- Optimizely X Web Experimentation
- Optimizely X Web Personalization
- Optimizely X Full Stack
- Optimizely X Mobile
THIS ARTICLE WILL HELP YOU:
- Create a new metric for an experiment or campaign
- Edit an existing metric
In Optimizely, a metric is a quantitative measurement of a visitor action. Metrics help you measure differences in visitor behavior based on changes you make to your site in experiment variations or personalized experiences.
Metrics are based on events, which track actions like clicks, pageviews, form submissions and purchases. After you create events and add them to an Optimizely X campaign or experiment, they become metrics. In other words, metrics express events in a way that can be measured.
Without metrics, your experiments and campaigns wouldn't have any results. Metrics determine which variations are winners and which are losers. Every Optimizely experiment needs at least one metric, or there’ll be no results to measure.
Choosing a poor primary metric is one of the most common reasons that experiments don’t reach statistical significance. See this article for tips on how to choose metrics that deliver statistically significant results.
You define and add metrics to your experiments and campaigns with Optimizely’s natural-language metrics builder. It asks you to define a small set of parameters that tell Optimizely 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 (unique conversions, total conversions, or total revenue - also called the numerator), and the rate at which you want to measure it (per visitor, per conversion, or per session - in Optimizely, this is called the denominator).
You can only specify the denominator if you're measuring total revenue. Otherwise, the denominator is automatically set to "per visitor."
This means, for example, that you can build three different metrics from the same “Purchased” event:
Unique conversions: the number of visitors with at least one conversion
Total conversions: the total number of conversions
Total revenue: total revenue generated
Build a metric
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.
To build a metric in the metrics builder, follow these steps:
From the Experiments tab, click Create New... and select Experiment or Personalization Campaign.
The New Experiment or New Campaign modal window will appear.
Locate the Metrics section in the modal window. Choose an event from the list to be your primary metric.
Choose a name for your metric and set its parameters:
Winning direction: Specify whether you want this metric to increase or decrease. In most experiments and campaigns, you will want to see your metrics increase. However, for some metrics - like bounce rate, for example - a lower value (i.e., negative lift) would be more desirable.
Numerator: The numerator specifies how events and values are counted. You can set this to measure unique conversions, total conversions, or total revenue.
Denominator: The denominator specifies whether this metric is calculated on a per visitor, per session, or per conversion basis. If you choose total revenue for your numerator, you can also select "per conversion" as the denominator. Otherwise, the denominator is automatically set to "per visitor" in Experimentation or to "per session" in Personalization.
Click Add to Experiment or Add to Campaign. This metric is now being tracked for the experiment or campaign.
Edit a metric
You can edit your metrics by going to the Results tab and clicking any metric's Edit button.
From here, you can change the metric's name, winning direction, and numerator. Click Save to save your updated metric definition.
For complete details about all the enhancements included in the metrics builder, check out this article.
Be careful when editing a metric on a running experiment or campaign. Doing so can change the statistical significance on other metrics in the same experiment or campaign. This is because Optimizely’s Stats Engine simultaneously limits the proportion of false detections across all metrics in an experiment or campaign through false discovery rate control.
Optimizely balances confidence and speed of results by controlling false discoveries only for metrics currently included on the experiment or campaign Results page, adapting as the metrics themselves change.
This means experiments and campaigns with metrics selected and fixed in advance are less prone to cherry-picked results that may present a misleading picture. Excessive metric changes after an experiment or campaign has started may invalidate Optimizely’s guarantees regarding false detection.