Enhance Influencer Campaigns Using E-Commerce Review Sentiment Data

Shaping Influencer Campaigns Using Sentiment from E-Commerce Reviews
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**Business Challenge

A fast-scaling D2C beauty and fashion brand spent lakhs on influencer campaigns—but wasn't getting expected ROI.

Their pain points:

Influencer content was misaligned with what customers really valued.
Negative reviews surfaced shortly after campaigns launched.
Product features promoted by influencers often mismatched customer experience.
“We need to stop pushing talking points and start amplifying what real users actually say.”

The brand partnered with Datazivot for E-Commerce Reviews Scraping to analyze customer feedback from Amazon, Flipkart, and Myntra. The insights were used to craft a targeted, data-driven influencer strategy aligned with real consumer sentiments.

Objectives

Extract real customer sentiment and top-praised product features.
Identify key themes and phrases used in positive reviews.
Craft influencer briefs using voice-of-customer insights.
Flag potential product issues before campaign amplification.
Track sentiment shifts before and after campaign drops.

Our Approach

1. Review Sentiment Mining
   We scraped 100K+ reviews across:
   Amazon – Lipsticks, foundation, skincare combos
   Myntra – Dresses, tops, shoes
   Flipkart – Grooming kits, accessories
   Captured:
   Product name, platform, rating
   Review text, timestamp
   Sentiment score
   Keywords and recurring themes

2. Review Language → Influencer Copy Mapping
   From highly positive reviews, we extracted:
   Frequently used adjectives: “lightweight,” “non-sticky,” “perfect shade”
   Common benefits: “lasts all day,” “blends well,” “true to color”
   Emotional cues: “felt confident,” “finally found my shade”
   These became the foundation for influencer talking points.

3. Issue Detection Before Campaigns
   We ran sentiment alerts on target SKUs before campaign launch:
   Negative review volume
   Keywords tied to dissatisfaction
   Pre-launch return patterns
   Campaigns were paused or messaging adjusted accordingly.

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Campaign Performance Boost

1. Higher Engagement & Authenticity Influencers mirrored exact phrases from reviews: Feels weightless even in humidity” “Finally a kurta that fits perfectly from shoulder to waist” Followers commented: “Same here!” / “That’s exactly what I thought!”

Result:
Engagement up by 34%
CTR improved 21%
Return rate dropped 17% on featured SKUs

1. Pre-Launch Review Pulse Avoided Backlash
   One serum had rising complaints (“smells weird,” “sticky texture”) on Amazon.
   Sentiment score dropped to 59%.
   Datazivot flagged it, and the influencer campaign was delayed.
   Brand reformulated fragrance and relaunched 5 weeks later—positive sentiment rebounded to 80%.
   Saved ₹5.2 lakh in potential influencer spend waste.

2. Localized Messaging from Review Regions
   Using reviewer locations and language style:
   South India: Emphasized “no white cast” in sunscreen
   West India: Highlighted “festive look” in fashion apparel
   Hindi-speaking regions: Used translated phrases from positive reviews in reels Regional influencer briefs matched review sentiment — improving local conversions by 26%.

Visual: Sentiment Summary Dashboard (Example)

Stack Used

Impact Summary

Influencer messaging 100% aligned with real user voice
Campaign prep time cut by 30% via auto-generated review briefs
Avoided ₹10L+ loss from misaligned influencer campaigns
Boosted engagement, lowered returns, improved sentiment

Conclusion
Influencer campaigns shouldn’t be guesswork.
With Datazivot’s sentiment-backed content planning, brands tap into what real users already love—and avoid amplifying what they don’t.
Because the best influencer copy isn’t written in a meeting room. It’s already in your product reviews.

Source : https://www.datazivot.com/influencer-campaigns-sentiment-scrape-ecommerce-reviews-data.php