Approach (brief): define metrics to distinguish behavioral distraction (users click CTA but abandon flow) vs functional regression (CTA click leads to broken or degraded signup funnel). Compute funnel conversion rates, time-to-signup, downstream event rates, error rates, and experiment cohort comparisons (treatment vs control, new vs old CTA). Check session-level sequences to see if clicks correlate with later drop-off or with technical failures.Key metrics:- Click-through rate (CTR) on CTA- Stepwise funnel conversions: CTA click → signup-start → signup-complete- Conditional conversion: P(signup_complete | CTA_click)- Time delta: median time from CTA_click to signup_complete- Downstream error events / API failures per CTA click- Session abandonment rate after CTA click- Unique users with multiple CTA clicks (confusion signal)SQL queries (assumes events table: events(user_id, session_id, event_name, event_time, properties), error_logs(session_id, error_code)):sql
-- 1) CTR and per-cohort counts
SELECT cohort,
COUNT(DISTINCT CASE WHEN event_name = 'page_view' THEN session_id END) AS sessions,
COUNT(DISTINCT CASE WHEN event_name = 'cta_click' THEN session_id END) AS cta_clicks,
COUNT(DISTINCT CASE WHEN event_name = 'signup_complete' THEN user_id END) AS signups,
SAFE_DIVIDE(COUNT(DISTINCT CASE WHEN event_name = 'cta_click' THEN session_id END), COUNT(DISTINCT session_id)) AS ctr
FROM events
WHERE event_date BETWEEN '2025-10-01' AND '2025-10-14'
GROUP BY cohort;
sql
-- 2) Funnel: P(signup | cta_click) and time-to-conversion
WITH clicks AS (
SELECT user_id, session_id, MIN(event_time) AS click_time FROM events
WHERE event_name='cta_click' GROUP BY user_id, session_id
),
signups AS (
SELECT user_id, MIN(event_time) AS signup_time FROM events
WHERE event_name='signup_complete' GROUP BY user_id
)
SELECT
COUNT(DISTINCT c.session_id) AS click_sessions,
COUNT(DISTINCT s.user_id) AS signups_after_click,
SAFE_DIVIDE(COUNT(DISTINCT s.user_id), COUNT(DISTINCT c.session_id)) AS conv_after_click,
APPROX_QUANTILES(TIMESTAMP_DIFF(s.signup_time, c.click_time, SECOND), 100)[OFFSET(50)] AS median_seconds_to_signup
FROM clicks c
LEFT JOIN signups s ON c.user_id = s.user_id AND s.signup_time >= c.click_time
sql
-- 3) Error rate after click (functional regression signal)
SELECT e.error_code, COUNT(DISTINCT e.session_id) AS sessions_with_error,
SAFE_DIVIDE(COUNT(DISTINCT e.session_id), COUNT(DISTINCT c.session_id)) AS error_rate_per_click_session
FROM clicks c
LEFT JOIN error_logs e ON e.session_id = c.session_id AND e.error_time >= c.click_time
GROUP BY e.error_code;
