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

1. 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

2. 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

3. 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

1. 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.

2. 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.

3. 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

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.