Articles about Technology, Gadget and Science

Iqiyi Data Scraping Api — Real-time Global Catalog, Top 10 & Regional Availability Data    By: REAL DATA API
Pull live iQIYI content catalog, per-country regional availability, Top 10 trending charts, bundled Channels, and leaving-soon expiration data at scale across every iQIYI market worldwide. 99.9% uptime, sub-3s response times.(read entire article)(posted on: 2026-10-14)
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Google Maps Review Scraping For Multi-location Retail Brands    By: iwebdatascraping
Google Maps Review Scraping for Multi-Location Retail Brands Google Maps reviews reveal what customers think about individual stores, from slow billing and poor product availability to helpful staff and excellent service. For brands operating hundreds of locations, manually monitoring thousands of reviews is difficult. Google Maps review scraping transforms public store reviews into structured data for location-level reputation monitoring, customer experience analysis, and operational improvements. In September 2026, an AI analytics company requested Google Maps reviews across Indian retail stores, while a strategy consultancy sought city-wise outlet counts and rating distributions. Both requirements highlight the growing need for scalable review intelligence. Why Store-Level Reviews Matter Footfall: Customers compare ratings before visiting stores. Local SEO: Ratings, review counts, and recency can influence local visibility. Operational insights: Reviews reveal billing delays, staffing issues, and stock shortages. Competitive benchmarking: Compare nearby stores to identify experience gaps. What Data Can Be Collected? Store Data: Place name, address, coordinates, category, overall rating, review count, and opening hours. Review Data: Star rating, review text, date, language, owner response, response date, and photo count. Aggregated Metrics: Rating distribution, review velocity, sentiment, and recurring complaint themes. iWeb Data Scraping focuses on public store and review content, excluding or anonymising reviewer identities by default. How Google Maps Review Scraping Works Step 1: Build the Store List Match store codes, addresses, and coordinates with Google Maps listings. Flag duplicate, outdated, or unmatched locations. Step 2: Collect Store Metrics Capture ratings, review counts, and star distributions regularly to track reputation changes. Step 3: Extract Reviews Collect available review history initially, then retrieve new reviews during subsequent updates. Step 4: Apply AI Analysis Classify reviews into themes such as billing speed, staff behaviour, pricing, cleanliness, product availability, returns, and parking. Sentiment analysis supports multilingual reviews written in English, Hindi, and regional languages. Step 5: Deliver Dashboards and Alerts Provide recurring data feeds, city-level dashboards, store rankings, and alerts for sudden rating declines. Sample Data and City-Level Insights The following figures are illustrative. A sample record may include a fashion store in Bengaluru with a 4.1 rating, 2,874 total reviews, and a recent two-star review mentioning a 25-minute billing queue. AI analysis identifies billing speed and staffing as key complaint themes. Illustrative city-level findings: Bengaluru: 42 stores, average rating 4.2, and 14% negative reviews. Billing speed is the leading complaint. Mumbai: 51 stores, average rating 4.0, and 18% negative reviews. Trial rooms are the top concern. Delhi NCR: 58 stores, average rating 3.9, and 21% negative reviews. Staff behaviour is the leading issue. Hyderabad: 27 stores, average rating 4.3, and 11% negative reviews. Parking is the main complaint. These examples demonstrate how brands can compare customer experiences across locations. Turning Reviews Into Action Rank stores by risk: Prioritise locations experiencing sudden rating declines. Resolve recurring issues: Address billing queues, staffing gaps, and availability problems. Track response rates: Monitor how consistently stores respond to customer feedback. Recognise positive experiences: Identify reviews highlighting excellent service. Benchmark competitors: Compare nearby stores to understand local performance gaps. Review Velocity and Reputation Dashboards Review velocity measures how quickly new reviews appear. Sudden increases combined with negative sentiment can signal service problems, incidents, promotions, or reopening-related activity. Dashboards can provide: Executives: Brand-wide and regional reputation summaries. Regional managers: Store rankings and locations requiring attention. Store managers: Recent reviews and actionable feedback. Operations teams: Trends in billing speed, cleanliness, and availability. Marketing teams: Review response tracking and reputation monitoring. How AI Improves Review Analysis AI models identify themes and sentiment across different languages, spelling variations, and informal expressions. For example, a review stating “bahut bheed thi, billing me 20 min laga” can be classified under crowding and billing speed. Automated summaries help managers understand recurring problems without manually reading every review. Industries That Benefit Fashion: Trial rooms, size availability, and returns. Electronics: Staff knowledge, pricing, and service delays. Supermarkets: Freshness, stock availability, and checkout speed.(read entire article)(posted on: 2026-10-14)
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Mango Tv Data Scraping Api — Real-time Global Catalog, Top 10 & Regional Availability Data    By: REAL DATA API
Pull live Mango TV content catalog, per-country regional availability, Top 10 trending charts, bundled Channels, and leaving-soon expiration data at scale across every Mango TV market worldwide. 99.9% uptime, sub-3s response times.(read entire article)(posted on: 2026-10-14)
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9 Benefits Of Hiring A Cio To Manage Your Enterprise Business    By: brainbell10
Fast-paced and technology-driven business Sataware landscape, the role of Byteahead a Chief Information Officer web development company (CIO) has become increasingly app developers near me crucial.(read entire article)(posted on: 2026-10-14)
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Automated Rera Data Collection For Real Estate Risk    By: iwebdatascraping
Automated RERA Data Collection for Real Estate Risk and Lending Intelligence Through Project Monitoring and Developer Performance Analytics(read entire article)(posted on: 2026-10-14)
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Jd Edwards Erp Security Best Practices & Compliance Guide    By: Saamsha
Strengthen your JD Edwards ERP security with role-based access, closed security models, auditing, data protection, and expert best practices for enterprises.(read entire article)(posted on: 2026-10-14)
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Moove Us Used City-level Ride-hailing Fare Data Scraping    By: iwebdatascraping
Moove US Used City-Level Ride-Hailing Fare Data Scraping for Advanced Mobility Pricing, Competitive Benchmarking, and Urban Transportation Intelligence(read entire article)(posted on: 2026-10-14)
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How Outsourced Soc Services Help Businesses    By: Devendra singh
NG Cloud Security helps businesses explore cybersecurity solutions that align with their infrastructure, security priorities, and operational goals. With the right provider, organizations can strengthen incident response, improve security monitoring, and focus on their core business activities while building a more resilient cybersecurity strategy.(read entire article)(posted on: 2026-10-14)
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Protecting Electronics From The Ground Up: Staticstop At Pcb Carolina 2026    By: Steven Dubin
StaticStop will exhibit at PCB Carolina 2026 on Wednesday, Nov. 11, at the McKimmon Center at NC State University in Raleigh, North Carolina.(read entire article)(posted on: 2026-10-14)
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Myntra Review Scraping: Review Trend Tracking For A D2c Label    By: iwebdatascraping
Review Trend Tracking on Myntra for a D2C Apparel Label: A Myntra Review Scraping Case Study Myntra review scraping helped a D2C apparel label track customer sentiment by product and month, find fit and fabric issues and cut size-related returns. --- The challenge The client is a fast-growing D2C label that sells most of its volume through Myntra. Its team asked iWeb Data Scraping a simple question: "We need our brand's customer reviews and how they are shaping over time." Ratings had slipped slightly over two seasons, returns were rising, and the team could not tell whether the cause was fit, fabric, colour accuracy or delivery. Reading reviews manually on the marketplace was slow, and the seller dashboard did not show trends by product, theme or month. Volume. The brand had more than 1,200 live styles with thousands of reviews between them. Short, mixed-language text. Many reviews were brief ("size runs small", "colour different") or written in Hinglish. Linking reviews to product attributes. To act, the team needed to know which fabrics, fits and categories drew complaints. Competitor context. A 4.0 rating means little without knowing how comparable brands perform in the same category. What we built Step 1: Product list and attributes iWeb Data Scraping collected all live styles for the brand and five competitors, with category, fabric, fit, colour, price and size range. Step 2: Review collection For each style, collectors captured public star ratings, review text, review date, size purchased where shown, and helpful votes. Reviewer names were excluded. Step 3: Theme and sentiment tagging Each review was tagged with themes using an AI classifier trained on fashion vocabulary, including Hinglish: fit (too small, too large, length), fabric (thin, see-through, rough, shrinkage), colour (different from image, fading), stitching and finish, comfort, value for money, and delivery or packaging. Step 4: Trend tables Ratings and theme shares were calculated by month, category, fabric and fit, for the brand and for each competitor. Step 5: Alerts Any style whose rolling 30-day rating fell below 3.8, or where "size runs small" exceeded 25% of reviews, triggered an alert to the product team. Results Reviews analysed: 48,000 in the first run, providing a broad customer-feedback dataset. Styles flagged for fit issues: 37 styles identified for potential improvements. Size-related feedback: "Size runs small" mentions in women's dresses decreased from 22% to 9% after the size-chart fix. Average rating improvement: Fixed styles gained +0.3 stars over three months. The biggest finding was that one rayon dress block ran small across 37 styles. The team updated its size chart, added a "size up" note on product pages, and corrected the pattern for the next production run. Returns linked to fit fell noticeably in the following season. A second finding, that two colours looked brighter in photos than in reality, led to a change in product photography. How it's delivered The record below is illustrative. { "brand": "Example Label", "style_id": "MY-2938471", "category": "Women > Dresses", "fabric": "Rayon", "fit": "A-line", "review_date": "2026-09-18", "stars": 3, "size_purchased": "M", "review_text": "Design is nice but size runs small, had to return and order L", "themes": ["fit_too_small"], "sentiment": -0.3, "helpful_votes": 12 } Women's dresses: Brand rating is 3.9 vs. competitor median 4.1. The top complaint is fit too small, mentioned in 22% of reviews. Women's tops: Brand rating is 4.2 vs. competitor median 4.1. The main complaint is colour different, appearing in 9% of reviews. Men's shirts: Brand rating is 4.0, matching the competitor median. The primary complaint is fabric thin, mentioned in 11% of reviews. Men's joggers: Brand rating is 4.3 vs. competitor median 4.0. The main complaint is length too short, appearing in 6% of reviews. How the product team uses the data each week Every Monday, the product team receives a short report with four parts: styles that crossed an alert threshold, the top three complaint themes by category, new themes that appeared for the first time, and positive themes worth using in marketing copy (for example, "soft fabric" and "true to colour"). The design team uses the fit data when grading new patterns, and the photography team checks colour complaints before each shoot. Customer support uses the same theme tags to prepare answers to common questions on product pages. The outcome Reviews linked to attributes showed patterns no single review could reveal. Hinglish-aware tagging captured complaints that keyword searches would miss.(read entire article)(posted on: 2026-10-14)
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