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Project Overview
Did any online review confuse you? That’s what we encountered on Amazon to identify as a fake review. These reviews mislead buyers, damage seller reputations, and distort product rankings. It’s a serious threat to sellers relying on authentic customer feedback.
Our client, a mid-sized Amazon seller with listings across multiple categories, noticed a sudden drop in product credibility. Their listings were receiving suspicious reviews that didn’t match actual customer experiences. Some reviews were irrelevant, while others promoted unrelated products. The client wanted to comprehend the problem, put a full stop to the damage and prevent future attacks.
We took on the challenge with a clear goal: identify fake reviews, analyse manipulation prototypes, and build a scalable system for reporting and prevention.
The Challenges
The client faced several interconnected problems:
- Suspicious Review Volume: A sharp increase in 1-star reviews with vague or irrelevant content. Many reviews appeared automated or copied from other listings.
- Manual Detection Limitations: The client’s internal team was unable to keep up the pace. Identifying coordinated attacks manually was slow and often inaccurate.
- Lack of Structured Evidence: Even when fake reviews were spotted, there was no organised data to present to Amazon for escalation.
- Hidden or Missing Reviews: Some reviews were visible only in certain regions or devices. Others disappeared after being flagged, making it hard to track patterns.
- Scalability Issues: The problem wasn’t limited to one product. Multiple listings across categories were affected, raising a doubt about a competitor’s involvement.
It was evident that the client needed a robust, data-driven approach to detect and respond to review manipulation.
Our Approach
We broke the solution into five key components:
Review Pattern Analysis
We started by analysing 1-star reviews. Many had repeated phrases, poor grammar, or promoted adult products. These weren’t relevant to the client’s listings. These reviews were likely generated by bots or coordinated groups.
We looked for patterns in timing, language, and reviewer behaviour. Reviews that appeared in bursts, used similar wording, or came from accounts with no purchase history were flagged.
Reviewer Profiling
Next, we studied the reviewer accounts. Some exhibited bot-like activity—posting multiple reviews in a short time, across unrelated categories. Others had conflicting review histories, praising one product while criticising a similar one from the client.
We also found profiles linked to competitor products. These reviewers often left negative feedback on the client’s listings and positive reviews on rival items. We flagged these accounts and documented their activity.
Impact Mapping
We tracked how fake reviews affected the client’s listings. Products with high volumes of suspicious reviews ended in poor visibility, conversion rates, and even lost Amazon badges like “Best Seller.”
We mapped these impacts across categories to understand how manipulation spread. This helped us identify which products were most vulnerable and which competitors were likely involved.
Structured Reporting
To support escalation, we built a detailed dataset. It included:
- Reviewer metadata (account age, review history, purchase status)
- Behavioural flags (repetition, timing, sentiment anomalies)
- Product segmentation (affected listings, categories, and review clusters)
- Hyperlinked references to each flagged review and profile
We created a clear and organised report that Amazon’s support team could easily review. It helped the client raise complaints and request the removal of fake reviews.
Automation Exploration
Finally, we proposed an AI-based detection framework. This system would:
- Analyse keywords and sentiment in reviews
- Detect overlaps in reviewer behaviour across listings
- Identify hidden or region-specific reviews
- Send real-time alerts if suspicious patterns appear
- This blueprint laid the foundation for future automation, making review monitoring faster and more reliable.
What Made Our Solution Different
Our approach stood out for four key reasons:
The Results We Achieved
The impact was clear and measurable:
- The client could flag and report fake reviews with confident.
- Amazon accepted the escalation and began investigating the flagged profiles
- Product credibility improved, and buyer trust returned
- Revenue loss from manipulated listings was reduced
- The client gained insights into competitor tactics and targeting patterns
- Reviewing reliability scores helped guide catalogue decisions and future product launches
These results proved that structured analysis and smart reporting could transform a complex problem into a manageable process.
Key Benefits & Impact
- Detects fake reviews and suspicious reviewer behaviour.
- Spots harmful content patterns across product listings.
- Helps sellers report fake reviews to Amazon with solid evidence.
- Builds a system to monitor future review attacks.
- Protects brand reputation from coordinated review manipulation.
- Reduces revenue loss caused by misleading reviews.
- Improves buyer trust by removing harmful content early.
- Reveals competitor tactics and targeting strategies.
- Supports smarter catalogue decisions using review reliability scores.
- Prepares for AI tools that detect fake reviews in real time.
Use Cases Across Industries & Domains
This solution works beyond Amazon sellers. It helps many types of businesses:
E-commerce Brands
- Protect product listings from fake reviews.
- Improve customer trust and sales performance.
Hospitality & Travel
- Detect fake hotel or tour reviews.
- Maintain reputation across booking platforms.
App Developers
- Spot fake ratings and reviews on app stores.
- Prevent manipulation of app rankings.
Healthcare Providers
- Monitor patient feedback for authenticity.
- Avoid reputational damage from false reviews.
Online Education Platforms
- Ensure course reviews are genuine.
- Build trust with students and educators.
SaaS & B2B Services
- Track client feedback across review sites.
- Use insights to improve service quality and retention.
Business Owners
- Understand how competitors use fake reviews.
- Make informed decisions using review data.
Final Delivery & Next Steps
We delivered two key assets:
- A Google Sheet with reviewer profiles, flagged reviews, and behavioural insights
- A PDF report summarising findings, patterns, and escalation-ready documentation
The client is now equipped to take action, either by escalating to Amazon or making internal decisions about product listings.
To Sum Up
This project highlights the power of structured review analysis. Fake reviews are more than dangerous. They damage brand credibility and customer trust. By combining manual insights with automation planning, we helped the client uncover manipulation, protect their listings, and prepare for future challenges.
Relu’s Promise: We don’t just solve problems! We build systems that protect your brand, scale with your growth, and earn customer trust.
