Automated Sentiment Dashboards, Customer Feedback Dashboard, Amazon Sentiment Data Dashboard, Flipka

Automating Review Sentiment Dashboards Across Amazon, Flipkart & MyntraBusiness 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 Approach1. 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)

Platform

Avg Rating

Pos%

Neg%

Top Keywords

Flag

Amazon

4.2

76%

12%

"battery, sound"

• p>

Flipkart

3.6

52%

29%

"heating, fake"

• p>

Myntra

4.5

83%

8%

"fit, cotton"

• p>

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 DashboardSKU-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 & Impact1. 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

Tool

Purpose

Python

Scraping, ETL pipelines

HuggingFace

Sentiment & keyword models

BigQuery

Centralized storage of review data

Power BI

Live dashboards

Slack/Email

Alerts & summaries to stakeholders

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.

ConclusionWith 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

Https://www.datazivot.com/extract-e-commerce-websites-reviews-data.php