Predict Product Returns Via E-commerce Review Insights

By Author: DataZivot
Total Articles: 74
Comment this article

Predicting Product Returns with E-Commerce Review Data

Business Challenge

One of India’s leading multi-category e-commerce sellers faced a costly issue:

“25% of certain SKUs were being returned within 10 days, but we couldn’t predict why in time to react.”

Despite excellent product specs and competitive pricing, return rates for fashion and electronics products remained unpredictably high.

The seller turned to Datazivot for E-Commerce Reviews Scraping to uncover whether customer reviews on Amazon, Flipkart, and Myntra could help predict returns and reduce losses.

Objectives

Datazivot was tasked with:

Scraping and analyzing reviews tied to high-return SKUs across categories.
Correlating negative sentiment, review keywords, and review timing with product return data.
Creating a return risk scoring model to flag at-risk SKUs in real-time.
Our Approach

1. Data Ingestion: Review & Returns
We integrated two data streams:

Public reviews from Amazon, Flipkart, and Myntra (star rating, title, review text, review date).
Client-provided ...
... SKU-level return data over 6 months (return reason, return request date, geography, product category).
Total Data Points:

1.2M+ Reviews DataSets
180K Return Records
2. Review Sentiment + Keyword Mapping
We used sentiment analysis (VADER + BERT) and keyword extraction to find patterns:

Negative and neutral reviews within 7 days of delivery
Keywords tied to dissatisfaction and likely return (e.g., “loose,” “damaged,” “fake,” “heating,” “not as shown”)
We found strong correlations between early review tone and subsequent return behavior.

Sample Data Snapshot

Key Insights & Outcomes

1. Keyword-Based Return Predictor
Using machine learning (Random Forest + Logistic Regression), we created a return likelihood model with 82% accuracy.

Top predictive keywords:

Fashion: “tight,” “too short,” “see-through,” “not as shown”
Electronics: “heating,” “doesn’t connect,” “fake,” “damaged”
Each SKU was scored 0–100 based on return risk.

2. Time-to-Return Prediction
We found that:

76% of high-return reviews were posted within 72 hours of delivery.
Products with >15% of negative reviews in the first 5 days had 2.3x higher return rates.
By acting on early review signals, brands could pull SKUs from promotions before damage escalated.

3. Platform-Specific Trends
Flipkart buyers were more vocal about delivery/packaging issues.
Amazon users flagged performance and authenticity.
Myntra users focused on fit, size, and fabric quality.
Platform-aware return predictors were added to the model.

Business Results

Reduced Return Rates
For flagged high-risk SKUs, the seller took preemptive actions:

Adjusted product images and size charts
Improved packaging for fragile SKUs
Ran A/B tests on reworded descriptions
Result: Return rates dropped by 21% in 60 days for flagged items.

Real-Time SKU Watchlist
Datazivot delivered a dashboard with a “Return Risk” meter, auto-updated daily using live reviews.

Impact on Revenue & Ops
25% fewer reverse logistics cases
30% more accurate stock reorder cycles
Improved product Q&A based on review themes

Tools & Stack

Scraping: Scrapy, BeautifulSoup, Selenium
NLP & Modeling: spaCy, BERT, scikit-learn, TensorFlow
Data Integration: AWS Glue, Google BigQuery
Dashboard: Power BI + Custom Python-based alerts

Strategic Impact

By using reviews as an early warning system, Datazivot helped the client move from reactive return handling to proactive SKU management.

Instead of waiting for returns to hurt profit, they flagged issues based on what customers were writing—often before returns happened.

Are return rates silently draining your profits?

Let Datazivot decode the warning signs hidden in your reviews—before the refunds roll in.

Source : https://www.datazivot.com/predict-product-returns-ecommerce-review-data.php