The A/B Testing feature lets you to experiment with design and content variations on your website pages.This helps you determine which variations produce the optimal results before you make permanent changes to your website. Use the feature's in-context, visual method to create A/B tests on any of the pages delivered by Oracle WebCenter Sites.
Note:A/B test functionality is provided through an integration with Google Analytics. To use the A/B test functionality you must first register with Google for a Google Analytics account that lets you measure the performance of an A/B test. In addition, you acknowledge that as part of an A/B test, anonymous end user information (that is, information of end users who access your website for the test) is delivered to and may be used by Google under Google’s terms that govern that information.
For example, you might use A/B Testing to explore:
Which banner image resulted in more leads generated from the home page?
Which featured article resulted in visitors landing on a promoted section of the website?
Do visitors spend more time on the home page with a red banner displayed versus a blue banner?
Does adding a testimonial increase the click-through rate?
Which page layout resulted in more visitors downloading the featured white paper?
Creating an A/B test involves tasks, such as deciding what to compare, what to measure, as well as how and when to start and end the test. Once these tasks are complete, you can approve the test, see the experiment in Google, and then view the test result in the A/B test report.
Here are the main tasks:
Select the variants to test.
You can compare two or more variants, where variant A serves as control (base) and compared with other variants B, C, or D. You can run multiple A/B tests at the same time.
Choose parameters to measure the test.
You cam use a goal to specify the visitor action the test captures and compares for the variants, and then displays as A/B test results. A goal refers to a specific visitor action that you identify for tracking, and you can specify the type of goal that fits your use case. The default goals are Destination, Duration, Pages per session, and Event. For example, if you choose Destination, you can specify a page like
surfing.html. The conversion occurs whenever anyone visits this page.
Create an A/B test and add variants to it.
On the management system, you can create the test and use the WYSIWYG A/B test mode to add one or more variants to a selected web or mobile page. You can identify a variant by its color-coding, and easily save or discard its tracked changes. In the following example, first variant (B) displays in green in both the A/B Test panel (B) and in each change (1) on the page. See How do I create A/B tests?.
Figure 11-1 A/B Test Mode
Define criteria to start and end the test.
Define criteria on how and when the test starts and ends. Google Analytics only shows the duration of the event. For example, if you select start date as 14 and end date as 28, Google Analytics shows as 2 weeks. See How to specify the start and end date of an A/B test?.
Specify a statistical confidence level, for example, 95 for a 95% confidence level to achieve the results See How to select the confidence level of an A/B test?.
Click Select Goal and choose a goal in the dialog box that appears. See How to specify the goal to track in an A/B test?.
Select the visitors or segments to target for the test. You can display the test to a specified percent of the entire visitor pool or to a specified percent of one or more selected visitor segments. See How to specify visitors or segments to target in A/B tests?.
Figure 11-2 A/B Test Criteria Pane
Approve the test (and its dependencies) to see the actual Experiment in Google.
View test results in the A/B Test Report.
As visitors view variants and conversions take place, you can view the results for individual A/B tests on the report. This maintains the same variant color-coding you saw while creating test variants. The report lets you see if there are measurable differences in visitor actions between variants. A cookie is left so that visitors see the same variant upon returning to the site. See How do I view A/B test results?.
Figure 11-3 A/B Test Showing Variants
Optionally update the site to use the winning variant.
After displaying the winning variant for a while, you might copy or create an A/B Test to include new variants, giving you the option to refine iterations over time.
You need to complete a number of prerequisites before you can create your A/B test. For example, enabling asset type
WCS_ABTest, including A/B code element, and setting the property
abtest.delivery.enabled to true on the delivery instance.
You must enable
WCS_ABTest asset type.
A WebCenter Sites administrator or developer can enable the WCS_ABTest asset type, by default it’s enabled only in "avisports" and "FirstSite II" sample sites.
See Administering A/B Testing in Administering Oracle WebCenter Sites.
You must include the A/B code element inside the template before testing. By default, the code element isn’t included in the template.
A WebCenter Sites developer adds the A/B code element to templates.
See Template Assets in Developing with Oracle WebCenter Sites.
abtest.delivery.enabled must be set to true on the delivery instance. This is an instance that delivers the A/B test variants to site visitors. You must not set the property on instances that are only used to create the A/B tests as this gives false results. The
abtest.delivery.enabled property is in the ABTest category of the
See A/B Test Properties in Property Files Reference for Oracle WebCenter Sites.
Any user that is able to “promote the winner” of an A/B test must be given the
MarketingAuthor role. Any user that is able to view A/B test reports and stop tests must be given the
See Configuring Users, Profiles, and Attributes in Administering Oracle WebCenter Sites.
You can create your A/B test by setting up variants of the web page that you want to test and also set the test criteria to get relevant results.
You’ll find that creating A/B tests in the Contributor interface is similar to editing in Web mode. What’s different between Web mode and A/B test pages is that you can make page changes to a test layer of variants instead of on the site pages; you’ll also see an A/B Test panel displaying on the right.
You can enable A/B test mode from the Contributor Web view by opening the browser page or mobile page on which to add a test; then click the A/B test icon in the upper right of the menu bar. (If you like to find the number of tests that exist for this page, you can hover over the icon.)
If A/B testing is not enabled for a page, you can still create an A/B test for that page; however, you’ll get a warning message.
If an A/B test is not added to a page, you see a control on the right panel with the text “Create a test”.
If one or more A/B tests already exist for a page, the panel at right shows the A/B Test controls and an option to change to the Criteria controls:
You can use the A/B Test controls to create and edit tests for a page, and to track changes that you make to a page’s variants.
You can use the Criteria controls to set the criteria for the selected test, such as its start and end determinants, which conversion to measure, and target and confidence information.
The controls in the toolbar also change when in A/B Test mode:
The toolbar label changes to A/B Test.
If the test has not yet been published, a Save icon and a Change Page Layout icon are available.
An Approve icon and a Delete icon are always available.
There are no Edit, Preview, Checkin/Checkout, or Reports icons.
You can create A/B tests, make copies of them, and select existing tests. You must perform these tasks in A/B Test Mode.
How do I create A/B test for a page?
In the list box containing either the text "Create a test" or a list of existing tests, click + (plus) button.
In the Create New Test window, enter a name for the test, then click Submit.
In the A/B Test toolbar, click Save .
How do I copy A/B tests?
Click + (plus) button.
In the Create New Test window, select a test to copy. In the top field, you can change the default name assigned.
Change the default name assigned.
In the A/B Test toolbar, click Save.
How do I select A/B tests?
Open the drop-down list of existing tests and select the one you want.
After creating, copying, or selecting a test, you can work with its variants (described in How to add, select, and edit A/B test variants?) and select its criteria. The criteria you select applies to all variants of the test.
You can set up A/B test variants by adding, selecting, deleting, or editing them, or changing their page layout.
To set up A/B test variants, see these topics:
You can define the A/B test by selecting a variant and editing its webpage. Use the color-coding and numbering to identify the variant and its changes.
To add, select, and edit test variants:
Below the test description field, variants of the current webpage are listed. Initially, there’s the base webpage A and a single variant of this webpage B. You can add further variants of the base webpage, which becomes C, D, E, and so on.
A, the original webpage, is the control.
The selected variant has its letter displayed in a color-coded circle. Clicking B displays its green circle, clicking C displays its yellow circle, and so on. As you modify the selected variant's webpage, circles of the same color display on the webpage, with numbers to indicate the changes.
Changes made to a variant are listed in the Tracked Changes section. The change numbers correspond to the color-coded numbers on the webpage.
In addition to a reference-only name, each variant is identified by:
Letter. The B variant is already created and selected, ready for you to begin modifying it.
Name. The name of the base webpage is initially "Base", and the names of the variants are initially "B", "C", "D", and so on. You can change these names at any time.
Color-coded circle indicates the selected variant. For example, clicking B displays its green circle, clicking C displays its yellow circle, and so on. As you modify the selected variant's webpage, circles of the same color but with numbers display on the webpage.
Numbered changes. These display in the Tracked Changes section. Changes are assigned numbers that correspond to the color-coded numbers on the webpage.
To select a variant, click its letter (for example, B). A circle in its assigned color displays.
To add a variant, click the + (plus) button below the last variant. A new variant is added and selected, and a circle in its assigned color displays. You can enter a name for the variant at any time. Your system may be configured to automatically save the variant at this point. If not, before switching to another variant, you must save the current variant, by clicking the Save icon on the toolbar.
You can use the search facility to find assets to include on a variant page. The search results panel will temporarily cover the A/B test panel. Move assets onto the variant page by dragging and dropping from the search results panel. Close the search panel to reveal the A/B test panel again.
You can also edit or delete the A/B test variants. See How to change the page layout of an A/B test variant?
As you make changes, they are listed, described, and numbered in the Tracked Changes section. Their numbers correspond to the numbers displayed in color-coded circles on the webpage. Changes you have made but not saved are listed in the Unsaved Changes section. As you move the mouse over each item in the Tracked Changes section, that item is highlighted on the webpage.
You can click the (Hide)/(Show) control to switch between hiding and showing the numbered circles on the webpage. You can also drag and drop the numbered circles to reposition them if they block your webpage view.
If you like, you can change the page layout of your A/B test variant to be different from the page layout used for the control or other variants.
To change the page layout of an A/B test variant:
Figure 11-4 Change Page Layout Window
After you select a page layout for the test variant, the dialog reloads showing Cancel and Apply buttons.
Setting up A/B test criteria involves number of tasks including specifying the start and end date of the test, the confidence level, the goal to track, and specifying visitors or segments to target.
To set up A/B test criteria, see these topics:
When creating a test, you can specify a start and end date for it. If you don’t specify a start date, the test starts as soon as it’s published. Once a test starts running, you can view its current results, stop it early, and promote its winner.
See, How do I view A/B test results? and How do I promote a test variant to the control (base) webpage?. You can’t edit a test after it’s published.
To specify the start and end date of an A/B test:
To start the test when A/B test assets are approved, leave the start field blank. The test starts after it’s published, as described in How do I approve and publish A/B tests?.
To start the test on a certain date, click the calendar next to the Start field and select a date from the calendar picker. By default, the start time is set to the current time. (If you select the current date and time, the test starts upon publish.) You can use the HH, MM, and SS fields to specify another start time, in which case select the date again to apply the new time. You can also enter a new value for the time by overwriting the one currently shown. Your server setup determines whether the start time uses a 12 or 24 hour format. If set for a 12 hour format, enter AM or PM.
When creating a test, you can select a confidence level for its results. This number determines the confidence in the significance of the test results, specifically that conversion differences are caused by variant differences themselves rather than random visitor variations. The larger the number of A/B test visitors, the easier it is to determine statistical significance in variant differences.
If you set a confidence level, the A/B test continues until that confidence level is reached, even beyond the specified end point for the test.
To select a test confidence level:
The confidence level is calculated using the Wald method for a binomial distribution. This method consists of two separate calculations. The first calculation determines the confidence interval. The second calculation determines the Z-score, which measures how accurate the results are. For A/B testing, the confidence interval for each variant is calculated by the number of visitors (who perform a specified action) divided by the number of visitors to that page variant.
In the Wald Method , conversion rate is represented by p.
Figure 11-5 Wald Method of Determining Confidence Intervals for Binomial Distributions
The results for each variant are then used to calculate the Z-Score. The Z-score measures how accurate results are. A common Z-Score (confidence range) used in A/B Testing is a 3% range from the final score. However, this is only a common use, and any range can be used. The range is then determined for the conversion rate (p) by multiplying the standard error with that percentile range of standard normal distribution.
At this point the results must be determined to be significant; that is, that conversion rates are not different based on random variations. The Z-Score is calculated in this way:
Figure 11-6 Z-Score Calculation
The Z-Score is the number of positive standard deviation values between the control and the test mean values. Using the previous standard confidence interval, a statistical significance of 95% is determined when the view event count is greater than 1000 and that the Z-Score probability is either greater than 95% or less than 5%.
Marketers are required to create and use custom goals in Google Analytics for A/B testing. These goals must be created before setting up A/B tests. You can create goals of your own, or you can use goals created by others.
To specify the goal to track:
Goals are listed by name and type. If you like, you can click Sort to change their sort order.
You can define your A/B test to target only a certain percentage of all visitors in a segment or the percentage of visitors in a segment. For example, you might target the test to 50% of all visitors or to 50% of visitors in a segment of visitors 65 years or older.
See Creating Segments.
Within the specified percent, test variants display in equal percentages to the target visitors. For example, if a test targeting 30% of visitors include two variants (B and C) in addition to the control (A), 10% of visitors would be shown control A, 10% variant B, and 10% variant C.
Click Select Segments that displays.
In the Target field in the Segments window, enter the percentage of visitors in selected segments to include in the test.
From the Segment Name column, select one or more segments. Click Sort to sort the segments list by name or modification date. To deselect a segment, click its x in the Selected Segments column.
Click Select. Selected segment names display under the Select Segments button.
You can review your A/B test results in the A/B test report. You can decide whether to promote one of the variant pages you tested to the active website.
To use A/B test results see these topics:
Once the test begins, whenever visitors view the control (A), or variant versions of the webpage, their site visit information is captured and these statistics become available in the A/B Test report. This means, that for completed tests, you can compare the relative performance of the base and variant webpage designs. You can then use this information to determine whether or not to promote one of the variants to become the webpage that visitors see.
To view the A/B Test report in the Contributor interface:
For each test in progress, a search result box is shown containing a chart and additional information. This shows the latest results for the test, but is not the full report.
|Stop (red)||In progress||Click to stop the test. You can’t restart the test after it’s been stopped.|
|Edit (green)||Complete but not published||Click to edit the test variants. You can’t edit a test once it’s published.|
|Promote (green)||Published||Click to see the test results in the report and decide if you want to promote a variant to be the page that's displayed to visitors. See How do I promote a test variant to the control (base) webpage?|
Once you’ve completed creating and editing your test's variants and specified its criteria, you’re ready to approve the test for publishing to the delivery system. The test is created in Google Analytics during approval. Once the approval process is complete, you can log into Google and see the experiment and all its settings.
Consider the following:
Approving the test approves both the test and its variants. As with all webpage approvals, you must approve an asset and all of its dependencies.
The test must include a goal before you can approve it.
A/B tests are published to a destination that you select. Before a destination is available to select, it too must be published. If there are no published destinations available, consult your administrator.
Upon publishing, the test begins at the date and time you specified in the test's criteria. If that date and time has already passed, the test starts immediately.
To approve a test's assets for publishing and to start the test:
You can view the A/B test report from the Contributor interface. The report provides the data to compare the relative performance of the test variants webpage designs to the control (base web page). You can then use this information to decide whether or not to promote the winning test variant to the control webpage that visitors see.
The A/B test report summarizes the data in a number of different ways to help you make important marketing decisions:
The report status and action button are displayed in the header.. The example below shows a report that is currently in progress and therefore has the action option of Stop.
Figure 11-7 Summary
Shows basic information about the A/B test, such as, webpage name, owner, and description.
Displays the type of conversion that the test is monitoring.
Describes the segmentation if the test includes a segmented user base. All means the user base is not segmented.
Displays when and how the test is set to stop.
Shows the conversion rate for each variant.
Metrics Bar Section
Figure 11-8 Metrics Bar
Shows the target percentage of all visitors, the end date, and the confidence percentage for this test.
Summary to date
Shows the number of visitors served, the number of conversions, and the confidence percentage reached so far, for all variants combined. If this test allows one variant to be identified as better than the others, this is shown as the winner, when the test is complete.
Figure 11-9 Conversions
Normally set to Device.
Conversion information for each variant.
Figure 11-10 Confidence
The chart shows the number of visitors, the number of conversions, the conversion percentage, the Z-score (see How is the A/B test confidence level calculated?), and the confidence percentage.
Once you’ve finished reviewing your A/B test results, you may decide to promote a particular test variant. This action permanently replaces the control (Base webpage) with the winning variant and displays it to all visitors.
To promote the winning test variant:
Note:Even though you’ve now promoted your winning test variant, it won’t display on your website until it’s published. See How do I approve and publish A/B tests?
Once an A/B test is deleted, the reports generated for that test are also deleted but not the data. If you want to re-use this data, you can create your own reports using the Oracle Endeca Information Discovery (EID) system. A warning message appears if you attempt to delete an A/B test that is currently running.
Note:The experiment that’s created in Google also remains even though the A/B test has been deleted. If you want to delete this experiment, you’ll have go to Google Analytics interface and delete it.
To delete an A/B test, click the Delete option in A/B test toolbar, select the assets you want to delete, and click Delete. If the A/B test has been published, you’ll get a warning message.