01
Review tracking and anomaly signals together
Bring spend anomalies, conversion health, and attribution differences into one place instead of checking them in separate tools.
Back to product playbook
Validate tracking before a false performance drop triggers the wrong budget cut
Use Spark to review pixel and conversion tracking status, spot spend anomalies, and compare platform revenue against Shopify so attribution problems get surfaced before the team makes the wrong optimization move.
Product
Spark: AI Store Assistant
Category
Attribution
How it works
See how this workflow runs from signal to action so the team can actually reach the intended outcome.
02
Identify whether the issue is platform, tracking, or attribution
Separate real performance change from broken event flow, missing conversion data, or revenue mismatch noise.
03
Turn the alert into the next repair or decision step
Use the signal to decide whether the next move is validation, repair, budget protection, or a deeper platform-side review.
Frequently asked questions
Frequently asked questions
Does this replace engineering-level tracking debugging?
No. Spark is the earlier warning and triage layer. Deep technical debugging may still happen elsewhere, but the team gets to the problem faster.
Why combine spend alerts and tracking health in one use case?
Because operators often see them together in the real world: spend looks wrong, conversions disappear, and the first job is deciding whether the issue is performance or data trust.