Myntra Review Scraping: Review Trend Tracking For A D2c Label

By Author: iwebdatascraping
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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.

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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.