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Why is A/B Testing Important in a Sales Funnel?

In today’s digital world, there are so many elements that can affect the performance of your online course’s sales funnel. It requires a detailed understanding of your target audience, creating content that resonates with their needs, designing a recognizable brand image, offering valuable lead magnets, a good email marketing strategy… the list goes on. And now we bring your attention to A/B testing.

This is the backend portion of your online course’s marketing plan, and it is exceptionally significant. So read on, dear creator, and you will gain an understanding of how this key part of marketing can optimize and scale the success of your online course.

What is A/B Testing?

Also referred to as “split testing”, this refers to a systematic method of comparing the performance of two versions of something. In marketing, this can be done with web pages, landing pages, emails (subject lines), ads, or any other marketing asset used in the sales funnel, to see which resonates with the audience. The two variations, A and B, are subjected to a controlled experiment against each other – A versus B – hence, A/B testing.

This is the basis of any controlled experiment. The general idea is that you have one variable that you change at a time during an experiment, where all the other variables remain constant, in order to achieve an accurate understanding of the outcome. We will expand on this in more detail further in this article. So let’s explore how this experiment works in different aspects of your sales funnel when promoting your online course.

The Significance of A/B Testing for Sales Optimization

A/B testing is a crucial tool that can be leveraged to optimize your sales funnel. With this method, you can make data-driven decisions, changes, and scale the effectiveness of your marketing campaign for your online course. Something that sounds good to you may not resonate as well with your audience, and it is easier to take the guesswork out by comparing your efforts side by side with the help of marketing tools to track success rates. This way you can monitor your progress from the backend, identify what works best for your target audience, and continually improve your conversion rates.

Methods to Maximize Your Efforts

Step 1: Hypothesis – Clearly define the goal you want to achieve and the variable you will test.

Step 2: Testing – Isolate the element you are testing, for effective results, you must test one at a time.

Step 3: Timing – Tests should be run for a period of time long enough to achieve statistically significant results.

Step 4: Segment Audience – Certain tests should be performed within the different demographics within your audience, some changes can be effective for one group and not another based on their pain points or interests.

Step 5: Document and Analyze – Keep records of the tests and their outcomes, this will help you make informed data driven decisions to optimize and scale your sales funnel.

We’re going to give some examples of how A/B testing can be used within subsets of your sales funnel, and how to go about each one.

  1. Email Marketing Campaigns – testing subject lines 

For this test, you will compare two subject lines for the same introductory email.

Protocol: 

  1. Develop two distinct subject lines
    1. (A): “Unlock Your Marketing Potential” vs (B): “Learn Digital Marketing from Scratch”
  2. Run (A) on one segment of your email list while simultaneously running (B) on another segment of your email list
  3. Monitor open rates, click-through rates, and conversion rates
  4. Use the winning subject line for the remaining audience

Pro Tip: Test the timing of your emails. Schedule your emails with different send times to determine when your audience is most responsive.

What Not To Do: Don’t change too many variables when running A/B testing, as it can make it challenging to determine what affects the changes in performance (in this example, change the subject line, but keep the content and CTA the same in both (A) and (B) tests).

Timing for Significance: This is dependent on the size of your email list. The statistical significance of a large email list can be determined within a few days, the timing would be extended for smaller email lists. 

  1. Paid Ads

For this test, you will compare the success of two paid ads with the same message. You can compare the success rate by keeping the same copy and changing the main image, and vice versa. You can also hone down which of the four possible combinations generates the most interest based on clicks and conversions.

Protocol:

  1. Create two versions of your ad, changing ad copy with the same image, or same ad copy with different images between (A) and (B).
  2. Run both ads with the same audience targeting.
  3. Monitor click-through rates (CTR), conversion rates, and return on ad spend (ROAS).
  4. Allocate the budget to the higher-performing ad

Pro Tip: Stay consistent when monitoring ad performance, and refresh your A/B tests periodically to stay ahead of ad fatigue (when your audience sees the same ad so much they lose interest, CTR and ROI both go down).

What Not To Do: *If you have too small a budget, A/B testing may not produce statistically significant results – you may need the aid of marketing experts to guide you cost-effectively.*

Timing for Significance: This is the tricky one because A/B testing for paid ads is affected by many factors including budget, number of clicks daily, and the size of the audience. 

  1. Sales Page Layout

For this test, you will be trying two different layouts on your online course sales page.

Protocol:

  1. Create two distinct versions of your sales page layout.
  2. Split traffic evenly between the two variations.
  3. Monitor conversion rates, bounce rates, and time spent on each page.
  4. Implement the layout that generates more conversions.

Pro Tip: You can test other elements on the sales page further, like headlines, length of copy, and visual elements to further optimize it for future visitors.

What Not To Do: Don’t rush to implement changes if you see initial differences in conversion rates. Make sure that the test runs long enough to be statistically significant.

Timing for Significance: Testing sales page layout changes may take a few weeks or longer, depending on website traffic and volume of landing page visitors.

Where to Find Metrics for Targeted Marketing Efforts

  1. Google Analytics: Track website performance, user behavior, and conversion rates.
  1. Email Marketing Platforms: Track email open rates, click-through rates, and subscriber behavior (you can try MailChimp or similar platforms to achieve this data).
  1. Social Media Ad Platforms: Track ad performance, click-through rates, impressions, and conversion metrics (Facebook/Instagram Ads (Meta), Google ads).
  1. A/B Testing Tools: Set up experiments to gather useful data on website variations using tools such as Google Optimize.

Final Thoughts

Thank you for reading through this article, dear creator! As you can see, A/B testing is a powerful tool for optimizing your sales funnel to increase conversion rates from target audience to student sign up for your online course. By continuous monitoring, testing, and analysis, over time you can begin to see a rise in your return on investment (ROI). This can be tedious and time consuming, and if you need a team you can schedule a call with us at CourseCamp. We are experts in digital marketing, A/B testing, data analysis, and optimization using the smartest tools available.

Are you ready to market your online course? Let us take care of that for you, your students are waiting!