AI social companies need dashboards that connect growth with product quality and variable compute economics. Traditional social metrics remain useful, but persistent AI interaction introduces additional dimensions.

Activation

Measure whether new users reach a meaningful first conversation, not simply account creation. Discovery-to-chat conversion and first-session depth can reveal onboarding quality.

Retention

Track D1, D7 and D30 by cohort and user job. Repeat interaction with the same personality can be particularly informative because it indicates continuity rather than browsing.

Monetization

Monitor payer conversion, average revenue per payer, credit repurchase and refund rate. Separate text, image and video spending where costs differ.

Contribution margin

Revenue should be viewed after inference, media generation, payment fees and creator share. Our unit economics guide provides the calculation logic.

Creator supply

Track active creators, time to launch, revenue concentration and creator retention. A large signed roster is less important than personalities that attract repeat users.

Quality

Monitor latency, failed generations, memory errors and identity drift. These operational measures can explain changes in retention before financial metrics do.

Safety

Use incident rates, review time and appeal outcomes carefully. The goal is not to optimize for fewer reports by hiding reporting tools, but to understand system behavior and response quality.

A board dashboard should tell a causal story

The best KPI set shows how acquisition becomes activation, activation becomes retention, retention becomes monetization and monetization becomes contribution margin while creator supply and safety remain healthy.