FAQ
What is DriveMetaData used for?
DriveMetaData is positioned as a customer intelligence and marketing data platform for teams that need to unify customer data, measure campaign performance, detect ad fraud, analyze behavior, and activate audiences.
Is DriveMetaData only for marketers?
No. Marketers are a primary audience, but growth, product, analytics, data, and media quality teams also use the same customer and campaign data to answer different questions.
What should we connect first?
Start with the sources needed for one trusted workflow. For most teams, that means behavior events, campaign cost data, CRM or customer records, and conversion or revenue data.
Why do attribution results differ from ad platforms?
Common reasons include different attribution windows, timestamp handling, duplicate conversion rules, self-attribution by platforms, fraud filtering, timezone differences, and refund or offline conversion timing.
Can we use DriveMetaData without third-party cookies?
The product positioning emphasizes privacy-first and cookieless-ready measurement. Your exact setup should use the approved first-party, consent-aware, and platform-compliant identifiers available to your organization.
How should we use AI predictions?
Use AI predictions as decision support for intent, churn risk, conversion likelihood, and value signals. Validate them with reporting, experiments, or controlled activations before making large operational changes.
How do we know whether a segment is safe to activate?
Review the segment purpose, inclusion criteria, exclusions, consent rules, suppression lists, destination identifiers, sample profiles, and success metric before launch.
What is the difference between a funnel report and a retention report?
A funnel report measures movement through a sequence of steps. A retention report measures whether a cohort returns or continues meaningful activity over time.
What should we do when fraud alerts spike?
Review the spike by partner, campaign, geo, device, event, and click-to-install timing. Compare the suspicious cohort against normal traffic and document any exclusion, block, or partner escalation.
Who should own data quality?
Data quality is shared. Analytics usually owns metric definitions and validation, engineering owns implementation quality, marketing owns campaign taxonomy, and product owns event meaning for product behavior.
How often should reports be reviewed?
Review operational campaign and fraud signals daily during active spend. Review funnel, retention, attribution, and KPI definitions weekly or monthly depending on business cadence.
Can this sample KB be used as production documentation?
Use it as a starting structure. Before production use, replace sample workflows with your exact workspace permissions, connector names, data handling policies, support process, and implementation details.