns using Amazon product data scraping. Amazon data scraping services will determine the best methods for evaluating the best performance and take the necessary steps to enhance the product.

Amazon Price Intelligence

Using Amazon price intelligence, you can easily check the product’s weakness and differentiate the price variation in comparison with competitor’s price scraping results. Web Screen Scraping deliver the services such as:

Amazon price extraction

Amazon price automation

Price extraction for continuous monitoring.

According to research, 61% of internet customers compare prices before buying a product. We extract Amazon data to help you comprehend various Amazon pricing optimization approaches for driving traffic to your website. We can modify the frequency with which the product prices are compared. Our Amazon data scraping services can assist you in managing variable pricing approaches based on client demand.

Amazon Competition Tracking

Amazon data scraping will help you keep a continual eye on the competitor.

Amazon price monitoring for product optimization

Extracting competitor’s data for special offers and discounts

Extract competitor’s Amazon data

Monitor Competitor Pricing for Similar Amazon Products

Amazon competitor monitoring will assist you in identifying product details. Amazon product scraping is used to better understand what your competitor’s are offering.

Amazon Inventory Scraping

Amazon inventories may be tracked and recorded by receiving notifications for sold-out or low-stock products. Amazon Inventory Scraping services will assist you in obtaining the exact goods in the perfect quantities.

Amazon Inventory Updates Automatically

Using the Location-Based Amazon Inventory Scraping Monitor, you can customize product availability and stock levels, as well as adjust inventory counts at Amazon Monitor.

Amazon Manufacturers and Sellers: Stock Alerts

Through Amazon data scraping, we assist in tracking inventory setup, comprehending your inventory, and adjusting inventory counts if the stock reaches a certain amount. We will complete the most time-consuming tasks for you, saving you both money and time.

Amazon Reviews Scraping

Web Screen Scraping’s service for Amazon product ratings and reviews will help to learn consumer’s insights for your product.

Amazon data extraction to gain customer insights.

Reviews and rating evaluation

We fetch the Amazon data, enabling you in obtaining the aspects that will help your items rank higher and receive more favorable reviews.

Web Screen Scraping uses Amazon reviews scraping to assist you to understand the customer's perspective and place the correct items for the right buyers.

[logicwisavani-blog]

[sandersoncarlen]

This blog will effectively explain the benefits of scraping amazon reviews and also guide about how to extract product reviews from Amazon and its usefulness for online sellers to grow their sales.

[unitedleadscraper]

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[simpatel]

Amazon USA | How Review Scraping Boosted Tech Brand CX

Learn how Datazivot helped a U.S. tech brand improve customer experience by scraping Amazon reviews to uncover product issues and drive smarter improvements. This case study explores how Datazivot helped a rising consumer electronics brand extract, analyze, and act on Amazon USA reviews to improve product performance, reduce returns, and drive a 27% boost in customer satisfaction.

[mobileapp14]

How to Extract Amazon Reviews: Navigating Code and No-Code Solutions

know more: https://www.mobileappscraping.com/extract-amazon-reviews.php

[retailgators]

How Can You Extract Amazon Review using Python in 3 Steps?

Introduction

In a web extracting blog, we can construct an Amazon Scraper Review with Python using 3 steps that can scrape data from different Amazon products like – Content review, Title Reviews, Name of Product, Author, Product Ratings, and more, Date into a spreadsheet. We develop a simple and robust Amazon product review scraper with Python.

Here we will show you 3 steps about how to extract Amazon review using Python

1. Markup Data Fields for getting Extracted using Selectorlib.

2. The code needs to Copy as well as run.

3. The data will be downloaded in Excel format.

We can let you know how can you extract product information from the Amazon result pages, how can you avoid being congested by Amazon, as well as how to extract Amazon in the huge scale.

Here, we will show you some data fields from Amazon we scrape into the spreadsheets from Amazon:

Name of Product

Review title

Content Review or Text Review

Product Ratings

Review Publishing Date

Verified Purchase

Name of Author

Product URL

We help you save all the data into Excel Spreadsheet.

Install required package for Amazon Website Scraper Review

Web Extracting blog to extract Amazon product review utilizing Python 3 as well as libraries. We do not use Scrapy for a particular blog. This code needs to run quickly, and easily on a computer.

If python 3 is not installed, you may install Python on Windows PC.

We can use all these libraries: -

Request Python, you can make download and request HTML content for different pages using (http://docs.python-requests.org/en/master/user/install/)

Use LXML to parse HTML Trees Structure with Xpaths – (http://lxml.de/installation.html)

Dateutil Python, for analyzing review date (https://retailgators/dateutil/dateutil/)

Scrape data using YAML files to generate from pages that we download.

Installing them with pip3

pip3 install python-dateutillxml requests selectorlib

The Code

Let us generate a file name reviews.py as well as paste the behind Python code in it.

What Amazon Review Product scraper does?

1. Read Product Reviews Page URL from the file named urls.txt.

2. You can use the YAML file to classifies the data of the Amazon pages as well as save in it a file named selectors.yml

3. Extracts Data

4. Save Data as the CSV known as data.csv filename.

fromselectorlibimport Extractorimport requestsimportjsonfrom time import sleepimport csvfromdateutilimport parser asdateparser# Create an Extractor by reading from the YAML filee = Extractor.from_yaml_file('selectors.yml')defscrape(url):headers = {'authority': 'www.amazon.com','pragma': 'no-cache','cache-control': 'no-cache','dnt': '1',upgrade-insecure-requests': '1','user-agent': 'Mozilla/5.0 (X11; CrOS x86_64 8172.45.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.64 Safari/537.36','accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9','sec-fetch-site': 'none','sec-fetch-mode': 'navigate','sec-fetch-dest': 'document','accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',}# Download the page using requestsprint("Downloading %s"%url)r = requests.get(url, headers=headers)# Simple check to check if page was blocked (Usually 503)ifr.status_code>500:if"To discuss automated access to Amazon data please contact"inr.text:print("Page %s was blocked by Amazon. Please try using better proxies\n"%url)else:print("Page %s must have been blocked by Amazon as the status code was %d"%(url,r.status_code))returnNone# Pass the HTML of the page and createreturne.extract(r.text)with open("urls.txt",'r') asurllist, open('data.csv','w') asoutfile:writer = csv.DictWriter(outfile, fieldnames=["title","content","date","variant","images","verified","author","rating","product","url"],quoting=csv.QUOTE_ALL)writer.writeheader()orurlinurllist.readlines():data = scrape(url)'if data:'for r in data['reviews']:r["product"] = data["product_title"]r['url'] = urlif'verified'in r:if'Verified Purchase'in r['verified']:r['verified'] = 'Yes'else:r['verified'] = 'Yes'r['rating'] = r['rating'].split(' out of')[0] date_posted = r['date'].split('on ')[-1]if r['images']:r['images'] = "\n".join(r['images'])r['date'] = dateparser.parse(date_posted).strftime('%d %b %Y')writer.writerow(r)# sleep(5)

Creating YAML files with selectors.yml

It’s easy to notice the code given which is used in the file named selectors.yml. The file helps to make this tutorial easy to follow and generate.

Selectorlib is the tool, which selects to markup and scrapes data from the web pages easily and visually. The Web Scraping Chrome Extension makes data you require to scrape and generates XPaths Selector or CSS needed to scrape data.

Here we will show how we have marked up field for data we require to Extract Amazon review from the given Review Product Page using Chrome Extension.

When you generate the template you need to click on the ‘Highlight’ option to highlight as well as you can see a preview of all your selectors.

Here we will show you how our templates look like this: -

product_title:css: 'h1 a[data-hook="product-link"]'type: Textreviews:css: 'div.reviewdiv.a-section.celwidget'multiple: truetype: Textchildren:title:css: a.review-titletype: Textcontent:css: 'div.a-row.review-data span.review-text'type: Textdate:css: span.a-size-base.a-color-secondarytype: Textvariant:css: 'a.a-size-mini'type: Textimages:css: img.review-image-tilemultiple: truetype: Attributeattribute: srcverified:css: 'span[data-hook="avp-badge"]'type: Textauthor:css: span.a-profile-nametype: Textrating:css: 'div.a-row:nth-of-type(2) >a.a-link-normal:nth-of-type(1)'type: Attributeattribute: titlenext_page:css: 'li.a-last a'type: Link

Running Amazon Reviews Scrapers

You just need to add URLs to extract the text file named urls.txt within the same the folder as well as run scraper consuming the same commend.

This file shows that if we want to search distinctly for earplugs and headphones.

python3reviews.py

Now, we will show a sample URL - https://www.amazon.com/HP-Business-Dual-core-Bluetooth-Legendary/product-reviews/B07VMDCLXV/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews

It’s easy to get the URL through clicking on the option “See all the reviews” nearby the lowermost product page.

What Could You Do By Scraping Amazon?

The data you collect from the blog can assist you in many ways: -

1. You can review information unavailable using eCommerce Data Scraping Services.

2. Monitor Customer Options on a product that you can see by manufacturing through Data Analysis.

3. Generate Amazon Database Review for Educational Research &Purposes &.

4. Monitor product’s quality retailed by a third-party seller.

Build Free Amazon API Reviews using Python, Selectorlib & Flask

In case, you want to get reviews as the API like Amazon Products Advertising APIs – then you can find this blog very exciting.

If you are looking for the best Amazon Review using Python, then you can call RetailGators for all your queries.

Source:- https://www.retailgators.com/how-can-you-extract-amazon-review-using-python-in-3-steps.php

[simpatel]

Amazon USA | How Review Scraping Boosted Tech Brand CX

Learn how Datazivot helped a U.S. tech brand improve customer experience by scraping Amazon reviews to uncover product issues and drive smarter improvements. This case study explores how Datazivot helped a rising consumer electronics brand extract, analyze, and act on Amazon USA reviews to improve product performance, reduce returns, and drive a 27% boost in customer satisfaction.

[simpatel]

Predicting Product Returns from Amazon Reviews – USA Brands' Approach

Discover how U.S. brands use Amazon reviews and sentiment analysis to predict product returns with Datazivot’s eCommerce review scraping and insights.

[simpatel]

Predicting Product Returns from Amazon Reviews – USA Brands' Approach

Discover how U.S. brands use Amazon reviews and sentiment analysis to predict product returns with Datazivot’s eCommerce review scraping and insights.

Originally Published By https://www.datazivot.com/usa-brands-use-amazon-reviews-to-predict-returns.php