Warehouse-Native Experimentation Analytics
Get started with Warehouse-Native Experimentation Analytics
Optimizely Warehouse-Native Experimentation Analytics enhances your experimentation results by evaluating experiments against data that lives in your data warehouse. This ensures your data warehouse remains the single source of truth, keeping your data secure and centralized.
Create a decision dataset in Analytics
Decision events (or impressions) are special events triggered when Optimizely Experimentation "decides" that a visitor is bucketed into a certain experiment and variation pair. See Data specification https //docs.
Create an Experiment Scorecard in Optimizely Analytics
Optimizely Feature Experimentation Optimizely Web Experimentation Optimizely Performance Edge Complete the following to create an experiment scorecard in Warehouse-Native Experimentation Analytics Add your Optimizely account ID in the Warehouse-Native Analytics app settings. To add your account ID, send an email to support\@netspring.
Conversion metric
Use a conversion block in Warehouse-Native Experimentation Analytics to segment a dataset based on the observed behavior and other properties. The behavior of a dataset's record is determined by the events that are linked with the entity.
Numeric aggregation metric
Numeric aggregation in Warehouse-Native Experimentation Analytics lets you create simple aggregations over existing columns in your data. A numeric aggregate block calculates a summary of a property or block.
Ratio metric
In Warehouse-Native Experimentation Analytics, a ratio metric divides the values of two events to produce a single derived value. You can use it to measure derived rates such as content completion rate, revenue per session, or items purchased per visit.
CUPED (Controlled-experiment Using Pre-Experiment Data)
The CUPED (Controlled-experiment Using Pre-Experiment Data) functionality is a statistical method that reduces variance in A/B tests, enhancing their sensitivity and making it easier to detect differences between groups. By lowering variance, CUPED lets experiments achieve statistical significance with less data if there is a true treatment effect.
Experiment analysis
In Optimizely, experiments are structured tests designed to compare different variations of a webpage, feature, or experience to determine which performs best based on specific goals. These experiments help businesses optimize user experiences, increase conversions, and make data-driven decisions.