Top 5 Features of Google Analytics Content Experiments (vs. Website Optimizer)

For about 5 years now, marketing professionals have been using Google’s Website Optimizer to run A/B tests and Multivariate tests on webpages. Google recently announced that Website Optimizer will be replaced with Content Experiments. Content Experiments offers similar functionality as Website Optimizer with a few limitations; however, I’ll highlight the top features that I think Content Experiments offers. Here are my top 5 features for Content Experiments when compared to Web Site Optimizer:

1. Experiment Integration within Google Analytics

Content Experiment’s integration within Google Analytics is much improved compared to Web Site Optimizer. Web Site Optimizer did not integrate with Google Analytics, which limited a user’s ability to obtain additional information about the test variations for each experiment such as time on site, bounce rate, or the possibility of segmentation.

2. Simplified Workflow with the Set-up Wizard

The simplistic workflow to implement an experiment is streamlined as well. The process went from 5 basic steps to 4 basic steps. The set-up wizard for the experiment clearly identifies where you are within the set-up process and the next steps. In addition, there are icons to help you throughout the process to understand what you’re doing.

3. Visuals of the Experiments within the Console

The simple workflow is enhanced with visuals of the experiment variations, which was not part of Web Site Optimizer. Within the console of Google Analytics Content Experiments, you can see exactly what your original design vs. the variation(s) will look like prior to launching the experiments.

4. Better, More Simplified Reporting

In my opinion, the reporting in Content Experiments is much better than before. Content Experiments provides high-level experiment detail at a glance (visits, days of data, status of the experiment, and percentage of included visitors). The conversion data is also much improved by providing separate columns for visits, conversions, conversion rates, and basic green & red arrows to compare the variation(s) to the original page. Finally, the look of the reports is now more consistent with the newer Google Analytics interface.

5. Rewrite the Variation URLs to the Original within GA Content Reports

By selecting to rewrite the URL variations, you can consolidate all of the traffic to your original and variation pages. These URLs will appear under the original page within your Content Reports. This ability makes the Content Reports easier to read and streamlines the analysis of the experiment’s impact on page metrics in addition to its data. This provides increased functionality with custom reporting and experiment segmentation.

What’s the BIG Deal with Content Experiments?

The simplified shift from Web Optimizer to Content Experiments will save companies and marketers’ time, money, and allow them to easily create testing experiments. Ideally, Content Experiments will reduce the amount of time to create experiments and simplify their data, making them easier to understand as well as more actionable. With more actionable information, companies and marketers should be able to improve their users’ online experience and generate higher conversions.

Get off the excuse bandwagon! Start experimenting for better lead generation and online sales, what are you waiting for? Leave your feedback on Content Experiments in the Comments section below!

A/B Testing (Split Testing) to Convert More Online Customers

What is A/B testing (split testing)?

A/B testing, or split testing, is a marketing testing method by which one baseline control sample is compared to a variety of single-variable test samples in order to improve response or conversion rates. An example would be to test two different subject lines of an email campaign. A/B testing has been implemented for direct mail and within the interactive space to test tactics such as banner ads, emails, landing pages, or even entire websites to improve performance. You can also extend A/B testing to PPC advertising copy, alternative keywords, or PPC keyword match types.

How can my business or marketing department apply A/B testing?

You should always be testing ways to improve the sales process to reduce your cost per acquisition and to improve your customers’ experiences. I would recommend that you start with the “low hanging fruit” that could have the greatest impact on revenue or the customer experience. For example, an A/B test could be a simple as testing the color of the calls-to-action to improve the click-through-rate.

A/B testing can be applied to marketing tactics to improve sales or lead generation at a lower cost. In general, it’s easier to implement A/B testing with digital advertising because of the ability to make changes quickly and optimize the process. The findings from digital advertising can also be carried over to traditional advertising.

A/B Testing Best Practices

If you’re new to A/B/ split testing, here are a few best practices:

Define your goals. Clearly state what you hope to accomplish.

Determine how you plan to accomplish your goals. Keep it Simple!

What are you testing & why?

What is the variation you are testing (color, position, ad copy, etc.)?

Define the control and your variation for testing.

What are your expected results & reasoning?

Measure & Analyze the results through the sales or conversion process.

How do you apply A/B Testing to Internet Marketing Strategy?

You can leverage A/B testing based on geography, psychographics, customer lifecycles, etc. You want to develop realistic goals based on your target audiences. At first, I would recommend being targeted with your approach and limiting the test to a single market. It’s important to identify the greatest impact on the conversion process or sales process by modifying the internet marketing strategy slightly. Be sure to focus on all the results from the beginning of the process to completion and to communicate the results. Think about the effect the testing will have on saving time, money, and creating efficiencies.

Conclusion

If you haven’t started A/B testing, you’re wasting time, money, and missing opportunities. As marketers, we should always be testing to maximize performance and to reduce costs. Testing goes beyond just a subject line or ad copy. It requires focus, consistency, and planning. In addition, you must think about usability, branding, layouts, the purchase process, etc. There are numerous tools such as Web Optimizer, Visual Website Optimizer, or Test &Target to get started.