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.

Sample Insights for Influencer Brief

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

2. 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.

3. 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.