Should we add more than one CTA button?<\/em><\/p>\n\n\n\n Should we include a name in the email subject line?<\/em><\/p>\n\n\n\n There are dozens of decisions to be made for each marketing asset that\u2019s created. Small decisions can have a big impact on whether the asset converts.<\/p>\n\n\n\n How do you decide what to do? Go with your gut? Do whatever the CEO says without any pushback? Use your best guess based on some quick wins in the past?<\/p>\n\n\n\n Allow me to introduce you to A\/B testing.<\/p>\n\n\n\n What is A\/B testing? <\/p>\n\n\n\n In a nutshell, A\/B testing (also called split testing) is creating two different versions of a marketing asset (landing pages, subject lines, CTA buttons, multistep forms<\/a>, exit pop-ups, click-through rate, etc.) and exposing it to two different audience segments.<\/p>\n\n\n\n After a specified amount of time, you\u2019ll analyze the test results<\/a> of the different segments to assess which were most successful in increasing your chosen business metric.<\/p>\n\n\n\n A\/B testing is helpful in a variety of ways:<\/p>\n\n\n\n It gives you data for the \u201cwhy\u201d of your content strategy:<\/strong> If your manager asks why you use short landing pages with three red CTA buttons, you\u2019ll have a proven data-based answer.<\/p>\n\n\n\n It increases your conversion rates:<\/strong> You\u2019ll increase sales by doing what works instead of wasting resources on what doesn\u2019t. Over the course of many A\/B tests, your marketing funnel will operate like a well-oiled machine with data-driven solutions<\/a> being deployed in the creation of all marketing assets.<\/p>\n\n\n\n It gives you useful data:<\/strong> Even if both your A and B tests fail at producing the desired result, you have examples of what didn\u2019t work and can focus your efforts on something else. So there\u2019s really no such thing as failure.<\/p>\n\n\n\n It helps optimize existing website traffic:<\/strong> If you are in the frustrating position of generating lots of traffic but only a small percentage is converting, A\/B testing will allow you to solve the mystery and turn traffic into sales<\/a>.<\/p>\n\n\n\n It gives user insight:<\/strong> Everything you learn from your A\/B tests can be used in your strategy moving forward to hone in on your target audience. From a website re-launch to new email campaigns, you\u2019ll understand what your target audience wants.<\/p>\n\n\n\n You can run A\/B tests on anything you\u2019d like, provided you have some variations to experiment with and a clear hypothesis.<\/p>\n\n\n\n Here are some of the most common areas that benefit from A\/B tests:<\/p>\n\n\n\n There are a few things you\u2019re going to want to be certain to establish when designing your test. It is worth noting that using A\/B testing tools<\/a> can make this process significantly more structured and efficient.<\/p>\n\n\n\n Establish your A\/B testing goals:<\/strong> What business objectives are you trying to achieve? Data for data\u2019s sake is cool and all, but you\u2019re trying to drive growth. Are you trying to increase website traffic? Convert traffic? Decrease bounce rate? Gain subscribers? Figure out what your objective is first.<\/p>\n\n\n\n Determine a hypothesis to test: <\/strong>This step is crucial. Based on what you know of current user behavior, what\u2019s your hypothesis about how to reach your goal? Clearly define your hypothesis and why you believe your proposed change(s) will make a difference.<\/p>\n\n\n\n Create test variations for your hypothesis:<\/strong> Next, select your variations. This can be the colors of a button, the placements of a layout, the length of a headline, or similar. Choose a variation on the testing elements you are designing your experiment around.<\/p>\n\n\n\n Establish a time frame for your test:<\/strong> Determine how long you want the test to run. It should be long enough to gather a sample size of statistical significance. The general rule of thumb is to run for at least two weeks but no longer than eight weeks.<\/p>\n\n\n\n Analyze your results: <\/strong>Go through your results with a fine-tuned comb. Of course, you\u2019ll be looking for statistically significant differences, but don\u2019t discount practical implications, especially if some of the segments don\u2019t behave in the way that you expect. Use your insights to refine the dimensions of future tests and to determine which variations should be adopted for long-term growth.<\/p>\n\n\n\n Document your findings:<\/strong> Whatever the results of your test, write them down.<\/em> Data lost is data that might as well never have been gathered. You don\u2019t want all your efforts to be lost in time or left unprovable when you are making a case for a change to be made. Additionally, don’t forget to safeguard your data. Options like AWS backup services<\/a> can securely store your data and ensure it remains accessible for future reference and analysis. Implementing AWS cost optimization<\/a> strategies can help you minimize the costs associated with storing and managing large backup volumes. To further protect and manage access, consider implementing AWS access governanc<\/a>e<\/a>, which helps control who can access your sensitive data and ensures compliance with security policies.<\/p>\n\n\n\n Adjust your marketing campaigns accordingly: <\/strong>Everything you discover in your A\/B testing won\u2019t do you any good if the data isn\u2019t integrated into your marketing plan. Use the data you collect as a blueprint to adjust your current marketing strategy and a foundation for designing those in the future.<\/p>\n\n\n\n Retest:<\/strong> Don\u2019t be afraid to test and test again! Something that was true for certain segments at a certain time may not be true a year later. COVID-19<\/a>, for instance, skewed many marketing metrics that many considered set in stone. Your audience will change with time, and your approach will also need to get the most reliable data.<\/p>\n\n\n\n Get outside help:<\/strong> Sometimes, you may need to consider outside help<\/a> when doing all of the designing and statistical analysis of A\/B testing. Hiring freelancers or agencies can be a big help with all the elements of your marketing strategy that require many A\/B tests, such as email campaigns. You can also use an employer of record<\/a> services to handle international hiring, payroll<\/a>, and compliance seamlessly.<\/p>\n\n\n\n A full-service email marketing agency<\/a> offers expertise in designing effective tests, analyzing results for actionable insights, and continuously refining email campaigns for maximum impact. A collaboration like this can take one element off your plate as you focus on your website\u2019s conversion rate.<\/p>\n\n\n\n Let\u2019s look at some variables that real companies have A\/B tested.<\/p>\n\n\n\n The clothing brand StudioSuits specializes in men\u2019s formal wear including shirts, pants, and men\u2019s linen suits<\/a>.<\/p>\n\n\n\n They wisely used A\/B testing during the holiday sales rush.<\/p>\n\n\n\n Before the holiday season was in full swing, their homepage prominently featured a heading focused on custom suits for men.<\/p>\n\n\n\n However, amid holiday fervor, StudioSuits employed A\/B testing, experimenting with alternative headings like \u201cLimited Deals\u201d and \u201cSpecial Offers\u201d to infuse a sense of urgency.<\/p>\n\n\n\n Image source<\/a><\/p>\n\n\n\n This iterative testing helped them identify the most compelling content and ensured that the online store was optimized to resonate with customer preferences, ultimately bolstering conversion rates.<\/p>\n\n\n\n In three different case studies<\/a> conducted by B2B companies (Hubspot, Dmix, and VWO), an A\/B test was conducted using different colors for CTA buttons.<\/p>\n\n\n\n Across the board, the red button performed the best, with the margin of difference being as high as 34%.<\/p>\n\n\n\n Image source<\/a><\/p>\n\n\n\n While using the data of previous companies can be a good starting point, it\u2019s important to test for your own brand as well.<\/p>\n\n\n\n For instance, if your brand is Coca-Cola, a red CTA button isn\u2019t going to stand out. It\u2019ll get completely lost in the overpowering presence of red in the rest of your branding.<\/p>\n\n\n\n What works well for one company or industry may not work the best for you. So don\u2019t fall into the trap of taking too many shortcuts at the expense of doing your own research and collecting your own data. Keep evaluating the effectiveness of your strategies and refine your data collection methods.<\/p>\n\n\n\n Email subject lines are a popular choice for A\/B testing, and for good reason.<\/p>\n\n\n\n Marketers can expect a $36 return for every $1 spent on email marketing campaigns.<\/a><\/p>\n\n\n\n The Catholic jewelry<\/a> e-commerce shop House of Joppa used an A\/B test for their holiday email marketing campaign.<\/p>\n\n\n\n Screenshot provided by the author<\/p>\n\n\n\n You\u2019ll notice in the A test email subject line sent November 27, House of Joppa highlighted \u201cCyber Monday\u201d to entice the reader.<\/p>\n\n\n\n In the B test, they used \u201cFlash Sale<\/a>\u201d and \u201cChristmas Collection.\u201d<\/p>\n\n\n\n Both email subject lines are meant to communicate the same message\u2014they are having a 15% off sale sitewide\u2014but this test allows them to see which subject line will have the highest open rate.<\/p>\n\n\n\n No matter how carefully your A\/B test is designed, falling into some common pitfalls can set you back significantly.<\/p>\n\n\n\n Here are some to avoid:<\/p>\n\n\n\n Sitting down and typing out your current analytics, the set-up of your A\/B test design, hypothesis, and results sounds about as exciting as watching paint dry. However, it\u2019s pivotal for success.<\/p>\n\n\n\n Unless you write things down and create standard operating procedures<\/a>, you\u2019re never going to remember the granular details of everything you\u2019re testing or the current state of all your marketing assets.<\/p>\n\n\n\n Furthermore, you won\u2019t be able to continue building on your success if you don\u2019t track where you came from and where you\u2019re going.<\/p>\n\n\n\n Lastly, you may not always be on your team doing the job you\u2019re doing now. Good documentation<\/a> guarantees that all the time and resources that went into gathering data aren\u2019t wasted every time there\u2019s a transition on the team.<\/p>\n\n\n\n It makes sense to want to gather as much data as you can with a single test. But if you are trying to determine which element performs better, you need to focus on that element. However, if you are managing sensitive data or complex configurations, utilizing a Privileged Access Management (PAM solution<\/a>) to control and secure access during your testing can prevent unnecessary exposure while you determine which element performs better.<\/p>\n\n\n\n Imagine, for example, you\u2019re A\/B testing a pop-up box on your home page prompting readers to sign up for your email list.<\/p>\n\n\n\n You want to increase the number of people who click your CTA button that currently says \u201cJoin Mailing List.\u201d<\/p>\n\n\n\n The current text\u2014\u201cJoin Mailing List\u201d\u2014is your control page, your A test.<\/p>\n\n\n\n However, you\u2019re going to create a separate pop-up with a button that says \u201cAdd Me\u201d to test which gets the highest click-through rate.<\/p>\n\n\n\n But you\u2019d really <\/em>like to know if a blue button or orange button performs better, so you set up your experiment to test that variable, too.<\/p>\n\n\n\n Well, now you have a problem.<\/p>\n\n\n\n If you get an increased number of subscribers with either test, you won\u2019t know if the color or text was the determining factor.<\/p>\n\n\n\n If you want to test both text and color, you\u2019ll need to do two separate A\/B tests.<\/p>\n\n\n\n (There is a way to simultaneously test multiple variables at once. It\u2019s called a multivariate test. But unsurprisingly, multivariate testing is more complex to execute and requires the expertise of advanced marketers.)<\/p>\n\n\n\n While it\u2019ll take time and patience, once you know exactly the variables that convert the best, you can use your knowledge to optimize all of your marketing assets in the future.<\/p>\n\n\n\n The good news is there are many tools available to help you design and run A\/B tests<\/a>. With the right tool, you won\u2019t need in-depth technical coding knowledge, even to run tests on more minute aspects of your web pages, such as authentication methods using SAML or OAuth<\/a>.<\/p>\n\n\n\n The bad news is the wrong tools can sometimes hurt more than they help.<\/p>\n\n\n\n Some tools come with many functionalities you don\u2019t need and will never use while being light on their A\/B testing capacity. Others may require advanced knowledge of how to set up and run campaigns you don\u2019t have and don\u2019t have the time to learn.<\/p>\n\n\n\n You want a straightforward tool that\u2019s easy to use and does exactly what you want it to do without extra hassle or expense.<\/p>\n\n\n\n Google Optimize (free and integrated with Google Analytics), Freshmarketer, and Crazy Egg are great places to experiment with A\/B testing tools and learn what\u2019s best for you and your company.<\/p>\n\n\n\n If you’re looking to expand your audience and are willing to invest in a service, consider exploring Google Ads alongside your email campaigns. It is essential to monitor Adwords competitors for optimal results.<\/p>\n\n\n\n Although analytical data can provide information on user behavior, it doesn’t explain the underlying reasons.<\/p>\n\n\n\n To gain insight into the causes behind these patterns, it\u2019s important to understand what users are viewing and doing. That\u2019s where a website session replay<\/a> tool comes in handy.<\/p>\n\n\n\n Image source<\/a><\/p>\n\n\n\n You can finally learn exactly how visitors interact with your content, all without doing any heavy lifting. These insights are a gold mine for perfecting your future content for conversions.<\/p>\n\n\n\n A\/B testing takes out the guesswork from the dozens of marketing decisions that arise when launching new marketing campaigns<\/a>.<\/p>\n\n\n\n With a carefully designed A\/B test, you can gain insight into how your users engage with your site and content and how to capture website traffic and convert it into sales.<\/p>\n\n\n\n While A\/B testing can become as complex as you\u2019d like to make it, many tools are available to help you design a straightforward and easy-to-analyze A\/B test to help you get the most out of your marketing efforts.<\/p>\n\n\n\n If you want to ensure you\u2019re getting the most out of your content, <\/em>contact us<\/em><\/a> about how we can help optimize your content library to be the most impactful.<\/em><\/p>\n\n\n\n Learn how to drive optimal results by refining and optimizing your content with masterful and strategic A\/B testing.<\/p>\n","protected":false},"author":31,"featured_media":3904,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[24],"tags":[148],"wf_post_folders":[],"class_list":{"0":"post-3903","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-content-strategy","8":"tag-content-creation"},"yoast_head":"\nA\/B testing in a nutshell<\/h2>\n\n\n\n
What elements can you test?<\/h2>\n\n\n\n
Websites<\/h3>\n\n\n\n
\n
Landing pages<\/h3>\n\n\n\n
\n
Email campaigns<\/h3>\n\n\n\n
\n
Software testing<\/a><\/h3>\n\n\n\n
\n
How to design an A\/B test<\/h2>\n\n\n\n
A\/B test examples<\/h2>\n\n\n\n
A\/B test example #1 – homepage<\/h3>\n\n\n\n
![\"StudioSuits](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image3-2.png\")
<\/figure>\n\n\n\nA\/B test example #2 – call-to-action buttons<\/h3>\n\n\n\n
![\"\"](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image5-3.png\")
<\/figure>\n\n\n\nA\/B test example #3 – email subject lines<\/h3>\n\n\n\n
![\"House](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image4-2-1024x59.png\")
<\/figure>\n\n\n\nCommon mistakes to avoid while A\/B testing <\/h2>\n\n\n\n
Being lax about documentation<\/h3>\n\n\n\n
Testing too many variables<\/h3>\n\n\n\n
Not using the right tools<\/h3>\n\n\n\n
![\"\"](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image1-2-1024x813.png\")
<\/figure>\n\n\n\nWrap up<\/h2>\n\n\n\n
![\"Traffic](\"https:\/\/beomniscient.com\/wp-content\/uploads\/5-1024x535.png\")
<\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"