Autonomous Anomaly Detective — Business Changes

Candidate change: weekly tickets increased

Observed data

Prior weekly counts: 102, 98, 105, 100, 99, 101, 97. Current week: 164. The seven-week prior mean is 100.3; the current value is 63.5 above that mean (about 63%). This is a descriptive comparison only, not a validated statistical significance test.

Initial assessment

Candidate anomaly: yes, for investigation. The observed count is materially higher than this short recent baseline. Confidence is limited because the series is small and we do not know the data definition, seasonality, daily distribution, or pipeline quality. A product launch is a plausible contextual factor, not a confirmed cause.

Verify before escalation

1. Confirm ticket definition, reporting window, filters, and source query stayed consistent.
2. Check ingestion delays, duplicates, backfills, outages, and missing data; missing is not zero.
3. Compare day-level counts and channels, products, customer segments, geography, and ticket types.
4. Check planned launch timing, campaign exposure, support staffing, and any known incidents.
5. Compare with longer history, day-of-week patterns, comparable launches, and relevant volume denominators such as active users or orders.

Hypotheses to test (not findings)

• Launch generated more questions or defects.
• Customer or order mix changed.
• A queue, source, or tracking rule changed.
• Duplicate/backfilled events inflated the count.
• An unrelated incident or normal variation explains the increase.

Next steps

Ask support operations and data owners to validate definition and pipeline health, then segment and compare the relevant cohorts. Escalate only if the increase persists after validation and materiality thresholds are met. No root cause is confirmed from these values alone.