You are implementing digital marketing to sell insurance and grow your business. But are you aware of which marketing strategies work best for your agency? When writing email copy or blog posts, when designing call to action buttons, or when publishing on social media, we often use our intuition to decide what will make our prospects click and convert. But leaving marketing decisions to guesses and assumptions can be detrimental to results. In this week’s post, we explain A/B testing for insurance agencies to help you figure out what works for your agency and what doesn’t, with evidence to back it up.
A/B testing, also called split testing, is the marketing process based on dividing your audience and comparing a number of variations of a single piece of marketing. This allows you to test what variation performs better and improve your marketing efforts. For example, you can create one email with two different subject lines or body copy. You can show version A to one half of your audience and version B to another.
Any of your marketing materials can be A/B tested, for example, elements on your website, such as headlines, call to action buttons, or images. You can test the colors, the fonts, or even the layout and functionality. A/B testing is also frequently implemented in email marketing tactics, in which you can test the subject line, the body of the email, or the calls to action. However, having the ability to test every little marketing decision, doesn?t mean you should embark on that journey. When thinking about A/B testing for insurance agencies, focus on the elements that influence visitor behavior and conversion rate.
The ultimate goal of A/B testing is to increase your conversion rate. Through testing different options, you will be able to narrow down what the most important elements in your marketing programs are and which ones are working better.
Implementing the versions that work better for your insurance agency, you will be choosing the marketing elements that motivate your audience to open and read your emails, or that better attract visitors and make them spend more time on your site. As a result, the number of requests for a quote and your conversion rate should increase. When you start implementing A/B testing for insurance agencies, your marketing efforts will become more effective and profitable. The ROI from A/B testing can be significant since minor changes can create a huge return.
A/B testing requires a tool. You can choose from a wide variety of solutions depending on your agency’s needs and capabilities. Google Analytics offers a free option in which you can A/B test up to 10 versions of a single page of your website. Google Analytics is a good option if you are already using the platform and want your data stored in one place. However, the options on the platform are limited.
If you want to dive into A/B testing, you can explore other paid-for platforms. For example, Optimizely and Visual Website Optimizer are well-known, they both offer similar options at different starting prices. Unbounce and LeadPages focus on A/B testing for landing pages. Kissmetrics is one of the most robust platforms to analyze your website, A/B testing is a small part of it, but will produce an overview of the impact of your marketing materials throughout your entire funnel. Lastly, Crazy Egg will deliver results in a visual way, focusing on the movements every user makes on your website.
Regardless of which A/B testing software you choose, there are some basic steps you will always need to follow.
Before A/B testing, ask yourself what element you want to test and why. Clearly, you can run A/B tests just to run them, but that approach won’t be as effective. As we established earlier, the goal of A/B testing is to increase conversion rate, but what is your agency’s specific issue that you could solve to increase conversions? Use heat maps, Google Analytics, or track your customer buying behavior to identify what the problem is and improve the corresponding metric.
Develop A Hypothesis
Once you know what the problem is and what is the metric affected, or what you want to improve to increase conversions, develop a hypothesis. Based on the insights from your research, establish the path you want to implement to solve the issue. For example, if your research results show high traffic to your website, but visitors are not requesting a quote, you can hypothesize your call to action will increase conversions.
Create A Variation And Split Your Audience
When thinking about A/B testing for insurance agencies, create two different versions of the marketing piece you want to test. You can use the version you are currently using and compare it with a new one, or you can create two new versions. Next, split your audience equally and randomly, and run each version on each part of your audience. The audience you need to split will depend on the marketing material you are testing. For example, when A/B testing your homepage, your audience will be your website visitors. If you are testing the subject of your email, your audience will be your subscribers. The tool you use to A/B test will help you with this process.
Set Up The Test
When conducting an A/B test it is important to keep these two aspects in mind:
When you finish running your A/B test, it is important to analyze your results and draw conclusions. Most likely, you will have a version that is statistically better than the other and, therefore, the decision will be made for you. If the test is statistically inconclusive, you can stick with your current version or run another test. Consider the inconclusive data when taking your next steps and drawing conclusions that you can apply to future marketing materials.
Throughout the post, we have illustrated A/B testing for insurance agencies with simple and clear examples based on specific problems, such as calls to action or email subject lines. Now, let’s consider another situation. Imagine a scenario in which you think that your website will drive more conversions if you include the perfect client testimonial on your homepage. Following the steps defined earlier, your hypothesis would be that including a testimonial on your homepage will increase your website’s conversion rate.
Next, you set up an A/B test in which one version of your homepage doesn’t have a testimonial, this is, your current homepage, and you create another version in which you include a client testimonial in the form of a video. You will display each version of the homepage to a different audience. Since your homepage gets around 30 visitors per day, you decide to run your A/B test for a month. That gives you a sample of 900 visitors, which is statistically significant in your case.
The result can go two ways. One, after running the test, the homepage that includes a testimonial has a higher conversion rate and you can implement it as the standard. Also, you can infer that clients opinions are valuable for your prospects and, in the future, include testimonials in other marketing materials.
Two, after running the test, the homepage with testimonials doesn’t drive a higher number of conversions. There is no statistical proof that it would work better than your current homepage. Next, you could try to make some modifications to the testimonial, such as replacing the video with a white paper-style testimonial or changing how it is displayed, and run another test to see if the results are somehow different. If running different tests you conclude testimonials don’t improve your conversion rate, stick to your homepage.
A/B testing for insurance agencies can make a significant impact on your marketing efforts. Looking at what successful companies are doing isn’t enough anymore. Different audiences behave differently, and something that works for one company, might not work for another. Incorporate A/B testing to your marketing analytics strategy. If you consistently A/B test, you will discover what works best for your agency without guessing, predicting, or using your instincts. You will make decisions based on statistically based results and you will reach your marketing efforts full potential.
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