This is super exciting, and I already see how we can use this on the fly. It will save us hours. My analysts will be over the moon for this.
Tapestry
Warehouse-native Analysen
Testen Sie anhand Ihrer wichtigsten Geschäftsmetriken
Schöpfen Sie das volle Potenzial Ihres Experimentierprogramms aus, indem Sie anhand der für Ihr Unternehmen wichtigsten Metriken testen und optimieren.
Entdecken Sie die Funktionen unserer Warehouse-nativen Analytik
Was uns auszeichnet
Connect with your data warehouse
Seamlessly integrate with the most popular data warehouses including Snowflake, Databricks, Google BigQuery, and Amazon Redshift, for faster setup and data flow management.
- Connect instantly to your data warehouse with ready-to-use integrations.
- Map essential data points and customize tables to make data readily available for experimentation.
- Run health checks to confirm connection stability and data readiness before diving into experiments.
- Ensure smooth data management across platforms for accurate, real-time analysis.
Build custom metrics
Create and customize a range of metric types to track, measure, and analyze experiment outcomes in detail.
- Select from built-in metric templates: Conversion Metrics (track user actions), Numeric Aggregations (sum or average values), and Calculated Metrics (combine multiple metrics for more complex insights).
- Define metrics like revenue, page load time, or return rate, with the flexibility to sum, average, and aggregate values.
- Write formulas to create custom metrics, with basic arithmetic operations to calculate complex data, such as total realized revenue from various sources.
Use the experiment scorecard
Analyze experiments with a comprehensive scorecard that gathers all essential data for quick and effective decision-making.
- Select an experiment, then add primary and secondary metrics in a dedicated view to tailor results to your needs.
- Automatically pull in experiment summary information like Days Run, Total Visitors, Page Targeted, and more for a holistic view of each experiment.
- See metric performance by variation, with details on metric value, percentage improvement, confidence interval, and statistical significance.
- Segment results by user attributes (e.g., platform or country) to uncover how different audiences respond to experiments.
Unlock native analytics views
Unlock deeper insights into user behavior with built-in analytics views designed to support detailed journey and funnel analysis.
- Visualize user flows with funnel views to track how users progress through each step toward conversion.
- Use path views to analyze the sequences users take to and from conversion events, capturing both entry and exit points.
- Explore trends over time and segment data within dashboards, which can be shared across your team for strategic decision-making.
Entdecken Sie die Vorteile
On-the-fly-Untersuchungen
Sparen Sie Stunden bei der manuellen Datenanalyse, indem Sie schnell kohortenspezifische Erkenntnisse gewinnen, ohne auf kostspielige oder benutzerdefinierte Datenabfragen angewiesen zu sein.
Datenkonsistenz
Stellen Sie die Zuverlässigkeit Ihrer Daten sicher und vermeiden Sie Diskrepanzen, indem Sie die vertrauenswürdige Datenquelle Ihres Unternehmens für das Experimentieren und die Analyse nutzen.
Sichere und geschützte Daten
Behalten Sie die volle Kontrolle über den Speicherort Ihrer Daten, indem Sie sie intern aufbewahren. So werden rechtliche Bedenken ausgeräumt und Sie können mehr experimentieren, ohne sensible Informationen zu verschieben.
Warehouse-native Statistiken
Nutzen Sie Daten aus allen digitalen Kanälen, einschließlich E-Mail und CRM, um mit der Stats Engine von Optimizely tiefgreifende Analysen zum Experimentieren durchzuführen.
We define our own sessions, and we define our own metrics. Everything already sits in our warehouse as a single source of truth.
Chewy
This is the holy grail of visualization that a lot of analytics tools don't have. This is awesome, especially if you can show the difference [in journeys] between different variants and your test vs. control groups. This is something we have never had. It's a very heavy lift and will save a lot of operation time from my analytics team.
Cox Automotive
