Looking for how to make a metric in Full Stack instead of Web? View the following documentation:
- Creating a metric in Full Stack - Pre-Flags project
- Creating a metric in Full Stack 2.0 - Flags (created after February 2021)
- Understand the different components of a metric in Optimizely
- Create a metric in Optimizely's metric builder
- Add a metric to your Optimizely experiments
Optimizely Web's natural-language metrics builder lets you quickly define and add metrics to your experiments and campaigns. It works by asking you to define a small set of parameters that tell Optimizely Web how to measure and report the results of your experiment or campaign. These parameters include the winning direction (increase or decrease), what you want to measure (the numerator), and the rate at which you want to measure it (the denominator).
The process of building a metric using the metrics builder is straightforward.
From the Experiments tab, open the experiment with which you want to work.
Click Metrics to open the Metrics window.
Choose an event from the list to be your metric.
Choose a name for your metric and set its winning direction, numerator and denominator.
Optimizely Web offers several pre-built metrics, as well as a flexible numerator called total value. If you are not sure which metric to choose, see Types of metrics and when to use them.
To learn how to edit your metrics once you build them, see Edit a metric in Optimizely.
The metrics builder
The metrics builder is the interface within Optimizely that enables you to create your own metrics.
You can only build metrics in Optimizely if your role is authorized to create and edit campaigns and experiments—that means administrator, editor or project owner. See this article for more details.
Several metric types have additional requirements you need to be aware of prior to using them:
When you set a metric's winning direction parameter, you are telling Optimizely Web what kind of change you hope to see: an increase in your metric, or a decrease. In most experiments and campaigns, you will want to see your metrics increase. However, for some metrics—like bounce rate, Cancel button conversions or cart abandonment—a lower value (that is, negative lift) would be more desirable.
The metrics builder offers six different types of metrics templates. These types are also called numerators. You should select your metric's numerator based on the specific questions you want your experiment to answer:
Unique conversions. Number of visitors with at least one conversion
Total conversions. Total number of conversions
Bounce Rate. Total number of times where the page being viewed is the first and only page the visitor sees before leaving your site
Exit Rate. Total number of times where the page being viewed is the last page the visitor sees before leaving your site
Total revenue. Total revenue generated (you will have to set up revenue tracking before using the total revenue metric)
Total value. Total of any other numerical value (check out our article on total value use cases)
For more details on each metric type, see our article on when to use each type of metric in Optimizely.
If you are using total revenue or total value as your numerator, complete an additional step. The additional step for total revenue is not the same as the additional step for total value. Make sure you are reading the correct section.
The metrics builder's denominators can also be thought of as the rate at which your metric measures its numerator. For example, you could measure unique conversions per session, or total revenue per conversion.
Because of the inherent differences between metric types, and the differences between personalization campaigns and experiments, not every denominator works with every numerator.
|Numerators||Experiment type||Possible denominators|
|Per visitor||Per session||Per conversion|
|Unique conversions and total conversions||Personalization campaign|
|Unique conversions and total conversions||Experiment|
|Bounce rates and exits rates||Any||*|
|Total revenue and total value||Personalization campaign|
|Total revenue and total value||Experiment|
When you run an experiment with many variations and metrics, there is a greater chance that some of them will give false positive results. The Optimizely Stats Engine uses false discovery rate control to address this issue and reduce your chance of making an incorrect business decision or implementing a false positive among conclusive results. To learn how Stats Engine prioritizes primary and secondary metrics and monitoring goals, see Stats Engine approach to metrics and goals.