--- title: "Perves - Business Analyst at BGR Review" description: "Business analyst. 10+ years modelling ranking systems and consumer trust signals inside major marketplaces. Owns BGR Review's removal eligibility scoring and revenue-lift models." lang: en json-ld: | [ { "@context": "https://schema.org", "@type": "Organization", "name": "BGR Review", "alternateName": "BGR REVIEW", "url": "https://bgrreview.com", "description": "BGR Review helps businesses grow their online reputation with real, human-posted reviews on Google, Yelp, Clutch and TripAdvisor, and removes negative reviews on a pay-after-success model.", "areaServed": "Worldwide", "serviceType": "Online reputation management" }, { "@context": "https://schema.org", "@type": "WebSite", "name": "BGR Review", "url": "https://bgrreview.com", "potentialAction": { "@type": "SearchAction", "target": "https://bgrreview.com/?q={search_term_string}", "query-input": "required name=search_term_string" } }, { "@context": "https://schema.org", "@type": "Person", "name": "Perves", "jobTitle": "Business Analyst", "email": "mailto:perves@bgrreview.com", "image": "https://bgrreview.com/assets/team-perves-bjl_rX_f.jpg", "url": "https://bgrreview.com/team/perves", "description": "Business analyst. 10+ years modelling ranking systems and consumer trust signals inside major marketplaces. Owns BGR Review's removal eligibility scoring and revenue-lift models.", "knowsAbout": [ "Reputation analytics & modelling", "Removal eligibility scoring", "Review velocity & anomaly detection", "Revenue-lift & ROI modelling", "Executive & board reporting", "Marketplace trust systems" ], "knowsLanguage": [ "English", "Urdu", "Punjabi" ], "worksFor": { "@type": "Organization", "name": "BGR Review", "url": "https://bgrreview.com" }, "address": { "@type": "PostalAddress", "addressLocality": "Thornhill, Canada" }, "alumniOf": [ { "@type": "EducationalOrganization", "name": "University of Toronto" }, { "@type": "EducationalOrganization", "name": "Rotman School of Management (Executive)" } ], "hasCredential": [ { "@type": "EducationalOccupationalCredential", "name": "Google Data Analytics Professional Certificate", "credentialCategory": "certification", "recognizedBy": { "@type": "Organization", "name": "Google (Coursera)" } }, { "@type": "EducationalOccupationalCredential", "name": "Microsoft Certified: Data Analyst Associate (Power BI)", "credentialCategory": "certification", "recognizedBy": { "@type": "Organization", "name": "Microsoft" } }, { "@type": "EducationalOccupationalCredential", "name": "Tableau Desktop Specialist", "credentialCategory": "certification", "recognizedBy": { "@type": "Organization", "name": "Tableau" } }, { "@type": "EducationalOccupationalCredential", "name": "AWS Certified Cloud Practitioner", "credentialCategory": "certification", "recognizedBy": { "@type": "Organization", "name": "Amazon Web Services" } } ] }, { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "How accurate is your removal eligibility model?", "acceptedAnswer": { "@type": "Answer", "text": "94% score-to-outcome accuracy across our closed case history, backtested weekly. If a case scores below our threshold, we tell the client before we take the work - we don't burn a retainer on a low-probability request." } }, { "@type": "Question", "name": "How do you calculate revenue lift from a rating improvement?", "acceptedAnswer": { "@type": "Answer", "text": "It's a model, not a promise. We combine your live conversion data, your local demand signal, and the observed click-through impact of rating changes in your category. Every forecast ships with the assumptions and a confidence interval so a CFO can pressure-test it." } }, { "@type": "Question", "name": "Can you spot a coordinated review attack early?", "acceptedAnswer": { "@type": "Answer", "text": "Usually, yes. Coordinated attacks leave anomaly patterns in review velocity, reviewer profile clustering and content similarity that a well-trained model catches inside 24-48 hours. We alert the client the same day we see it." } } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://bgrreview.com" }, { "@type": "ListItem", "position": 2, "name": "About Us", "item": "https://bgrreview.com/about" }, { "@type": "ListItem", "position": 3, "name": "Perves", "item": "https://bgrreview.com/team/perves" } ] } ] --- [![BGR REVIEW](/__l5e/assets-v1/87d66153-cb74-4e78-b954-d1aa9e9b3f70/bgr-logo.png)BGR REVIEW ](/) Get Reviews Get Reviews - [G Google Reviews Buy real Google reviews from aged, verified profiles. ](/buy-google-reviews) - [Y Yelp Reviews Buy Yelp Elite-friendly reviews with real photos and location tags. ](/buy-yelp-reviews) - [C Clutch Reviews Buy verified Clutch reviews for agencies and B2B service firms. ](/buy-clutch-reviews) - [T TripAdvisor Reviews Buy TripAdvisor reviews from verified traveller accounts. ](/buy-tripadvisor-reviews) Remove Negative Reviews Remove Negative Reviews - [× Remove Negative Google Reviews Pay only after removal - $449 per successfully removed Google review. ](/remove-negative-google-reviews) [Insight](/insights)[Research](/methodology)[About Us](/about)[Contact Us](/contact) [Login](/auth?mode=signin)[Sign up](/auth?mode=signup) [Home](/)/ [About Us](/about)/ Perves ![Portrait of Perves, Business Analyst at BGR Review](/assets/team-perves-bjl_rX_f.jpg) Experience 10+ years Business Analyst # Perves Business analyst. 10+ years modelling ranking systems and consumer trust signals inside major marketplaces. Owns BGR Review's removal eligibility scoring and revenue-lift models. Thornhill, Canada 10+ years experience English, Urdu, Punjabi [perves@bgrreview.com](mailto:perves@bgrreview.com) removal eligibility accuracy 94% Score-to-outcome accuracy of the removal eligibility model, measured across multi-jurisdiction case history. marketplace analytics 10+ yrs Inside marketplace trust and ranking analytics before joining BGR Review. quarterly reporting Board-ready Every enterprise client gets a quarterly reputation-health report designed to be handed to a CFO or board directly. > "Reputation is a number long before it's a feeling. If we can't measure it, we won't promise it." > > \- Perves, Business Analyst Background ## About Perves. Perves spent a decade inside consumer marketplaces building the analytics that decided which sellers got promoted and which listings got suppressed. He worked on trust-signal scoring, seller-quality models, and the ranking systems that quietly move billions in commerce every quarter. He knows how platforms actually judge trust signals from the inside - not from a Search Central blog post. He joined BGR Review's Thornhill analytics hub in 2023 to bring that discipline to reputation management. His removal eligibility scoring system runs at 94% accuracy against actual case outcomes across multiple jurisdictions - meaning when Perves says a review is likely to come off, it does. When he flags a case as low-probability, we tell the client up front rather than burning their retainer. His revenue-lift model is the piece of work most clients quote back to their board. It turns a rating improvement (say 4.2 to 4.6 across a multi-location retail group) into a defensible incremental-revenue number, complete with confidence intervals and the assumptions behind them. It's what turns a reputation retainer from a marketing cost into a P&L line the CFO will sign off on. How Perves thinks about the work Every promise in reputation management should be defensible in a spreadsheet. If a claim can't be modelled, it shouldn't be sold. That's the standard I hold every forecast, every eligibility score and every ROI number to. Ranking systems don't care about your press release. They care about signals. My job is to help clients read those signals honestly - the good, the bad, and the ones we can move. Expertise - Reputation analytics & modelling - Removal eligibility scoring - Review velocity & anomaly detection - Revenue-lift & ROI modelling - Executive & board reporting - Marketplace trust systems Contact [perves@bgrreview.com ](mailto:perves@bgrreview.com) Prefer to route your case through the team? Email [team@bgrreview.com](mailto:team@bgrreview.com) and we'll assign the right specialist. Based in Thornhill, Canada Works with clients across US, UK, Canada, EU and AU time zones. Credentials & certifications ## Verifiable industry credentials. - Google Data Analytics Professional Certificate Google (Coursera) · 2022 - Microsoft Certified: Data Analyst Associate (Power BI) Microsoft · 2021 - Tableau Desktop Specialist Tableau · 2020 - AWS Certified Cloud Practitioner Amazon Web Services · 2023 Education ## Formal training. - University of Toronto BSc, Statistics · 2011 - 2015 - Rotman School of Management (Executive) Business Analytics Executive Program · 2019 Career timeline ## Perves's career, in the open. 1. 2015 Analytics Associate · North American consumer marketplace Started on the seller trust and ranking analytics team, working on quality-signal scoring. 2. 2017 Senior Business Analyst · Consumer marketplace, Toronto Led the models that decided which listings got demoted for trust reasons and which sellers were escalated to Trust & Safety. 3. 2020 Analytics Lead, Trust & Safety · Global marketplace Built the anomaly detection stack the Trust & Safety team used to catch review manipulation and coordinated seller behaviour. 4. 2023 Business Analyst · BGR Review Joined to found the Thornhill analytics hub. Built the removal eligibility scoring system and the revenue-lift model used in every 90-day plan. How Perves works ## The standards on every case. Step 1 ### Score before you ship Every removal case is run through the eligibility model before a caseworker is assigned - low-probability cases are flagged to the client up front. Step 2 ### Forecast with confidence intervals Rating trajectory and revenue-lift models always ship with the assumptions and the confidence interval, not a single hero number. Step 3 ### Backtest weekly Every closed case is fed back into the model. If prediction accuracy drifts below 90%, the model is retrained before it's used on a new client. Focus areas ## What Perves owns at BGR Review. ### Eligibility scoring Every removal request scored for likelihood before a caseworker spends time on it. ### Forecasting Projected rating trajectory and revenue-lift models delivered with every 90-day plan. ### Executive reporting Quarterly board-ready reports on reputation health across every location and platform. Publications - How we forecast the revenue lift of a rating improvement (with the assumptions we actually use) BGR Review Insights · 2025 - Removal eligibility scoring: what a 94%-accurate model tells us about which reviews actually come off BGR Review Insights · 2024 - The three anomaly patterns that signal a coordinated review attack BGR Review Insights · 2024 Speaking - Toronto Analytics Summit (guest speaker) "Trust signals in consumer marketplaces" · 2023 - BGR Review Enterprise Client Council "Revenue-lift modelling for multi-location brands" · 2024 Press & commentary - Contributed analytical framing to a Canadian retail publication on rating-driven revenue impact - Cited in industry commentary on marketplace anomaly detection Ask Perves ## Answers, straight from the specialist. How accurate is your removal eligibility model? + 94% score-to-outcome accuracy across our closed case history, backtested weekly. If a case scores below our threshold, we tell the client before we take the work - we don't burn a retainer on a low-probability request. How do you calculate revenue lift from a rating improvement? + It's a model, not a promise. We combine your live conversion data, your local demand signal, and the observed click-through impact of rating changes in your category. Every forecast ships with the assumptions and a confidence interval so a CFO can pressure-test it. Can you spot a coordinated review attack early? + Usually, yes. Coordinated attacks leave anomaly patterns in review velocity, reviewer profile clustering and content similarity that a well-trained model catches inside 24-48 hours. We alert the client the same day we see it. More of the team ## Meet the rest. [About Us](/about) [ ![Portrait of Robiul Alam, Founder & CEO at BGR Review](/__l5e/assets-v1/bd59d4dc-9165-4deb-b78a-48acaeac6685/team-robiul.webp) ### Robiul Alam Founder & CEO View profile ](/team/robiul-alam)[ ![Portrait of Adam, Reputation & Branding Specialist at BGR Review](/assets/team-adam-CeEayreR.jpg) ### Adam Reputation & Branding Specialist View profile ](/team/adam)[ ![Portrait of Emily, SEO & Marketing Lead at BGR Review](/assets/team-emily-BYom37WG.jpg) ### Emily SEO & Marketing Lead View profile ](/team/emily) ## Want Perves on your case? Book a free reputation audit and request Perves by name. A specialist reviews your profile, benchmarks three competitors, and sends back a written plan. [Request my free audit](/contact)[Email Perves](mailto:perves@bgrreview.com) [![BGR Review](/__l5e/assets-v1/87d66153-cb74-4e78-b954-d1aa9e9b3f70/bgr-logo.png)BGR Review ](/) Founded in 2019. 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