Deepnote powers collaborative gaming analytics, enabling real-time insights on player retention, monetization strategies, and game performance optimization.
Trusted by leading gaming companies
Protect player and studio data with enterprise-grade controls. Deepnote ensures compliance for gaming analytics teams at any scale.
Single Sign-On (SSO)
SAML authentication
Granular permissions
Encrypted connections
Single-tenant deployment
On-premise options
Audit logs
Sample Vendor in Gartner® Hype Cycle™ for Collaborative Analytics for 2025
Deepnote is the enterprise notebook platform for gaming analytics.
Analysts, data scientists, and game designers collaborate in real time across SQL + Python to deliver governed game analytics dashboards:
Cohort models and KPI modules
Improved player retention and lifetime value
Turn mobile game data into actionable video game insights:
Game retention strategies and player churn prediction
Game economy tuning and game balance
Support for LiveOps
With native connectivity to your game analytics infrastructure (warehouses, ETL, BI) and in-cell AI that automates code and commentary, Deepnote accelerates game analysis, operationalizes games monitoring
Keeps analytics reproducible and governed
Connects with SensorTower, Unity, BigQuery, Snowflake, and Redshift
Player retention & churn
Cohort curves (D1/D7/D30), rolling retention reports, and churn risk scoring models to improve mobile game retention and mobile game data analytics.
Use BigQuery, Snowflake, or Redshift with integrations such as GameAnalytics, Unity Analytics, Mixpanel, and dbt for transformations.
Monetization & economy tuning
Combine IAP and ads data, calculate ARPDAU and ARPPU, segment payers, and build player lifetime value models.
Run game balance and game economy simulations, perform pricing tests, and forecast revenue with Python.
Connect warehouses and BI tools (Looker/Tableau) with AppsFlyer or Singular for attribution.
Community sentiment & toxicity detection
Monitor Steam reviews, Reddit, and Discord to detect issues early and guide roadmaps.
Use LLM/NLP to classify toxic in-game chat and trigger real-time warnings, mutes, dashboards, and playbook actions to protect community health and retention.
Acquisition & attribution performance
Track CPI and ROAS cohorts by channel, and measure creative lift.
Map SKAN data to understand how analytics can help improve game user acquisition.
Work with AppsFlyer, Singular, Mixpanel, and Google Analytics (Firebase for Unity).
Native connectors to warehouses (BigQuery, Snowflake, Redshift) and dashboards; no fragile local setup, perfect for game analytics pipeline work.
AI‑assisted SQL+Python, charting, and code generation slash time‑to‑answer for game analysis.
Game analytics metrics (retention, ARPDAU, LTV, eCPM, fill rate) rendered as reusable modules.
Use Deepnote Agent for game analytics pipelines and in‑game analytics with Python or R, write SQL with schema context, and deploy notebooks into production. Integrate ML models and AI agents directly into notebooks when BI falls short.
Real‑time multi‑editor notebooks, comments, and governed workspaces keep analysts, PMs, and designers aligned on game statistics and games monitoring.
LiveOps
Set up alerts on DAU, payer conversion, and server KPIs.
Connect these metrics directly to feature flags and remote config with Unity Analytics (Snowflake Data Access), PlayFab events, and Streamlit apps in Deepnote.
A/B testing
Analyze FTUE/tutorial funnels and identify funnel drop-off points.
Run statistical tests for in-game analytics on new features using Mixpanel, Unity Analytics via REST API, and orchestration tools like dbt and Airflow.
Game balance
Run hero/build win-rate analysis and evaluate matchmaking balance.
Generate gaming insights from match telemetry for video game analytics using OpenDota and Steam Web APIs, DuckDB or warehouse tables, and interactive Deepnote notebooks and apps.
Capability
Deepnote for Gaming
Classic Jupyter
Data connectivity
Native integrations with Snowflake, BigQuery, Redshift, Databricks, and more
Schema browser and secrets vault
Python and SQL together
Manual driver setup
Local environment configuration
Connectors vary
Collaboration
Real‑time multi‑user editing
Comments, shareable projects, and shared program state
Single editor by default
Handoffs via files or Git
AI agent
End-to-end AI-assisted data analysis
Inline AI suggestions for Python and SQL
Text to SQL with your data context
No native AI functionality
No shared AI context of schemas or enterprise metadata
Resource optimization
Auto-scaled cloud compute resources
Persistent environments that restart cleanly
Manual environment management and upkeep
Local or DIY servers
Enterprise governance
SAML SSO, SCIM, RBAC, audit logs, and workspace policies
Artifact lineage tracking, SOC 2 readiness and GDPR support
Add‑ons or self‑built solutions required
Scheduling and apps
Schedule recurring jobs
Publish interactive applications and dashboards
Needs external tools for scheduling and deployment
Integrations
First-class integrations across the modern data stack
Few or no first-class integrations available natively
Khanh Nguyen, Data Scientist
Guy Yarkoni, Head of Analytics & BI
Book a demo or start free to connect your warehouse, import mobile game data, and publish a game analytics app your product team can use today.
