traffic-change-diagnosislisted

# Traffic change diagnosis The specialist version of the diagnostic method, applied to one question: why did traffic (sessions, pageviews, unique visitors) move? Read `analytics-diagnostic-method` first if you haven't. This skill assumes you already know MECE, triangulation, sample size, and Pyramid presentation. It adds the traffic-specific fingerprints and drill paths. ## Opening moves 1. Read `analytics-profile.md` if present. The profile tells you the expected monthly traffic range and known measurement gaps. Both matter: a "spike" from 200 to 400 sessions/day on a <1k/month site is plausibly random; the same shape on a 1M/month site is an event. 2. Restate the question precisely. "Traffic dropped" is not a question. "Daily sessions fell from ~1,400 to ~900 starting April 18, with no corresponding drop in pageviews" is. 3. Decide: is this a real change at all? Apply the sample-size rule from `analytics-diagnostic-method`. Below ~300 observations per bucket, most "changes" are noise. ## The hypothesis tree for traffic Walk in this order. Cheap checks first. ``` Traffic change ├── 1. Measurement (check first, always) │ ├── Tracking regression (script missing, container unpublished, CSP block) │ ├── Bot filter toggle (inclusion/exclusion changed) │ └── Attribution shift (referrer policy, cookie policy, cross-domain) ├── 2. Time shape (what does the curve look like?) │ ├── Cliff (single hour/day step) → discrete event │ ├── Ramp (gradual over days