**Automating Review Sentiment Dashboards Across Amazon, Flipkart & Myntra
**
*Business Challenge
*
A leading omnichannel retail brand with 200+ SKUs across fashion and electronics platforms faced this recurring issue:
“We manually check Amazon and Flipkart reviews every week, but it’s too slow to act on.”
The brand’s product and marketing teams lacked:
A centralized review sentiment view
A way to track sentiment shifts daily
Live keyword monitoring across categories
They partnered with Datazivot to build a fully automated, cross-platform sentiment dashboard updated in near real-time.
Objectives
Scrape and process daily Reviews from Amazon, Flipkart, and Myntra
Run sentiment analysis and keyword extraction for all SKUs
Build an automated dashboard by product, brand, and platform
Provide alerting for negative sentiment spikes or trending complaints
Our Approach
Real-Time Review Scraping Infrastructure
We deployed dedicated scrapers and rotating proxy pools to extract review data every 12 hours.
Fields Captured:
SKU & Product Name
Review Title & Body
Star Rating
Platform
Timestamp
Sentiment Score
Feature Mentions (e.g., battery, fit, color, packaging)
Platforms Integrated:
Amazon.in
Flipkart.com
Myntra.comSentiment + Keyword Pipeline
Used BERT and RoBERTa models for sentiment tagging.
Built keyword classification based on category:
Fashion: fabric, stitching, fit, design, delivery
Electronics: battery, audio, UI, packaging, build quality
We added time-series analysis to detect rising complaint clusters (e.g., “battery drains fast,” “size mismatch”).
Note: SKUs with negative sentiment >25% are auto-flagged for weekly review
- Dashboard Architecture Backend: Python ETL scripts (scheduled via Airflow), AWS Lambda Database: Google BigQuery for scalable review storage Frontend: Google Data Studio + Power BI (client-selected) Alert System: Slack + Email notifications when negative mentions spike
Key Features of the Dashboard
SKU-Level View
Avg rating, review count, sentiment breakdown (last 7, 30, 90 days)
Keyword cloud with volume and polarity
Trend Tracker
Daily sentiment shifts
Top emerging positive/negative keywords
Spike alerts for product managers
Category Comparisons
Which category (e.g., shirts, mobiles, shoes) has the best customer sentiment?
Identify gaps vs competitors (integrated competitor tracking in Phase 2)
Automated Weekly Summary Report
Sent every Monday morning to product and marketing heads
Outcomes & Impact
Reduced Review Monitoring Time
Manual review checks that took 6–8 hours/week were replaced with auto-generated dashboards.
Marketing and product teams could focus on acting, not aggregating.Faster Sentiment Response
Negative spikes now detected within 12–24 hours of trend onset.
Example: A “loose stitching” issue in a Myntra-exclusive SKU was caught in 36 hours, preventing 300+ potential returns.Marketing Campaign Alignment
Positive keyword trends like “comfortable fit,” “premium look,” and “fast delivery” were integrated into ad creatives and influencer briefs.
Click-through rates increased by 18% on campaigns using sentiment-derived messaging.
Strategic Value Delivered
Fully automated sentiment feedback loop
Real-time product insight engine
Actionable voice-of-customer (VoC) monitoring
Alerts for reputation risk management
Rather than relying on anecdotal feedback or outdated monthly summaries, the client now had a data-driven radar across all major platforms.
Conclusion
With a live dashboard powered by Datazivot, the brand moved from reactive review reading to proactive sentiment-led decision-making.
No more scattered spreadsheets or delayed decisions — just a single screen showing exactly what their customers felt, platform-by-platform, product-by-product.
Want your review data to work while you sleep?
Datazivot builds automated sentiment dashboards across Amazon, Flipkart, and Myntra—so you act on customer feedback faster than ever.
Originally Published By https://www.datazivot.com/automated-review-sentiment-dashboards-amazon-flipkart-myntra.php
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