Sample Scenario:

• You want to add a page with a video or demo to your web site.
• You believe that it is important for visitors to view as much as possible of the video.
• You have 2 videos to test against each other, and your test goal is to determine which one of your videos keeps more visitors watching (engaged) for at least a certain period of time and plan to  keep the one that engages viewers more than the other.

In this scenario, since your test goal is to determine which video users are more engaged in watching for at least a certain period of time you will therefore want a conversion to trigger and to be recorded in GWO after that set period of predetermined minimum viewing time.

Back to the Scenario:

• You want a conversion to trigger if a visitor in the experiment views at least 20 seconds of your 1 minute videos.

This is easily done by using Time On Page as your conversion goal. What basically happens is that if the visitor stays on the page or in this scenario views the video for that certain period of time that you have predetermined; the setTimeout JavaScript function (in the script given further down in the article) executes the script that reports a conversion in Google Website Optimizer.

To do this, use this time on page goal tracking script and place it at the bottom of the page that has your control script and test page tracking script. Change the red bold faced font in the script below to the amount of time you want the visitor to have reached before a conversion is triggered. The number is represented in milliseconds where 1000 milliseconds equal 1 second – so in this scenario since we want 20 seconds of viewing time to trigger a conversion, simple multiplication of 1000 milliseconds x 20 = 20000. Therefore if the viewer views the video for AT LEAST 20 seconds a conversion is recorded. You will replace the xxxx’s in blue below with the data given to you in the control script and test page tracking script (it references your GWO account and this test).

<script>
if(typeof(_gat)!=’object’)document.write(’<sc’+'ript src=”http’+ (document.location.protocol==’https:’?’s://ssl’:'://www’)+ ‘.google-analytics.com/ga.js”></sc’+'ript>’)
</script>

<script>
setTimeout(’_gat._getTracker(”UA-xxxxx-x“)._trackPageView(”/xxxxxxxxxx/goal”)’, 20000);
</script>

Note: Since the conversion is triggered in this instance by a viewer who stays on your page for a certain minimum period of time, and if your page has more than just the video (such as news, photos, or articles) and a visitor stays for the minimum period of time doing other “things”, the conversion will still trigger. You either need to set your test to be just for the video (requiring a more advanced setup), or have nothing else on the page but the video – and by nothing else I am referring to other pieces of content that could potentially keep people on that page doing something other than viewing the video.

Related posts:

1. How to Interpret the Estimated Conversion Rate Range in Google Website Optimizer Interpreting the Estimated Conversion Rate Range Properly in GWO is...
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• Ability to add notes to tests – it would useful to be able to make notes about tests within the test admin itself for each test. Many times we have multiple people looking at a test and I would like to leave comments and get responses within the test, not only for ease but for permanent documentation

Today, Trevor Claiborne of the Google Website Optimizer team posted on the GWO Blog that they have added the ability to add notes to your GWO experiments. I am so very excited as this should really help out with collaboration and to help others who like to look at the current status of tests we have live as even the documentation for past tests. Although I already have a reliable system for test documentation this should help decrease the amount of cross-referencing and multiple email responses to others (I hope!) that now can easily be documented within the test itself.

Thank you Google Website Optimizer team!

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Google Analytics by default captures data just as it appears in a visitors browser. This being so, you can have two or more URLs that are the same but appear as separate entries due to different cases in the URLs, both lowercase and uppercase.

For Example:

• /coffee/flavorA/index.html
• /coffee/flavorA/INDEX.HTML

Although both will take you to the same page if you were to put them into your browser, Google Analytics will create separate line entries for each one leaving you to have to merge the two to get to the complete data for that URL. Imagine the pain if you had 100s, 1000s or more pages on your website with multiple line entries for the same URL.

However, Google Analytics allows you to apply a Custom Filter to remedy this problem. You can associate this filter on a profile by profile basis in case you have certain profiles that you do not want this applied to. One thing worth noting is that this won’t “fix” past data already in your reports, but anything going forward it will put into one line entry (either lowercase or uppercase depending on how you setup the Custom Filter.

Here are the easy steps to create the custom filter that will force the Request URI to lowercase or uppercase:

1. Add a new filter to your profile and use the following values:
2. Filter Name: Name Your Filter
3. Filter Type: Custom filter
4. Select “Lowercase” (or “Uppercase” if you want to force the URLs to Uppercase)
5. Filter Field: Request URI
6. Select which websites you want to apply the filter too and select Add
7. Save Changes

Here is a screenshot of how it will look:

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It’s easy to get initially excited when you see that one of your test panels in Google Website Optimizer has a higher Estimated Conversion Rate than that of your control panel as presented on the Combination Report page. This may even lead to you believe you can end the test (prematurely I may add).

Unfortunately, just looking at the Conversion Rate number given to you by Google in the bold type font isn’t enough, you most certainly need to do a little bit of visualization to really have a better understanding of what is going on and how they are performing against each other.

Whether you are running an A/B test or a multivariate test, this is important to know for either – the number they give you is a conversion RATE RANGE. Many people mistakenly look at just the number given and do not visualize the full conversion range given along with it (done so with simple addition and subtraction of the number given next to the estimated conversion rate after  the plus and minus sign). This range is based on the observed conversion rate of during the experiment thus far. Not factoring this in can lead to many people ending or wanting to end tests before they are truly ready to be ended. For example,

Estimated Conversion Rate

• Test Panel – 6.0% +/- 1.0%
• Control Panel – 5.5% +/- 1.0%

Reading and interpreting this correctly would actually tell you that the:

• Test Panel is converting in the range of 5.0% to 7.0%, and the
• Control Panel is converting in the range of 4.5% to 6.5%

This being true, their conversion rate ranges are overlapping each other.  Visualizing this information shows you the overlap much more clearly as shown below:

So, is the test panel really converting better than the control? Potentially the control panel could still be converting better – it’s not clear enough to make a decisive or correct decision at this point.

Now think of a scenario such as two panels having a higher conversion rate and much larger conversion rate range:

• Test – 55.0% +/- 8.0% (conversion rate range of 47.0% to 63.0%)
• Control – 52.0% +/- 7.0% (conversion rate range of 45.0% to 59.0%)

Some may feel that the test panel is the winner and so it’s ok to stop the test and roll out with the test panel…BUT…

As the test goes on for a longer period of time, you should find the conversion rate range getting “tighter” and the above test potentially ending in such as a way as:

• Control panel converting at 58% +/- 1 .0%, and the
• Test panel converting at 47.0% +/- 1.0%.

In this ending scenario the control panel is actually the winner not the test panel as we saw mistakenly earlier on. Ending the test too early certainly changes the story drastically as does the results could differ as well!

I know sometimes it’s exciting to see your test panel winning, but patience is certainly of high importance in A/B and multivariate testing in order to make sure your tests and results are always moving forward correctly.

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Please make sure that when you fill out the calculator with your test information that you then click on the calculate results button and look below it in the results section to see if you have a winner or not.

If the A/B calculator says that you have a winner, you can determine the winner very easily by just looking at the conversion rates of  each of the panels and the one with the better conversion rate is your winner. If the A/B split test calculator results declare that you don’t have a winner then you will need to run your test longer, or run a different test.

You can try out my online A/B split test calculator at www.SplitTestCalculator.com .

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