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Traffic Quality Scoring & Analysis

8 min read

What Makes Traffic High Quality?

High-quality traffic comes from real users with genuine intent who match your target audience. Low-quality traffic comes from bots, incentivized users, or audiences with no interest in your product. Traffic quality scoring assigns a score to each traffic source based on multiple signals, helping you identify which affiliates send valuable traffic and which need investigation.

Quality Signals to Monitor

SignalWhat It Tells YouRed Flag Threshold
Click-to-conversion ratioWhether clicks are genuineBelow 0.1% or above 30%
Time on siteWhether users engageUnder 5 seconds average
Bounce rateWhether users find what they expectAbove 90%
Geographic consistencyWhether traffic matches target marketsTraffic from unexpected regions
Device distributionWhether traffic is human95%+ from one device type
IP reputationWhether IPs are cleanKnown proxy, VPN, or datacenter IPs

Building a Scoring Model

Assign weights to each quality signal and calculate a composite score per affiliate. A simple model might weight conversion quality at 40%, behavioral signals at 30%, and technical signals at 30%. Partners above 70 are considered high quality. Partners between 40-70 need monitoring. Partners below 40 need investigation or suspension.

Automated vs. Manual Review

  • Automated: Set rules that flag or pause affiliates when quality drops below threshold
  • Manual: Review flagged partners before taking action -- false positives happen
  • Hybrid: Automated flagging with manual review for suspension decisions

Start with 3-4 quality signals and expand over time. Monitoring everything at once creates noise. Focus on the signals most relevant to your vertical and fraud risk profile.

Key Takeaways

  • Traffic quality scoring uses multiple signals to identify valuable vs. suspicious traffic
  • Monitor click-to-conversion ratio, time on site, geographic consistency, and IP reputation
  • Use a weighted scoring model -- partners below threshold get flagged for review
  • Start with 3-4 quality signals and expand as your fraud detection matures