This dashboard provides a comprehensive, data-driven view of how users engage with the Solana blockchain across key behavioral stages: activation, retention, reactivation, and deactivation. Using on-chain data, it dissects user journeys over time, highlighting critical growth patterns, drop-off points, and program-level drivers of activity.

Key Components:

• User Onboarding Trends
  Tracks new wallet activations and programs that spark initial interaction with the Solana network.

• Engagement & Retention
  Visualizes new vs. returning users and average engagement spans, offering insight into how sticky the Solana ecosystem is month over month.

• Reactivation Patterns
  Reveals how long users stay inactive before returning and how reactivation varies by user segments.

• Deactivation Behavior
  Identifies users who drop off based on active days, showing both temporal churn and long-term disengagement.

• Program-Level Analysis
  Breaks down activation events by program, uncovering what types of dApps or features drive user activation.

• Strategic KPIs
  Includes metrics like Returning User Rate, Retention Rate vs. Churn, and Activation vs. Engagement ratios, all helping evaluate ecosystem health.

This dashboard equips developers, ecosystem growth teams, and analysts with actionable insights for enhancing user experience, reducing churn, and designing re-engagement strategies on Solana.

To provide reliable insights, this analysis draws from both the signers and transactions tables. Any perceived inconsistencies stem from the nature of the data each contains — explained below for clarity:

Note on Data Sources & Discrepancies

This dashboard combines insights from the signers and transactions tables. The signers table tracks wallet activity at a high level (e.g., first and last activity), making it reliable for identifying new users. The transactions table, meanwhile, captures detailed on-chain interactions and is used to estimate monthly active users.

However, certain types of transactions — such as spam, bot activity, or low-signal system instructions — are filtered out to focus on meaningful user behavior. As a result, the active user count from the transactions table may sometimes appear lower than the number of new users from the signers table. This is an intentional tradeoff to improve data quality and relevance.