Data we collect and why we collect it

A practical view of data collection

Betatron is designed to collect the minimum information needed to provide campaign intelligence, automation controls, and account health visibility. The platform is not built to harvest unrelated personal information; it is built to support accountable ad operations with clear auditability.

Most data falls into a few categories: account metadata, campaign performance metrics, onboarding and chat instructions, and operational logs used for reliability and abuse prevention. Keeping categories explicit helps teams understand what is processed and what is intentionally out of scope.

When customers evaluate privacy risk, the most useful approach is to map each data type to a direct product function. If a field does not have a defensible purpose, it should not be retained.

Google Ads account and campaign metadata

Betatron reads selected Google Ads account information through authorized API access so it can analyze campaign behavior and generate recommendations. Typical fields include campaign names, ad group structures, spend and performance summaries, conversion action metadata, and account configuration states.

This metadata is used to power diagnostics, pacing analysis, recommendation ranking, and deployment safeguards. It is not used to profile individuals outside the agreed product context.

• Account identifiers and linked resource references
• Campaign, ad group, and keyword structure details
• Spend, click, impression, and conversion aggregates
• Settings relevant to targeting, bidding, and budgets

User-provided business context and chat inputs

During onboarding and ongoing operations, teams provide strategic context in chat: objectives, CPA targets, exclusions, audience constraints, launch timing, and approval preferences. This context is essential because pure metric analysis rarely captures business nuance.

Betatron stores this context so recommendations remain consistent over time and so changes can be traced when teams revisit prior decisions. Without this retained context, optimization quality would regress after each session.

Organizations should treat chat inputs as operational instructions and avoid placing unnecessary sensitive personal data in those messages.

Technical telemetry and security logging

Like most SaaS systems, the platform captures technical telemetry such as request timestamps, authentication outcomes, API error responses, and feature usage events. This telemetry supports uptime monitoring, incident response, abuse detection, and product reliability improvements.

Security logs are particularly important for tracing account access issues, unexpected permission failures, and suspicious activity patterns. Retaining these logs for an appropriate period improves forensic readiness during investigations.

• Authentication and authorization event trails
• System health and performance diagnostics
• Rate-limit, abuse, and anomaly signals
• Change history for high-impact account actions

Data not needed for core ad operations

Betatron does not require broad collection of unrelated personal consumer data to deliver its core value. The product focuses on campaign operations rather than identity monetization or cross-service behavioral profiling.

Teams can improve their own privacy posture by limiting which sources they connect, sharing only relevant business context, and avoiding sensitive customer-level records unless specifically required for a supported integration and lawful purpose.

Data minimization is a shared responsibility: product architecture can reduce default collection, and customer workflows can reduce over-sharing.

How collected data is used in practice

Collected data is used to run core product workflows: generate campaign recommendations, evaluate optimization outcomes, maintain system reliability, provide support diagnostics, and enforce platform safety controls. Each use case should map to documented operational needs.

For example, conversion trend data helps Betatron prioritize recommendation impact, while permission metadata determines whether a suggested action can be executed safely through API access. Logging and audit trails support accountability when teams review what changed and why.

Where possible, aggregated or scoped views are preferred over raw broad exposure. This reduces accidental access and keeps internal processing proportional to purpose.

Your role in privacy stewardship

Even with strong platform controls, customer teams play a critical role in privacy outcomes. Access policies, onboarding discipline, and periodic reviews of connected accounts all influence whether the system remains aligned with your compliance obligations.

• Grant access using least-privilege principles
• Avoid sharing unnecessary personal details in chat
• Review connected accounts and permissions regularly
• Document internal governance for who can approve changes

When privacy is treated as an ongoing operating practice rather than a one-time checkbox, both security and campaign performance become more durable.