#Web Scraping Services in India

[uniquesdata]

A Detailed Website Scraping Guide for Businesses

Web data scraping for various business industries is vital in a data driven world for extracting useful information to help make smart decisions, reformulate strategies, unlock market insights, and more. Here’s a comprehensive guide on website scraping for businesses.

[actowizsolutions0]

News Extract: Unlocking the Power of Media Data Collection

In today's fast-paced digital world, staying updated with the latest news is crucial. Whether you're a journalist, researcher, or business owner, having access to real-time media data can give you an edge. This is where news extract solutions come into play, enabling efficient web scraping of news sources for insightful analysis.

Why Extracting News Data Matters

News scraping allows businesses and individuals to automate the collection of news articles, headlines, and updates from multiple sources. This information is essential for:

Market Research: Understanding trends and shifts in the industry.

Competitor Analysis: Monitoring competitors’ media presence.

Brand Reputation Management: Keeping track of mentions across news sites.

Sentiment Analysis: Analyzing public opinion on key topics.

By leveraging news extract techniques, businesses can access and process large volumes of news data in real-time.

How News Scraping Works

Web scraping involves using automated tools to gather and structure information from online sources. A reliable news extraction service ensures data accuracy and freshness by:

Extracting news articles, titles, and timestamps.

Categorizing content based on topics, keywords, and sentiment.

Providing real-time or scheduled updates for seamless integration into reports.

The Best Tools for News Extracting

Various scraping solutions can help extract news efficiently, including custom-built scrapers and APIs. For instance, businesses looking for tailored solutions can benefit from web scraping services India to fetch region-specific media data.

Expanding Your Data Collection Horizons

Beyond news extraction, companies often need data from other platforms. Here are some additional scraping solutions:

Python scraping Twitter: Extract real-time tweets based on location and keywords.

Amazon reviews scraping: Gather customer feedback for product insights.

Flipkart scraper: Automate data collection from India's leading eCommerce platform.

Conclusion

Staying ahead in today’s digital landscape requires timely access to media data. A robust news extract solution helps businesses and researchers make data-driven decisions effortlessly. If you're looking for reliable news scraping services, explore Actowiz Solutions for customized web scraping solutions that fit your needs.

[ecommerceserviceprovider]

The process of gathering information from multiple sources—including websites, databases, spreadsheets, documents, text files, and more—is known as data scraping. Data integration, migration, analysis, and information retrieval are just a few of the uses for data scraping.

[outsourcebigdata]

Best data extraction services in USA

In today's fiercely competitive business landscape, the strategic selection of a web data extraction services provider becomes crucial. Outsource Bigdata stands out by offering access to high-quality data through a meticulously crafted automated, AI-augmented process designed to extract valuable insights from websites. Our team ensures data precision and reliability, facilitating decision-making processes.

For more details, visit: https://outsourcebigdata.com/data-automation/web-scraping-services/web-data-extraction-services/.

About AIMLEAP

Outsource Bigdata is a division of Aimleap. AIMLEAP is an ISO 9001:2015 and ISO/IEC 27001:2013 certified global technology consulting and service provider offering AI-augmented Data Solutions, Data Engineering, Automation, IT Services, and Digital Marketing Services. AIMLEAP has been recognized as a ‘Great Place to Work®’.

With a special focus on AI and automation, we built quite a few AI & ML solutions, AI-driven web scraping solutions, AI-data Labeling, AI-Data-Hub, and Self-serving BI solutions. We started in 2012 and successfully delivered IT & digital transformation projects, automation-driven data solutions, on-demand data, and digital marketing for more than 750 fast-growing companies in the USA, Europe, New Zealand, Australia, Canada; and more. 

-An ISO 9001:2015 and ISO/IEC 27001:2013 certified  -Served 750+ customers  -11+ Years of industry experience  -98% client retention  -Great Place to Work® certified  -Global delivery centers in the USA, Canada, India & Australia 

Our Data Solutions

APISCRAPY: AI driven web scraping & workflow automation platform APISCRAPY is an AI driven web scraping and automation platform that converts any web data into ready-to-use data. The platform is capable to extract data from websites, process data, automate workflows, classify data and integrate ready to consume data into database or deliver data in any desired format. 

AI-Labeler: AI augmented annotation & labeling solution AI-Labeler is an AI augmented data annotation platform that combines the power of artificial intelligence with in-person involvement to label, annotate and classify data, and allowing faster development of robust and accurate models.

AI-Data-Hub: On-demand data for building AI products & services On-demand AI data hub for curated data, pre-annotated data, pre-classified data, and allowing enterprises to obtain easily and efficiently, and exploit high-quality data for training and developing AI models.

PRICESCRAPY: AI enabled real-time pricing solution An AI and automation driven price solution that provides real time price monitoring, pricing analytics, and dynamic pricing for companies across the world. 

APIKART: AI driven data API solution hub  APIKART is a data API hub that allows businesses and developers to access and integrate large volume of data from various sources through APIs. It is a data solution hub for accessing data through APIs, allowing companies to leverage data, and integrate APIs into their systems and applications. 

Locations: USA: 1-30235 14656  Canada: +1 4378 370 063  India: +91 810 527 1615  Australia: +61 402 576 615 Email: Sales@aimleap.com

[callofdutymobileindia]

Real Projects You Should Build During a Machine Learning Course in Mumbai

Taking a Machine Learning Course in Mumbai is a smart move. The city is packed with start-ups, analytics firms, financial institutions, and healthtech companies—all increasingly reliant on machine learning to drive business decisions.

But here’s the thing: learning the theory alone isn’t enough. To stand out in the job market—or even to fully understand what you're doing—you need to build real, end-to-end projects that go beyond toy datasets or textbook examples.

The right machine learning projects demonstrate that you know how to apply ML in real-world scenarios. They show you understand data cleaning, feature engineering, model evaluation, and—most importantly—problem solving.

Let’s walk through the kinds of projects you should aim to complete during your course, especially if you’re serious about landing a role in Mumbai’s ML-driven ecosystem.

1. Customer Churn Prediction for a Telecom Company

Why it matters:

Mumbai is home to several telecom and consumer service companies. Churn prediction is one of the most in-demand applications of ML across B2C businesses.

What you'll learn:

Data preprocessing on messy, real-world customer data

Feature engineering with call logs, internet usage, and complaint history

Logistic regression, decision trees, or ensemble models

Handling imbalanced datasets

Evaluation metrics beyond accuracy (AUC, recall, precision)

Bonus:

Try deploying the model using Flask or FastAPI. Employers love seeing deployed models.

2. Fake News Detection Using Natural Language Processing (NLP)

Why it matters:

In a media-heavy city like Mumbai—with its publishing houses, digital marketing agencies, and political think tanks—content verification is a growing use case.

What you'll learn:

Text vectorization (TF-IDF, word embeddings)

Training models using Naive Bayes, SVM, or LSTM

NLP preprocessing (tokenization, stopword removal, lemmatization)

Model interpretability for textual data

Performance tuning on unstructured data

3. Credit Risk Scoring for Loan Approval

Why it matters:

Mumbai is India’s financial capital. ML in fintech isn’t a trend—it’s the backbone of lending, credit scoring, and fraud detection.

What you'll learn:

Handling missing data and outliers

Using ensemble methods (Random Forest, XGBoost) for classification

Creating custom scorecards

Interpreting model predictions in high-stakes environments

Fairness and bias in financial models

4. Sales Forecasting for a Retail Chain

Why it matters:

Retailers and e-commerce firms across Mumbai use ML to predict product demand, manage inventory, and plan logistics.

What you'll learn:

Time series analysis (ARIMA, Prophet, LSTM)

Seasonality and trend decomposition

Lag features and rolling statistics

Model performance over time windows

Real-world demand prediction challenges

5. Sentiment Analysis on Mumbai Restaurant Reviews

Why it matters:

Mumbai's booming food and hospitality sector thrives on reviews. Brands want to know what customers are really saying.

What you'll learn:

Web scraping (Zomato, Google Reviews)

NLP pipelines for cleaning and tokenizing reviews

Sentiment classification (positive/negative/neutral)

Topic modeling to identify key complaints or praises

Word clouds or dashboards for presentation

6. Traffic Congestion Prediction Using GPS and Weather Data

Why it matters:

Anyone who’s driven through Mumbai knows the traffic is unpredictable. ML models that forecast congestion have direct real-world utility.

What you'll learn:

Working with geospatial and temporal data

Feature engineering with time, location, and weather variables

Regression models with continuous targets

Evaluating RMSE, MAE, and time-lag effects

Visualization with Plotly or Seaborn for maps and trends

7. Image Classification of Waste for Smart City Applications

Why it matters:

Mumbai’s Smart City initiatives are increasingly looking to tech for urban waste management and automation.

What you'll learn:

Image preprocessing using OpenCV or PIL

Building CNNs with Keras or PyTorch

Multi-class classification (plastic, paper, organic, etc.)

Model augmentation to deal with small datasets

Transfer learning with pretrained models (ResNet, VGG)

8. Resume Screening Tool for Mumbai-Based Recruiters

Why it matters:

HR tech and recruitment firms in Mumbai are flooded with resumes. ML can automate pre-screening based on job descriptions.

What you'll learn:

Text parsing from PDFs or Word files

Entity extraction (skills, experience, education)

Text similarity between job descriptions and resumes

NLP embeddings and cosine similarity

Scoring and ranking system for resumes

9. Stock Price Movement Prediction for NSE Companies

Why it matters:

Mumbai is home to the National Stock Exchange (NSE). While no model can predict stocks perfectly, ML can identify patterns and indicators.

What you'll learn:

Working with real-time or historical financial data

Feature generation from technical indicators (SMA, RSI)

Time series regression with LSTM or XGBoost

Cautionary practices to avoid overfitting

Use of walk-forward validation instead of random splits

10. Personalized Movie Recommender System Based on Indian Viewers

Why it matters:

OTT platforms and production houses based in Mumbai are increasingly using ML to personalize content recommendations.

What you'll learn:

Collaborative vs content-based filtering

Matrix factorization techniques

Using cosine similarity for item-based recommendations

Implicit vs explicit feedback handling

Creating a recommendation API endpoint

Final Thoughts

If you're taking a Machine Learning Course in Mumbai, don’t just aim for completion—aim for competence. And the best way to build it? Real, context-rich projects that show you can handle the messy, unpredictable nature of real-world data.

Mumbai’s job market for machine learning talent is competitive, but it's also wide open for candidates who can demonstrate applied skill. Whether you're targeting roles in fintech, media, logistics, or health analytics, the right projects will set you apart.

So pick 3–5 of these project ideas, go deep, and document your process. You won’t just pass your course—you’ll build the portfolio that gets interviews.

[uniquesdata]

Best Tools & Techniques for Data Extraction from Multiple Sources

Data extraction is common and rapidly grown in the business landscape. As the technology advances, it is vital to update tools and techniques for best extracted outcomes. Read further in detail about tools and techniques for data extraction services.

[travelscrape]

Extract Monsoon Promotion Flight Data for Fare Opportunities

Introduction

Our advanced travel data scraping services enabled a leading online travel agency to Extract monsoon promotion flight data across major domestic and international airlines operating in India. The client faced challenges in tracking rapidly changing flight deals during the monsoon season, where flash promotions and limited-time discounts are highly dynamic and time-sensitive.

Using our custom scraping infrastructure, we monitored multiple airline websites and OTA platforms in real time. This allowed the client to identify trends in fare slashes, special weekend discounts, and route-specific offers. By leveraging this data, the client updated their pricing engine instantly and presented the best offers to users ahead of competitors. The implementation resulted in a 28% increase in user engagement and a notable boost in booking conversions.

Our services also supported efforts to scrape airline fare drops India, enabling the client to predict promotional patterns and drive intelligent marketing campaigns.

Our Client

Our client, a fast-growing travel-tech company based in Singapore, aimed to strengthen its presence across key Asian markets by offering competitive and timely flight deals. To achieve this, they partnered with us for Real-time flight price scraping Southeast Asia, focusing on monitoring fare variations across budget and premium airlines. We developed a scalable solution for Web scraping airline fares India and other major countries in the region, capturing live data from airline portals and aggregator websites. By leveraging Real-time data scraping for flight promotions, the client was able to detect flash sales, festival-based offers, and sudden price drops. This gave them a significant competitive edge in pricing strategy, resulting in higher user retention, increased bookings, and a stronger position in the regional travel market.

Challenges in the Travel Industry

Tracking flight deals during peak travel periods can be challenging. Our real-time scraping solutions empower travel businesses to capture dynamic airline promotions, ensuring competitive pricing and improved booking performance across regions.

Lack of Timely Access to Regional Offers: The client struggled to Extract flight pricing trends during monsoon season, missing out on limited-time promotions and flash deals published without prior notice.

Inconsistent Data Across Sources: Attempting to Scrape airline discount data in Southeast Asia revealed inconsistencies in fare listings across airline websites and OTAs, leading to unreliable price comparisons.

Scalability Issues with Legacy Tools: Their existing tools failed to keep pace with the expanding demand, highlighting the need for a robust Travel Web Scraping Service capable of real-time data extraction across multiple platforms.

Complex Fare Structures: Airlines Data Scraping became challenging due to dynamic pricing, hidden fees, and route-specific conditions that varied by time and location.

Integration Difficulties: Without a flexible Travel Scraping API, integrating scraped data into their existing analytics and pricing engine proved time-consuming and error-prone.

Our Approach

Custom Scraping Frameworks: We build tailored scrapers to meet the specific needs of our clients, ensuring the accurate and scalable extraction of flight prices, discounts, and promotional data.

Real-Time Monitoring: Our system continuously tracks airline websites and OTAs to capture live updates, enabling clients to react instantly to fare changes and promotions.

Geo-Targeted Data Collection: We focus on region-specific sources, helping clients gather highly relevant data, such as Southeast Asia monsoon deals or airline promotions in India.

Seamless API Integration: We deliver structured data through robust APIs, ensuring smooth integration with pricing engines, dashboards, and travel platforms.

Quality Checks & Data Normalization: Scraped data is cleaned, validated, and standardized to support consistent analysis, reliable reporting, and actionable travel intelligence.

Results Achieved

We help travel platforms capture real-time flight deals, enabling smarter pricing, faster updates, and higher conversions across competitive travel markets.

25% Increase in Booking Conversions: By accessing real-time flight promotions, the client attracted more users with timely offers, directly boosting booking rates.

Faster Response to Price Drops: Automated monitoring enabled immediate updates on flash deals, improving user satisfaction and deal visibility.

Expanded Market Reach in Southeast Asia: Region-specific scraping helped capture localized discounts, enhancing relevance and appeal in new markets.

Improved Pricing Intelligence: With accurate and timely data, the client refined their pricing strategies and stayed ahead of competitors.

Enhanced Platform Engagement: Live fare updates and curated promotions led to higher session durations and repeat visits from deal-savvy travelers.

Client's Testimonial

"Our experience with this team has been exceptional. Their ability to extract flight promotion data in real time gave us a major edge during the monsoon travel season. With their tailored scraping setup, we could monitor fare changes instantly and update our platform faster than ever before. The data accuracy, scalability, and ongoing support exceeded our expectations. It's rare to find a partner so committed to performance and reliability in travel data."

—Director of Marketing

Conclusion

In today's highly competitive travel market, having access to accurate and timely flight data is essential for delivering value-driven services. Our advanced solutions for Real-time web scraping airline fare monitoring empower travel platforms to stay ahead of rapid pricing shifts, seasonal promotions, and competitor strategies. By enabling data-driven decisions and faster user engagement, our tools help maximize conversions and enhance customer satisfaction. From regional campaigns to global fare tracking, we support scalable, real-time intelligence that drives growth. Whether you're managing OTAs, price comparison engines, or Travel Aggregators Scraping, our expertise ensures you never miss an opportunity in the fast-moving airline industry.

Source : https://www.travelscrape.com/extract-monsoon-promotion-flight-data.php

[thatware03]

Unlocking AI Search Dominance: Why AEO for Google Gemini and Bard Is the Future of SEO

The SEO world is evolving faster than ever with the integration of artificial intelligence in search engines. Traditional keyword-stuffed strategies are losing ground to smarter, intent-driven approaches. The future lies in Answer Engine Optimization (AEO) — especially as AI-powered search systems like Google Gemini and Bard redefine how users discover content.

At Thatware LLP, we specialize in AEO strategies tailored for these next-generation platforms. As a leading AEO agency, our mission is to help businesses adapt to the new rules of AI search and future-proof their visibility across conversational search tools.

What is AEO and Why It Matters Now?

Answer Engine Optimization (AEO) is the process of optimizing your content to provide direct, concise, and authoritative answers to user questions—particularly for AI-powered engines and virtual assistants. Unlike traditional SEO, which focuses on ranking on SERPs, AEO focuses on delivering your content as the direct answer in AI-generated responses.

With the introduction of Google Gemini and Bard, AI search engines are now conversational, contextual, and highly selective. They pull data not just from indexed websites but from structured data, entities, and NLP-driven context mapping. That means if your content isn't optimized for answer intent, you're invisible.

Why AEO for Google Gemini Is a Game-Changer

Google Gemini, the successor to Google's LaMDA, is a multimodal AI model designed to understand and respond to queries across text, image, and code. Unlike Google’s traditional algorithms, Gemini draws from its vast AI training to summarize, interpret, and deliver answers—often without linking back to a source unless the content is contextually clear and authoritative.

Here’s why AEO for Google Gemini is essential:

AI Summarization Bias: If your content isn’t semantically structured, Gemini might skip it while generating answers.

Entity-first Indexing: Gemini emphasizes understanding “who,” “what,” and “why.” Structured content and schema markup give your pages an edge.

Conversational Relevance: Gemini anticipates intent behind queries. AEO allows your content to align with likely questions and deliver optimal relevance.

At Thatware LLP, we help clients adapt their content strategy to meet the technical and contextual expectations of Gemini using advanced knowledge graphs, schema integration, and conversational UX frameworks.

Optimizing for Bard: AEO for Bard Demands Contextual Precision

AEO for Bard focuses on optimizing content to be featured in responses generated by Google’s Bard AI. Bard, like Gemini, uses a large language model trained on natural language and web content. However, Bard prioritizes current data and live web scraping, meaning your content has to be:

Frequently updated

Rich in structured metadata

Optimized for long-tail conversational queries

Unlike static search results, Bard seeks dynamic insights. For example, if someone asks, "What is the best AEO agency in India for Gemini optimization?"—Bard might not just show links. It will summarize the best agency based on authority, reviews, semantic alignment, and relevance.

At Thatware LLP, we optimize web pages with real-time SEO signals, conversational content design, and schema strategies that make your content Bard-friendly.

How Thatware LLP Leads as a Specialized AEO Agency

Not all SEO agencies are built for the AI-first world. At Thatware LLP, our specialization in AEO services makes us one of the leading names in AI-driven optimization. As a trusted AEO agency, we offer:

Entity Optimization

We map your brand and offerings into Google’s Knowledge Graph using structured data and Wikidata-backed entries.

Schema and NLP Structuring

From FAQs to How-Tos, we use advanced schema markup to help AI engines identify and surface the right answers.

Semantic Search Engineering

We optimize your content not just for keywords, but for semantic clusters and question-driven search patterns—ideal for Gemini and Bard.

Prompt-based Testing

We simulate AI chatbot queries across Gemini, Bard, and ChatGPT to assess your content visibility and make real-time AEO tweaks.

Real-time AI Audits

Our proprietary tools audit how your website content performs in AI-generated answers and identifies gaps for improvement.

Real-World Benefits of AEO for Gemini and Bard

Higher Visibility Without Click Dependence: AI search answers are often zero-click. AEO ensures your brand gets mentioned even when there’s no direct link.

Voice Search Domination: AEO principles apply directly to voice assistants like Google Assistant and Alexa.

Better Conversion Rates: AI answers often influence immediate decisions. Being the trusted source boosts your lead quality and conversion potential.

AEO Is the Future. Are You Ready?

As search evolves from keywords to conversational AI, traditional SEO will no longer be enough. Businesses that invest in AEO for Bard and AEO for Google Gemini today will be the thought leaders of tomorrow’s digital landscape.

At Thatware LLP, we’re already helping global brands shift their strategy from SEO to AEO with measurable success. Whether you're a B2B SaaS firm, eCommerce store, or an enterprise-level company—our AEO agency is ready to future-proof your digital presence.

Ready to Get Started with AI-Optimized Search?

If you're struggling to get noticed in Gemini or Bard, it's time to think differently. Let Thatware LLP be your strategic partner in building an AI-proof content strategy.

[productdata]

Real-Time Grocery Price Monitoring For Zepto, Blinkit & BigBasket

Introduction

India’s quick commerce boom has transformed how millions shop for groceries. To keep up with changing prices, offers, and hyperlocal stock availability, Indian startups are embracing Real-Time Grocery Price Monitoring For Zepto, Blinkit & BigBasket. Product Data Scrape helps these brands build competitive, real-time insights into grocery price shifts, discounts, and competitor tactics. By combining Scrape Real-Time Price Data from Zepto, Blinkit & BigBasket with robust tracking tools, startups now respond instantly to market moves and demand spikes. In this case study, discover how real-time price monitoring has powered smarter pricing, increased sales, and improved customer loyalty for India’s fastest-growing quick commerce brands.

The Client

An emerging grocery aggregator startup approached Product Data Scrape with one clear goal: outperform larger players by using reliable, granular price intelligence across India’s top grocery delivery apps. This client needed Real-Time Grocery Price Monitoring For Zepto, Blinkit & BigBasket to spot price drops, match competitor discounts, and adjust their own offers dynamically. With fierce local competition and daily pricing changes, it was no longer enough to rely on manual checks or outdated spreadsheets. The client also wanted to scale insights across multiple cities and store formats, covering essentials, fresh produce, and Gourmet Food Data. The need for a trusted partner who could deliver Real-Time Data Monitoring for Grocery Prices & Discounts led them to Product Data Scrape’s proven expertise. The client’s vision was clear: get real-time data or get left behind.

Key Challenges

The client faced several challenges typical for India’s quick commerce and grocery tech startups. First, manual price checks on Zepto, Blinkit, and BigBasket were time-consuming and error-prone, missing daily promotions and location-specific discounts. Second, without a system to Scrape Real-Time Price Data from Zepto, Blinkit & BigBasket, they couldn’t confidently match or beat competitor offers, which led to lost customers. Third, the team struggled to handle huge volumes of SKU-level data with variations across cities, PIN codes, and product categories. They needed Real-Time Grocery Price Tracking from Zepto, Blinkit & BigBasket to feed their pricing engines and marketing tools automatically. Additionally, they lacked robust tools for Quick Commerce Grocery & FMCG Data Scraping, which meant missing insights on emerging neighborhood-level demand. They also required clean integrations with Real-Time Indian Grocery Price Scraping APIs , so their tech stack could automatically update pricing dashboards daily. Without dependable Grocery Price Monitoring Scraper For Zepto, Blinkit & BigBasket, their strategy was reactive instead of proactive. Staying competitive demanded a scalable solution to Extract Blinkit Grocery & Gourmet Food Data , Extract Bigbasket Product Data , and launch smarter promotions instantly.

Key Solutions

Product Data Scrape built a robust solution covering every pain point. We deployed dedicated crawlers to Scrape Real-Time Price Data from Zepto, Blinkit & BigBasket with 99% accuracy. The client gained city-level price feeds that updated hourly, fueling their dynamic pricing engine with precise SKU details and store-specific offers. Our team customized Zepto Grocery Data Scraping modules to capture neighborhood differences for quick commerce. Combined with Web Scraping Grocery Price Data, they could compare pricing trends, track discounts, and identify competitors’ loss leaders. To scale, we integrated Real-Time Indian Grocery Price Scraping APIs into the client’s dashboards, giving instant visibility into price gaps and fresh offers.

Product Data Scrape also activated Grocery & Supermarket Data Scraping Services for broader market mapping, including insights from smaller grocery stores and specialty listings. The client used our Grocery Data Scraping Services to enhance supply chain forecasting and inventory planning with a high-quality Grocery Store Dataset. Our tools helped them Scrape Grocery & Gourmet Food Data to spot premium product trends, boosting margins with curated assortments. Together, this complete solution turned chaotic market signals into actionable pricing strategies. Today, the client uses Product Data Scrape for continuous Grocery Price Monitoring Scraper For Zepto, Blinkit & BigBasket, plus robust Quick Commerce Grocery & FMCG Data Scraping to stay ahead in India’s competitive grocery space.

Client’s Testimonial

"Product Data Scrape transformed how we compete. Their Real-Time Grocery Price Monitoring For Zepto, Blinkit & BigBasket helps us adjust prices daily, match discounts, and win more loyal customers. Their data scraping quality and support are unmatched."

— Head of Growth, Leading Indian Quick Commerce Startup

Conclusion

Real-time grocery price tracking is no longer optional for India’s quick commerce brands — it’s mission-critical. This case study proves that Real-Time Grocery Price Monitoring For Zepto, Blinkit & BigBasket drives competitive advantage, sharper pricing, and smarter promotions. Product Data Scrape remains the trusted partner for startups that need powerful Grocery Price Monitoring Scraper For Zepto, Blinkit & BigBasket and ready-to-use insights that fuel growth. Get started with Product Data Scrape today and unlock your edge in India’s grocery market!

Unlock More Info>>>https://www.productdatascrape.com/real-time-grocery-price-monitoring-zepto-blinkit-bigbasket.php

[crawlxpert01]

Extracting Customer Reviews from Food Delivery Apps for Sentiment Analysis

Introduction

Customer reviews are a goldmine of information for businesses operating in the food delivery industry. With these reviews, customers could give an insight into areas they are satisfied with, their preferences, and what they want to improve. The plethora of food delivery websites like Uber Eats, DoorDash, Grubhub, and Zomato has now driven most businesses to extract and analyze customer data to improve their delivery systems for better market competition.

Sentiment analysis helps organizations perform customer-level analysis and trend detection, which can then be used to develop products based on real-world, actual feedback. Web scraping will also help businesses gather customer reviews/feedback from food delivery apps, and analyze sentiment in data collected from different sources such as apps and online communities. This blog examines the extraction of customer reviews, their methods of web scraping, the challenges involved, and effective practice techniques for conducting sentiment analysis.

Why Extract Customer Reviews from Food Delivery Apps?

1. Understanding Customer Sentiment

Customer reviews are often written to show the feelings that exist within a group of customers for a restaurant, a food item, or even a delivery service. Through this analysis of the sentiment, businesses can quantify the general trend in the attitudes of their customers, be they happy, dissatisfied, or neutral.

2. Pain Points Detection

By extracting reviews, it helps businesses identify recurring customer complaints, for example, late deliveries, food quality issues, high service charges, and poor customer care. Addressing these common issues will lead to enhanced satisfaction for all consumers.

3. Competitive Benchmarking

Through a competitive review of comments, a food delivery service will understand where it stands compared to its competitors in the service, prices, and quality offered. Competitive sentiment analysis provides wonderful insights into companies' strengths and shortcomings.

4. Enhancing Brand Reputation

Businesses can build an improved reputation and have great relationships with their customers by tracking customer reviews and taking proactive actions on negative customer reviews.

5. Data-based Decision Making

The analysis of customer sentiment assists businesses in making decisions based on data from a given customer when it comes to menu enhancement, pricing changes, and marketing campaigns.

Methodologies for Extracting Customer Reviews

1. Identifying Target Platforms

To perform sentiment analysis, businesses must collect reviews from leading food delivery platforms. Some major apps include:

Uber Eats – Offers a wide range of restaurant options globally.

DoorDash – Dominant in North America, known for rapid deliveries.

Grubhub – Features local and chain restaurants with extensive customer feedback.

Zomato – Popular in India and the Middle East, known for detailed restaurant reviews.

Swiggy – Leading Indian food delivery service with comprehensive user feedback.

Postmates – Provides food, groceries, and essentials with customer ratings.

Deliveroo – Strong presence in the UK and Europe, with extensive review data.

Just Eat – Offers food delivery services across European markets.

Foodpanda – Serves Asia and Eastern Europe with multilingual customer reviews.

Glovo – Provides restaurant deliveries along with grocery and retail services.

2. Data Fields to Extract

Key data points to extract from customer reviews include:

Review Text – The actual customer feedback.

Rating Score – Numeric rating (e.g., 1-5 stars).

Customer Name (if available) – Some platforms display user details.

Date of Review – Helps track sentiment trends over time.

Restaurant Name – Identifies which restaurant the review belongs to.

Order Details (if available) – Extracting menu items for granular analysis.

3. Web Scraping Tools and Techniques

Web scraping is the primary method for extracting reviews from food delivery apps. Some commonly used tools and technologies include:

Scrapy – A Python-based framework ideal for large-scale data extraction.

Selenium – Useful for scraping JavaScript-rendered pages.

BeautifulSoup – Extracts structured data from HTML pages.

Puppeteer – Automates headless browser interactions for complex websites.

API Integration – Some platforms offer official APIs for structured data access.

4. Handling Anti-Scraping Mechanisms

Food delivery platforms employ various anti-scraping measures, including:

CAPTCHAs – Preventing bots from excessive data extraction.

IP Rate Limiting – Restricting access based on request frequency.

Dynamic Content Loading – Using JavaScript to load reviews asynchronously.

To overcome these challenges, businesses use techniques like:

Rotating IP Proxies – Prevents detection by mimicking multiple users.

User-Agent Rotation – Avoids detection by altering browser identifiers.

Headless Browsing – Simulates real user behavior for seamless data extraction.

Sentiment Analysis of Extracted Customer Reviews

1. Preprocessing the Data

Before performing sentiment analysis, extracted reviews must be cleaned and structured. Steps include:

Removing Special Characters & HTML Tags – Eliminates unwanted symbols and formatting.

Tokenization – Splitting text into individual words for analysis.

Stopword Removal – Eliminates common words that do not add meaning (e.g., "the," "and").

Lemmatization – Converts words to their root forms (e.g., "delivering" → "deliver").

2. Sentiment Analysis Techniques

Once preprocessed, reviews undergo sentiment analysis using various techniques:

a. Lexicon-Based Approach

This method uses predefined sentiment dictionaries (e.g., VADER, TextBlob) to assign sentiment scores to words and determine overall sentiment polarity (positive, negative, or neutral).

b. Machine Learning Models

Supervised ML algorithms such as Naive Bayes, Support Vector Machines (SVM), and Logistic Regression are trained on labeled review datasets to classify sentiment.

c. Deep Learning Methods

Advanced neural networks like Long Short-Term Memory (LSTM) and Transformers (e.g., BERT) analyze sentiment based on contextual word meanings.

3. Visualizing Sentiment Trends

Extracted sentiment data can be visualized using:

Word Clouds – Highlighting frequently mentioned keywords in reviews.

Sentiment Distribution Charts – Displaying positive, negative, and neutral review proportions.

Time Series Analysis – Tracking sentiment trends over time.

Challenges in Extracting and Analyzing Customer Reviews

1. Review Manipulation & Fake Reviews

Many businesses post fake reviews to manipulate their ratings. Detecting and filtering fake reviews requires advanced anomaly detection algorithms.

2. Language & Context Challenges

Customer reviews often include slang, abbreviations, and context-dependent meanings, making sentiment analysis complex.

3. Data Privacy & Compliance

Extracting customer reviews must comply with GDPR, CCPA, and platform-specific policies to ensure ethical data usage.

4. Sentiment Ambiguity

Some reviews contain mixed sentiments (e.g., "The food was great, but delivery was late"). Multi-label sentiment classification helps address this issue.

Best Practices for Review Extraction and Sentiment Analysis

Use API Access When Available – Avoid scraping by using official APIs if provided.

Ensure Compliance with Data Regulations – Follow ethical and legal guidelines while extracting data.

Implement AI-Powered Fake Review Detection – Use machine learning to filter out suspicious reviews.

Regularly Update Scraping Scripts – Adapt to website structure changes and prevent data extraction failures.

Monitor Sentiment Trends in Real-Time – Set up dashboards for continuous monitoring of customer feedback.

Conclusion

What must be eye-catching are customer reviews from food delivery apps, as they provide important insights for businesses into how they can improve the customer experience as well as services. Structured data extraction techniques such as web scraping, API integration, and manual collection can help organizations mine sentiment trends for data-based strategic formulation and comply with data privacy legislation governing ethical data handling. CrawlXpert provides advanced extraction and analytics solutions to help make customer reviews more accessible. This will give organizations a competitive advantage in the food delivery business.

Know More : https://www.crawlxpert.com/blog/extracting-customer-reviews-from-food-delivery-apps

[uniquesdata]

Unlock the Power of Data with Uniquesdata's Data Scraping Services!

In today’s data-driven world, timely and accurate information is key to gaining a competitive edge. 🌐

At Uniquesdata, our Data Scraping Services provide businesses with structured, real-time data extracted from various online sources. Whether you're looking to enhance your e-commerce insights, analyze competitors, or improve decision-making processes, we've got you covered!

💼 With expertise across industries such as e-commerce, finance, real estate, and more, our tailored solutions make data accessible and actionable.

📈 Let’s connect and explore how our data scraping services can drive value for your business.

[actowizsolutions0]

Tracking EV Price Trends with Web Scraping by Actowiz Solutions

Introduction

As electric vehicles (EVs) dominate headlines and showroom floors, buyers, sellers, and investors are racing to stay ahead of price trends. Whether you're a mobility startup, automotive analyst, or EV manufacturer, price transparency and market intelligence are essential for making informed decisions.

But there's a problem—EV pricing is dynamic, fragmented, and varies dramatically by platform, city, and model year. There’s no single source to monitor daily EV price fluctuations, and most listing platforms offer limited analytical visibility.

Actowiz Solutions, a global leader in web scraping and data intelligence, is changing that with its powerful, scalable EV price trend scraping service. By extracting real-time pricing and listing data from marketplaces like CarDekho, Cars.com, OLX Autos, EV Trader, and Autotrader, Actowiz empowers businesses with electric vehicle pricing insights that were once buried in thousands of scattered listings.

Why Track EV Price Trends?

The shift to EVs isn't just technological—it’s financial. With growing demand, fluctuating subsidies, and evolving battery life concerns, electric vehicle pricing data is a strategic asset. Some key reasons to monitor EV pricing include:

Price Volatility: EV prices shift frequently due to incentives, battery costs, and supply chain pressures.

Resale Analytics: Understand how well EV models retain value compared to ICE counterparts.

Market Entry: Identify high-demand regions based on price elasticity and average resale time.

Inventory Pricing: Help dealerships and resellers align with fair market values.

Depreciation Tracking: See which EV models drop fastest in value post-purchase.

The Fragmented EV Listing Landscape

Unlike traditional gasoline vehicles, electric vehicles are often listed on niche and mainstream platforms, such as:

EVTrader.com

Cars.com (USA)

CarDekho EV (India)

Autotrader UK

OLX Autos (India, UAE, Brazil)

Each has its own UI, search filters, and update cycles. This fragmentation makes it nearly impossible to get consistent, structured insights manually.

That’s where EV web scraping becomes the most scalable, real-time solution.

Actowiz Solutions’ EV Web Scraping Capabilities

Multi-Platform Coverage

Actowiz scrapes and analyzes data from leading EV marketplaces and general automotive platforms, including:

EVTrader (global EV-only listings)

Cars.com, Carvana, TrueCar (USA)

OLX Autos, CarDekho (India)

AutoScout24, Autotrader (Europe)

Data Fields Extracted

Make & Model: Vehicle name (e.g., Tesla Model 3, Tata Nexon EV)

Year: Model year of the vehicle

Battery Capacity: Measured in kWh (if available)

Range: Estimated distance per full charge

Fuel Type: EV / Hybrid / PHEV

Price: Listed selling price (local currency)

Location: City, state, or ZIP code of listing

Seller Type: Dealer or individual seller

Mileage: Distance driven (in kilometers or miles)

Condition: Whether the vehicle is new or used

Charging Type: Fast or slow charging (if specified)

Listing Date: Date the ad was posted

Contact Info: Phone/email (if publicly visible)

Real-Time & Batch Access

Actowiz offers:

Real-time scraping via REST APIs

Daily or hourly batch datasets

Custom dashboards with filtering, charting, and price alerts

Clients can request:

Pin code-level EV price tracking

Brand-wise price breakdown (Tesla, BYD, Tata, Hyundai, etc.)

Currency conversions and global comparison dashboards

Use Cases for EV Price Trend Data

1. Dealership Pricing Intelligence

EV dealerships use Actowiz’s scraped data to compare pricing for similar models in competing cities or platforms. For example:

“A 2021 Hyundai Kona EV listed at ₹16.4L in Delhi and ₹15.2L in Pune. Should we adjust our pricing or offer financing support?”

With historical price charts, dealers align closer to buyer expectations and market trends.

2️. EV Startups & Mobility Platforms

Subscription and EV rental platforms often base their offerings on predicted resale value. Web scraping helps them identify:

Depreciation curves for used EVs

Price gaps between similar models

Inventory saturation in Tier-1 vs Tier-2 cities

3️. Investor & Market Research Firms

Scraped EV data offers insight into:

EV adoption rates per region

Demand shifts based on fuel type

Vehicle availability based on battery/range data

Incentive-driven price spikes or dips

4️. EV Resale & Exchange Platforms

EV marketplaces or comparison apps use scraped data for:

Real-time price benchmarking

Dynamic quote generation (buy/sell)

Inventory alerts for high-demand models

Sample Dataset (EV Listings)

Tata Nexon EV

Year: 2022

Price: ₹13,50,000

City: Mumbai

Range: 312 km

Mileage: 14,000 km

Platform: OLX Autos

Tesla Model 3

Year: 2021

Price: $34,200

City: San Diego

Range: 490 km

Mileage: 27,000 mi

Platform: Cars.com

MG ZS EV

Year: 2022

Price: ₹21,40,000

City: Bangalore

Range: 419 km

Mileage: 8,000 km

Platform: CarDekho

Hyundai Ioniq

Year: 2020

Price: £18,999

City: London

Range: 274 mi

Mileage: 21,000 mi

Platform: Autotrader UK

Insights Unlocked

Price Fluctuations

Understand how pricing varies daily or weekly per model and location.

Resale Value Curves

Track long-term depreciation by brand, battery age, or mileage.

Regional Heatmaps

Identify cities with high inventory or price gaps—great for dealerships.

Fuel Shift Analysis

Monitor adoption of fully electric vs hybrid vs plug-in hybrid cars.

Lead Scoring

Scrape listings with contact info and freshness to generate high-quality EV leads.

AI-Powered EV Price Prediction

With scraped data, Actowiz enables:

Machine Learning models for price estimation

Discount pattern detection over festive or incentive periods

EV resale trend forecasting using historical time series

Why Track EV Price Trends?

Whether you’re tracking BYD and Tesla in the U.S., Tata EVs in India, or Hyundai EVs in the UK, Actowiz supports:

Currency conversions (INR, USD, GBP, EUR)

Mileage unit handling (KM vs Miles)

Region-specific price benchmarks

Language/localization filters

Compliance & Ethical Web Scraping

Actowiz ensures:

Ethical scraping that respects rate limits and platform T&Cs

Data delivered for internal research, analytics, and optimization

GDPR-compliant, anonymized datasets

Option for API throttling to maintain platform stability

Output Formats Offered

JSON

CSV

Excel

REST API

Dashboard integration with Power BI, Tableau

Who Should Use EV Price Scraping?

Auto Dealerships ▸ Optimize pricing strategies ▸ Monitor competitor listings and trends

EV Startups ▸ Plan inventory and stock based on demand ▸ Predict ROI across locations and segments

Marketplaces (OLX, Cars.com, etc.) ▸ Benchmark vehicle listings ▸ Automate dynamic pricing or promotional offers

Investors ▸ Identify undervalued regions or EV models ▸ Evaluate resale potential and depreciation

Researchers ▸ Track EV adoption trends by city/state ▸ Correlate vehicle usage, pricing, and infrastructure

Governments & Policy Makers ▸ Analyze how subsidies or tax incentives affect pricing ▸ Evaluate EV affordability across regions

Expert Tip: Layer EV Price Trends with Policy Data

EV incentives (like India’s FAME II or the U.S. federal credit) dramatically impact pricing and resale. Combine scraped data with policy shifts for predictive accuracy in:

Buyer behavior

Seasonal pricing changes

Post-incentive depreciation

Conclusion

The EV revolution isn’t slowing down. But it’s those who understand the pricing patterns, resale behaviors, and listing shifts that will win the market.

With Actowiz Solutions, you're not just scraping listings—you're extracting electric vehicle market intelligence.

Don’t guess. Don’t generalize. Scrape, analyze, and optimize your EV strategy with real-time price data from the source.

Learn More >>

Originally published at https://www.actowizsolutions.com

[proxylustinc]

Why Speed and Location Matter in Proxy Performance by Proxy Lust, Inc.

When it comes to proxies, most people focus on IP type—residential, mobile, datacenter, or ISP. But two factors can make or break your proxy experience, regardless of the type: speed and location.

Whether you’re web scraping, managing multiple accounts, bypassing geo-restrictions, or automating social media, a fast proxy in the right location will determine your success.

In this blog post, Proxy Lust, Inc. breaks down why proxy speed and geographical location matter—and how to choose the right setup for your use case.

⚡ Speed: The Hidden Power Behind Every Proxy Task

What Affects Proxy Speed?

Network latency (distance from client to proxy and from proxy to target site)

Bandwidth capacity of the proxy server

Server load (shared vs. dedicated proxies)

Routing path from the proxy to the final destination

ISP or hosting infrastructure quality

If a proxy is slow, everything takes longer—from scraping 1,000 product listings to loading a social media feed or completing a transaction.

Why Speed Matters:

Use CaseWhy Speed is CriticalSneaker botsMilliseconds matter—slow proxies lose dropsWeb scrapingFaster requests = more data in less timeStreaming or geo-bypassReduced buffering and loading timesAd verificationView ads in real-time without delayRemote RDP/VPS tasksLess lag, smoother automation and monitoring

📍 Location: Where Your Proxy Lives Matters

The physical or network location of your proxy determines how websites perceive your traffic. A proxy in the wrong location may trigger:

Geo-blocks or region-specific CAPTCHAs

Incorrect content or pricing on localized sites

Higher latency when connecting to target servers

Why Proxy Location Matters:

Use CaseProxy Location NeededLocalized web scrapingCountry or city where data is targetedStreaming unblockingCountry where service is licensedeCommerce listing/postingRegion where platform accepts postsSEO testingLocation of the target audienceSocial media automationMatches the IP to the account’s geo history

🧠 Speed + Location: The Winning Combo

Choosing a proxy with great speed but in the wrong location won’t help. Likewise, a properly geolocated proxy that is overloaded or misconfigured is just as bad.

Key Tips:

Use geo-targeted residential or ISP proxies for location-sensitive tasks

Opt for dedicated or low-user-count proxies to maintain bandwidth

For latency-sensitive tasks (like botting or automation), pick proxies close to your target site’s data center

🚀 How Proxy Lust Optimizes Speed and Location

At Proxy Lust, Inc., our infrastructure is built to give you the best of both worlds:

✅ Dedicated ISP and mobile proxies with fast speeds and low latency

🌎 Coverage in key regions: U.S., UK, Canada, Germany, India, and more

💡 Custom geo-routing options available on request

📈 Load-balanced infrastructure to avoid bottlenecks

Whether you’re automating ad campaigns, scaling SEO tools, or running sneaker bots, our proxies are built for speed, stability, and stealth.

👉 Browse Proxy Plans by Speed & Location →

In 2025, raw IP type isn’t enough. To succeed in a competitive online environment, you need proxies that are fast, stable, and perfectly positioned.

Before buying your next proxy, ask yourself:

How close is this proxy to my target server?

Is this a shared or dedicated IP?

Will this IP appear natural in the context of my use case?

When speed and location align, your tools run better, your success rate improves, and your risk of bans drops dramatically.

Let Proxy Lust help you make the smart choice.