Diagnose Ecommerce Traffic and Bot Anomalies

The opportunity

A traffic spike can come from real demand, a campaign, referral spam, server-side events, duplicate tags, monitoring tools, internal users, or automated sessions. Filtering before checking commerce impact can hide a real event; treating every session as a customer can distort decisions.

BizSidekick compares the anomaly with a normal baseline, profiles the affected traffic, checks event and order consistency, and separates verified facts from likely causes.

How it works

1. Define the anomaly window — Confirm the metric, start and end, comparison baseline, time zone, known releases, campaigns, and business events.
2. Profile the traffic change — Compare source, medium, campaign, geography, device, browser, landing page, hostname, engagement, and session timing.
3. Inspect event behavior — Check duplicate events, impossible sequences, event-to-session ratios, missing identifiers, consent behavior, and tag changes.
4. Verify commerce impact — Compare add-to-cart, checkout, purchase events, Shopify orders, net sales, refunds, and inventory activity.
5. Classify likely causes — Separate real demand, known campaign traffic, tracking defects, internal traffic, monitoring, referral spam, and probable automation.
6. Build filtering and monitoring guidance — Return evidence, business impact, proposed data filters, validation steps, owners, and recurrence alerts.

Safety boundary

The diagnosis does not delete analytics data or apply a permanent filter. Probable automation remains a hypothesis until supported by multiple behavioral signals and checked against real orders.

@BizSidekick Diagnose this GA4 traffic anomaly. Compare source, geography, device, landing page, events, engagement, conversion, Shopify orders, and known campaign changes, then separate likely bots, tracking defects, and real demand.