cx-article-effectivenesslisted

# Measuring article effectiveness View counts are the most dangerous metric in self-service. **A high-traffic article that precedes a contact is a failure, not a success** — customers read it, did not get what they needed, and wrote in anyway. Teams celebrate the traffic; the contact rate tells the truth. The question is not "did they read it?" It is **"did reading it remove the need to contact?"** ## Views are not resolution | Metric | What it actually measures | What it cannot tell you | | --- | --- | --- | | Page views | Exposure | Whether the problem was solved | | Time on page | Engagement (ambiguous) | Whether they found the answer or gave up | | Search clicks | Findability | Whether the destination helped | | Helpful votes | Sentiment of readers who bother | Selection bias; no contact linkage | | Deflection rate (platform) | Vendor-defined, often optimistic | Methodology varies; rarely auditable | An article can have high views because it ranks well, because the title promises something it does not deliver, because agents link it habitually, or because the bot surfaces it before handoff. **None of those imply resolution.** ## Core effectiveness signals Build analysis from conversation exports linked to article events. Exact event names vary by platform; the logic does not. **1. Contact-after-view (CAV).** Customer viewed article A, then opened a ticket on the same topic within a defined window (typically 24–72 hours, same session where possible). **CAV rate = c