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

1. 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.com

2. Sentiment + 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”).

Sample Dashboard Snippet (Live Data Format)

Note: SKUs with negative sentiment >25% are auto-flagged for weekly review

3. 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

1. 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.

2. 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.

3. 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.

Stack Snapshot

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.