*Using Location-Based Review Analysis for Regional Product Strategy
*
Business Challenge
A nationwide consumer brand was seeing mixed sales performance across different states.
They struggled to understand:
Why certain products performed better in Bangalore vs Lucknow
Why return rates were higher in Hyderabad than in Delhi
What regional preferences existed across platforms like Amazon, Flipkart, and Myntra
“We’re selling the same SKU in every zone — but customers are reacting differently. We need data to localize better.”
They partnered with Datazivot to analyze location-tagged reviews and create a regional product strategy backed by sentiment and behavioral insights.
Objectives
Scrape reviews from Amazon, Flipkart, and Myntra with geo-tags (explicit or inferred).
Map sentiment trends by city/state.
Identify regional preferences and pain points for products.
Help the brand localize campaigns, inventories, and messaging.
Our Approach
- Location-Aware Review Scraping We extracted:
Explicit city mentions in reviewer profiles (Amazon/Flipkart)
Indirect location clues (language, delivery references: "Bangalore weather," "Ahmedabad store")
Platform-level region filters for category listings
Platforms covered:
Amazon.in – Electronics & personal care
Flipkart – Fashion, FMCG, phones
Myntra – Apparel and footwear
Review dataset: 450K+Review Datasets, 120+ cities covered
- City-Wise Sentiment & Keyword Analysis We broke down reviews by:
Region → City → Pin-code clusters
Product category → Subcategory
Sentiment distribution (positive, neutral, negative)
Keyword trends by region
Example mapping:
“Sweatproof,” “slim-fit,” “bright color,” “festive” reviews in South India
“Warm fabric,” “true size,” “pale tone” in North India winters
Sample Regional Insights Table
- Geo-Sentiment Dashboards We developed city-wise dashboards with:
Top SKUs and review performance per region
Keyword cloud and pain point mapping
Negative review heatmap by pin-code zone
Comparison across platforms (e.g., Amazon in Chennai vs Flipkart in Chennai)
Results & Strategic Actions
Localized Inventory Allocation
Bangalore, Chennai, Hyderabad preferred lighter fabrics and breathable designs.
Stocking of heavy-knit ethnic wear reduced in South India during Q3, saving ~₹14 lakh in overstock costs.
Reviews told the brand what wasn’t working in each region—before sales drop made it obvious.Hyperlocal Campaign Personalization
Flipkart reviews in Indore and Jaipur showed strong sentiment for “festive look,” “vibrant colors,” and “traditional embroidery.”
Ads for those zones began using regional influencers and ethnic hooks.
Result: 22% CTR increase and 15% conversion lift in Tier-2 festive season campaigns.Return Rate Reduction
In Hyderabad, 1 in 4 negative reviews for shoes involved “tight fit.”
Sentiment dashboard flagged this in real-time, leading to immediate fit chart customization and additional size availability in the region.
Return rates in Hyderabad dropped from 21% → 13% in 45 days.
Visual: Geo-Sentiment Heatmap (Sneakers Category)
Stack Used
Tool Use Case
Scrapy + Proxies Geo-tagged review scraping
NLTK + spaCy Location/entity extraction
BERT Review sentiment classification
GeoPandas + Mapbox City/pincode-level heatmaps
Power BI Interactive dashboard for brand teams
*Strategic Takeaways
*
Regional review analysis gave brand teams city-specific clarity.
Campaigns could now match cultural preferences and pain points.
Inventory was better optimized, reducing reverse logistics costs.
Marketing & supply chain teams finally had shared real-time location insights.
Conclusion
Datazivot helped transform regional guesswork into data-driven product and campaign decisions.
Location isn’t just a shipping detail — it’s a powerful signal of what your customer values.
Originally Published By https://www.datazivot.com/location-based-review-analysis-regional-product-strategy.php
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