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Amazon Store Onboarding & Operations Overhaul
Comprehensive Account Audit: Conducted a full operational review upon taking over store management to identify critical growth bottlenecks, listing suppressions, and campaign inefficiencies.
Health & Compliance Stabilization: Resolved account health warnings, reinstated restricted listings, and protected brand assets to ensure seamless day-to-day operations.
Catalog & Buy Box Optimization: Adjusted pricing strategies and optimized listing variations to maximize Featured Offer (Buy Box) win rates.
PPC Restructuring: Audited active advertising campaigns, cut non-performing search terms, and restructured ad groups to eliminate wasted spend and prepare the account for scalable growth.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Expert Multi-Platform E-Commerce Virtual Assistant & Problem Solver
Are account warnings, listing errors, catalog sync bugs, or cross-platform inventory issues holding your store back?
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✅ Etsy | TikTok Shop | Wayfair | WooCommerce
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eCommerce ManagementProduct StrategyAmazon AdsHelium10amazonvirtualassistantecommercedatashopifyexpert
Retail Fraud Analysis
Business Gap & Problem
Retail businesses suffer significant financial losses through fraudulent transactions and ineffective fraud monitoring. Without clear visibility, organizations struggle to identify where fraud occurs, understand the behavioral indicators tied to suspicious transactions, and translate data into practical risk management recommendations.
Solution
Built an end-to-end retail fraud analytics solution to analyze high-volume transaction logs, track suspicious activity flags, and present actionable risk intelligence through a multi-page Power BI dashboard.
Page-by-Page Dashboard Breakdown
1. Overview Page
Purpose: Serves as the high-level financial command center, tracking overall enterprise revenue against fraud exposure and categorizing risk tiers.
Key Metrics & Visuals: Displays top-level KPI cards for Total Revenue ($12.04M), Fraud Loss ($6.95M), Net Revenue ($5.09M), and Total Transactions (100k). Features a fraud indicators matrix, regional fraud breakdown across top countries, transaction amount trends by fraud over time, domestic vs. international transaction distribution, and a risk level analysis chart categorizing exposure into Low, Medium, and High risk.
2. Detection Page
Purpose: Focuses deeply on transaction-level detection mechanics, velocity flags, and vulnerable merchant or payment categories.
Key Metrics & Visuals: Highlights fraud change metrics year-over-year (+52.44%) and a global fraud rate gauge (47.51%). Includes a transaction frequency matrix, fraud revenue by payment method (spanning Google Pay, Debit Card, Credit Card, PayPal, and Apple Pay), fraud revenue across merchant categories (Fashion, Luxury, Gaming, Travel, Groceries, Electronics), and a breakdown of fraud by account age.
3. Recommendations Page
Purpose: Translates complex analytical findings into an executive-ready summary and actionable risk mitigation strategies.
Key Metrics & Visuals: Features a structured Executive Summary detailing financial exposure across risk tiers (noting that Medium Risk carries the highest exposure at $7.1M) and a targeted Recommendation section advising management to strengthen medium-risk monitoring, review specific payment methods, enhance international verification steps, and track multi-indicator accounts.
Every row. Every decision. Every failure.
I built Dispatch Guard to explore what happens after a data audit meets a real failure boundary.
Six synthetic rows become three accepted records and three review items. Then change the source after approval, repeat an intent, or simulate a worker crash after the mock receiver commits. The Control Room exposes the consequence, the preserved approval and the event trace.
The single HTML attachment runs directly from the Contra Work case. Its JavaScript model agrees with four separately recorded Python/SQLite reference cases; 51 Python tests pass.
The important boundary: an outbox alone cannot guarantee one external delivery. Recovery depends on the receiver retaining the request key, payload binding and receipt.
Original self-initiated, AI-assisted engineering demonstration using synthetic data.
