Insights on product analytics and data modeling.
Semantic layers, user behavior models, and the statistics behind the metrics F2P teams live by.
Warehouse-native analytics cost, and how to keep it low
Warehouse-native analytics runs queries in your warehouse, so the compute is your bill. Here is what drives that cost, and how Asemic keeps it low.
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Are Dashboards Dead? No: a Dashboard Is a Cached Answer
Dashboards are not dead; they are cheap. When a semantic layer makes every chart generatable on demand, the definitions become the asset, not the packaging.
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An Analytics MCP Server for the Person Who Wants a Number
Asemic's analytics MCP server connects Claude, ChatGPT, or Gemini to your game's metrics: one URL, sign in, permissions automatic. No developer setup.
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Your Firebase BigQuery Export Is Already a Data Warehouse
Turn the Firebase BigQuery export into retention, cohorts, and LTV without a pipeline, and avoid the query-cost trap of a view over events_*.
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Measuring Monetization Impact on Engagement: A New Approach
The mDAU/DAU ratio isolates the real effect of monetization on engagement from selection bias, and it is measurable without building a model.
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Modeling User Behavior Metrics in Freemium | Part 2
A dual-propensity model of engagement and payment behavior that reproduces real payer retention and cohort conversion curves with two parameters.
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Modeling User Behavior Metrics in Freemium | Part 1
Paying users retain far better than non-payers. A simple random-flagging experiment shows how much of that gap is selection bias rather than psychology.
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Why User-Centric Analytics Beats Event-Based Tracking
Why event-based analytics holds back product insights, and how user-centric analytics simplifies metrics, improves performance, and fits business goals.
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New Approach to Semantic Layer Modeling
Why Asemic builds the semantic layer from business logic down: define metrics in business terms; the app maintains the physical model in your warehouse.
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Data Modeling: The Essential Foundation of Effective Data Analysis
Discover how data modeling shapes analysis outcomes and why it's crucial for deriving meaningful insights from your data.
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