How To Use Google Analytics Content Experiments to Optimize Your Sales Funnel

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The ultimate goal of any business website is to bring more sales and leads through different mechanisms. Companies use customized squeeze pages, banners, posts and sign up forms to grab leads for building a list of potential buyers. These prospects go through various on-site or off-site sales funnels where companies try to convert them into buying customers. But, the big question is - How effective is your lead acquisition and sales funnel system? If you're not getting the desired results from your website-based sales funnel, probably it's time to rethink your entire approach towards designing and hosting these revenue generation channels. Today, we're going to discuss about a powerful feature available in Google Analytics that lets publishers optimize their websites to get more engaged traffic that actually shows interest in your offerings. 'Content Experiments' lets you do rigorous testing of your on-site marketing channels for creating an optimized system with high conversion rate.

Decide - What to Test?

Depending on the type of lead acquisition system deployed on your website, you may need to define the boundaries for testing. The first obvious step involves selection of web pages that needs to be tested. The type and number of web pages involved in your testing can differ depending on the type of sales funnel on your website. These pages can be broadly divided into 3 categories.
  • A single landing page - This is the simplest form of online marketing existent from over a decade. You have a single page with all sales pitches, call-to-actions, testimonials, sign up forms and purchase payment gateways. If you've deployed a similar system consisting a single entry and exit point, this is only page you need to test for better conversions.
  • Intermediate page within a long sales funnel - Modern online marketing systems involves multiple pages with each one having a different goal to allure or persuade a potential buyer. In such cases, you may consider testing each and every page throughout the ladder up towards the final page.
  • A final goal page - Sometimes, marketers is more interested in optimizing the goal page that indicates a successful conversion. This indicates the all the pages prior to the goal page are well optimized, but traffic is bouncing right on the final goal page.
Carefully study your entire lead generation system and make a list of web pages that need to be optimized. Again you can take help of your web analytics system to identify non-performing pages. Some of the general techniques and metrics to identify these pages are high bounce rate, high exit rate, low CTR and so on.

Create Multiple Versions for Candidate Web Pages

Now that you've identified and created a list of candidate web pages that need to be tested and optimized, the next step involves a time-consuming process of creating multiple test pages against each original page. This is the crux of successfully using content experiments feature with Google Analytics. You aim is to provide different versions of pages to incoming traffic on a random basis and then analyzing the results for identifying the best performing test page. Here are some of the guidelines to create different test pages.
  • Use different graphics - Large graphics are a perfect fit for squeeze pages. You must design alluring graphics for each and every test page to grab visitors' attention. Make sure you optimize these images before uploading them to the server.
  • Change layout - When we're talking about change in layout, it involves change in the dimensions of different sections as well as change in position of these sections. This helps in identifying the right layout combination as the test progresses.
  • Change call-to-action elements - Creating multiple and unique call-to-actions for each test page is critical for successfully testing and optimizing your sales funnel. You must ensure that each experimental call-to-action element doesn't deviate from the original page goal.
  • Keep striking difference in each page - Sometimes newbies make the mistake of designing almost identical test pages with little variations. This dilutes your testing efforts as visitors get almost the same experience with each test page. You must ensure that each test page is completely unique and different from its competitors.
You must add rel="canonical" attribute to each test page to indicate it as the same version of the original page. This prevents any duplicate content penalty from search engines while your test is in progress. Make sure each test page has a unique URL and is publicly accessible.

Create Content Experiment

You can access this excellent testing feature from within your Google Analytics dashboard through Standard Reporting → Content → Experiments. Start by providing the URL of the original web page currently visible to general traffic. This is the primary web page you're interested in optimizing. Give an appropriate name to the content experiment such that you can easily identify its purpose.

Provide original and test pages -
The next step involves providing URLs of test pages and giving them appropriate name. Giving them a proper name helps in analyzing the reports correctly.

Experiment pages in step 1

In the example shown above, I've taken two pages. The first one is the original page that needs to be optimized and second one is the test page that will be tested against the original one. Now, taking a single test page makes no sense. You must take a minimum of two test pages to get the best possible results. You can provide up to 5 test pages for a single experiment. I'm again stressing on giving a proper and meaningful name to each test page during this step as it greatly helps in properly interpreting the reports.

Specify experiment options - The next important step involves provision of several key options that decides the way your content experiment will be conducted. You must carefully go through each of these options and must provide the appropriate data so that the entire testing exercise is conducted as per your requirements.

First, select the experiment's objective or the key metric that defines the primary purpose of creating the content experiment. Create or select an existing goal that is made specifically for your test so that you can measure the outcomes against each test page in the detailed reports. If you're not familiar with goal creation in Google Analytics, here's a handy guide for the same written specifically for creating critical goals essential for sales funnel optimization.

Content experiment options

The second important information you need to provide is the percentage of overall traffic you want to be included in the content experiment. How to decide this percentage? Well, such type of tests needs a decent traffic data set to come up with analyzable trends and deviations. If your website has low to medium traffic, you must choose 100% else websites having massive amount of traffic must cut down the percentage to limit the volume of traffic necessary for coming up with some concrete test data.

Another important option present on this page often creates confusion for newbies. Rewriting of variation URLs within content reports is often not understood by many webmasters. Well, activating this option doesn't affect your content experiment reports at all. It only combines the pageviews of test pages within the original page pageviews in the general content reports in your dashboard. It's up to you whether you want to see them in aggregated form in general content reports or not.

Install Experiment Code - Now that you've configured all the necessary options, it's time to install the content experiment code on your website. You must also ensure that all the test pages are live and are accessible by everyone once the code is installed and is in the active state.

To install the code, copy and paste it after the <head> tag of the original page. Be very sure that this code only appears within the original web page that is being tested. This code shouldn't be present in any other web page on your website.

Content experiment code

Once the code is installed, publish the test pages and check whether they're accessible without any glitch. If you cannot directly access your web server and it is maintained by somebody else, you can choose the second option for code installation. This option instantly creates an email with shareable link to access the install code. Simply provide the email address of your webmaster and it will be mailed to him for installing the code at the appropriate location within the original web page.

Remember, each content experiment runs for a maximum of three months. If you want to extend the period, you must create a fresh one for extension of three more months. It is always advisable to save (export) the old data before creating a new content experiment made solely for the purpose of extending the test time period.

Analyze and Optimize

This entire experiment creation exercise is worthless until you religiously dig in the reports and take appropriate optimization steps to come up with the right combination of improved sales pages. Fortunately, content experiment reports are extremely user friendly without any ambiguous or highly technical terms. Let's dissect these reports to learn identifying the most performing test page.

Content experiment report

The three most important columns that need your attention are Conversion Rate, Compare to Original and Probability of Outperforming Original. Let's take a look at each of these columns and try to understand how they help in correctly interpreting the report.
  • Conversion Rate - This is pretty simple and straightforward. It gives the ratio of total visits versus goal completion. In simple words, this column tells you the percentage of goal completion against the total number of unique visits. The higher the conversion rate, the better is your test page.
  • Compare to original - This useful column displays the deviation of conversion rates compared to the conversion rate of the original web page. Conversion rate higher than the original page is shown as positive deviation with green arrow and conversion rate lower than the original page is shown as negative deviation with red arrow.
  • Probability of outperforming original - And last but not the least is an important decision making column that provides you with the probability about how much a specific test page is successful against the original page. The higher this percentage, the stronger is the candidate (test page) for replacing the original web page.
While the experiment is in active state, you can also disable inclusion of a specific test page such that it is not included in the test anymore. You can also change the percentage of traffic to be included in the content experiment. This flexibility allows you to change key parameters during the course of experiment. The effectiveness of these reports is entirely dependent on your intent of applying the positive changes as per the results. I wish you good luck with your content experiment efforts and hope you'll be able to lift up your sales through this excellent testing mechanism.


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