Bot Traffic Scoring Model for E-Commerce Reporting
Built a hybrid bot traffic scoring model to protect KPI accuracy in e-commerce business performance reporting — catching sophisticated bots that evaded existing third-party filtering.
01. The Problem
Business KPIs were being distorted by bot traffic, including highly sophisticated bots that mimicked human behavior closely enough to slip through existing filtering solutions. This made it difficult to trust the numbers driving strategic decisions.
02. The Approach
Conducted in-depth analysis of traffic data to identify behavioral patterns associated with bot activity. Built a two-layer scoring model: the first layer flagged potential bot visits based on known behavioral signatures; the second applied a human-likeness filter to distinguish genuine human sessions from sophisticated bots. Each session was assigned a composite score, and visits crossing the threshold were excluded from reporting.
03. The Outcome
Delivered significantly cleaner KPI data for business performance reporting, reducing bot-driven distortion in executive metrics and restoring confidence in the numbers used for strategic decisions.
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