The song “New River Train” says, “Darling you can’t love two and your little heart be true”. So, sometimes in life, we have to make a choice, no matter how hard it may seem. 😋
The same goes for your business strategy, where A/B testing helps you make tough decisions easily, based on data-backed evidences.
A/B testing tests two or more versions of the product, service, app, webpage, email, graphic, etc., to figure out which one has the highest potential to convert your audience. 📈
Thus, it’s an experimentation that’s data-driven, reliable, and widely used by marketers to provide the best possible experience to their prospects and website visitors to win them over.
Not just that, there are numerous other ways demand gen managers can benefit from systematic A/B testing. So stay tuned to know what the talk is all about. 📢
Conducting A/B testing might seem daunting at first, especially if you have not done it before. But with an organized approach, you will be up and running.
Your A/B test should ideally comprise the following steps:
✅ Identification of goals
✅ Data collection
✅ Test hypothesis generation
✅ Creation of different variations
✅ Running experiment
✅ Analyzing results
✅ Implementing the winning version
Source: vwo
Need further clarity? Dive into how to conduct a systematic and effective email A/B testing.
The global software market meant for A/B testing by 2025 is going to be of value $1.08 billion, as compared to $485 million in 2018. Seems like its significance continues to go from strength to strength, isn’t it?
Be it finalizing your website’s new design, making attempts to create effective lead-nurturing campaigns, or working on tactics to solve your email marketing woes, you would want to have good enough assurance before investing in it, right? After all, marketing can be a costly affair.
And this assurance comes from A/B testing, which helps you finalize the most effective version of the two. Once it’s done, you can invest your money, time, and efforts to divert 100% of your traffic toward the most suitable version.
It also helps you identify your audience, understand their pain points, adapt to changing trends, make data-driven decisions, reduce bounce rate, and thus amplify your conversion rate.
Source: Adobe
Furthermore, by conducting effective email A/B testing, you can stay ahead of the curve. Be it enhancing the click-through rate, or open rate or gathering the pivotal data of your prospects for future campaigns, email A/B testing has got you covered.
Opting for a testing tool is subjective since no two businesses are alike and have unique traits and requirements no matter how similar it may seem, you and your competitor’s audience will have some unique features that separate them.
So, you should consider factors like ease of use, sample size, functionality requirement, statistical analysis capability, scalability, pricing, expertise needed, customer support, etc. while figuring out the ideal tool.
Some of the A/B testing tools recommended by our RevOps experts are:
1. Hubspot A/B Testing kit
2. AB Tasty
3. VWO
4. Optimizely
5. Adobe Target
6. Kameleoon
7. Google Optimize
Let’s say you have a top MarTech tool in your arsenal, that is a champion in the field of marketing automation and customer relationship management. However, there is a good possibility that it doesn’t offer extensive A/B testing functionality.
This can be true the other way around as well. Some tools that give the testing facilities extensively are not among the best when it comes to marketing automation. Again, this may make your selection a half-hearted decision.
Similarly, even if a tool gives the A/B test facility, it may be ideal for only limited variables like landing page or email.
Guess what? The story doesn’t end here! 😋
Some more common pitfalls when it comes to leveraging the MarTech tools for A/B testing are as follows:
Incorrect sample size: An incorrect sample size can skew your test results, depriving you of your test goals. One size fits all is a no-go; you must work on finalizing the correct sample size before approaching the tools.
Hypothesis-building limitations: The effectiveness of your hypothesis would depend largely on the accuracy of the data that you use to build it. Regardless of your business size, putting all the data into structured form and maintaining objectivity can be challenging.
Variable selection challenge: Identifying the ideal variables to test can be tricky. However, the fortune of your test would be primarily defined by the variables you opt for. For this, you should be using both analytical and behavioral data points.
Flickr effect: Imagine if you have set up both versions to be tested separately for different users. However, when the user shows up, both versions appear one after the other, leaving the user confused. The choice made in this case might not be rational.
This could be due to the slow speed of your webpage, internal problems in the tool being used to test, or due to some limitations in the webpage’s design altogether.
Lack of objectivity: Without you even realizing it, you could run into a lack of objectivity and biases while getting the versions ready for the test. Although intuition, speculation, and prediction have a role to play to a certain extent, data-driven tests are likely to yield unbiased and accurate results for you.
Undermining user experience: Now this might come up as a little contradictory to the point above but overly prioritizing data metrics over the user experience can deprive you of getting the benefits of qualitative insights. A/B testing is a balancing act.
For instance, gathering feedback is a classic way to go. HubSpot recommends adding an exit survey form to your website so that the users can let you know why they didn’t click or chose to exit.
Recency bias: Let’s say that you have made a recent change to the “call to action” button. The regular visitors, who are familiar with your website, might prefer it due to their recency bias. This may give you inaccurate information regarding the variables.
You need to get past the roadblocks of A/B testing functionality to fully utilize its potential with your MarTech tools. Some of the sure-shot ways to do it are as follows:
Choosing the right tool: Depending on how extensively you need to conduct the test, you can opt for a tool. As discussed earlier, a tool specializing in A/B testing may not serve your other needs as you may like. It’s true the other way around as well.
Pricing can also influence your decision. For instance, Google Optimize, a freemium web analytics and testing tool by Google, and HubSpot & Kissmetrics' A/B Testing Kit are free of cost.
Restricted approach: A test should be done in a restricted manner in the sense that, defining it, sticking to it, and verifying it multiple times need a forced approach, rather than a scattered one.
Let’s say you tested a subject line, and it worked like a charm and so you stopped testing it after some time. But later, it may become dysfunctional and that’s where monitoring becomes essential.
Generating the ideal sample size: The smaller the sample size, the higher the chances of the results getting skewed. As a first step, you need to make sure that you have enough traffic to make it happen.
For this, the guidance of statisticians and data analysts can be taken as the marketers may not have the needed expertise for that.
Overcoming the “people vs tool problem” dilemma: You need to understand that A/B testing is more of a people problem than a tool problem. After all, once a tool passes your requirements criteria, it will be as good as how you utilize its potential. Hence, the qualitative angle cannot be undermined.
Precise hypothesis creation: Hypothesis creation can be challenging, especially when it requires you to ensure high data accuracy and proper data structure.
For this, you should utilize feedback and customer journey data. You should ask yourself and then define what you expect to occur and why.
Identifying the key elements: Identifying the key elements is a critical step. However, it can get tough especially if you have plenty to choose from.
In this case, you can prioritize those elements that can have a significant impact on your business. But again, to be able to do this, you need a thorough data analysis of the key elements on your landing page and learn how they align with your business goals.
Leveraging audience interaction: Assuming that your audience within its group doesn’t interact among themselves, is one of the easiest pitfalls to fall into. Their interactions can impact the outcome of your test.
Harvard Business Review cites LinkedIn’s approach to overcome it, which ensures that a subject, along with those who can potentially interact with him/her, are all placed in the same group.
If it’s not clear to you, just think of yourself browsing through LinkedIn. Isn’t your probability of interacting with a post higher when one of your connections has already “liked” it or has commented on it? It’s as simple as that.
LinkedIn also famously uses “time series experiments” to match job-seekers with job openings. For this, it simultaneously exposes all job openings and job-seekers in a given market to the new algorithm for a certain period. In the next cycle, it would randomly decide to either switch to the old algorithm or stay with the new one.
This continues till it finds all the different types of job search patterns to match.
Taking feedback seriously: You may make critical changes to your business based on the A/B test. But your job doesn’t end here and you have to bring in the good old friend “Feedback” into play. Based on that, further testing might be needed. Tests may have to be done in succession.
Testing for varied digital environments: Your diverse customer segment may have varied degrees of digital access, internet speed, and devices. Hence, while measuring the impact of your testing, you should align it with such users' digital journeys.
Getting over the failed tests: As poetic as it may seem, failures are the stepping stones towards success. This mindset is necessary to embrace the shortcomings of the test. After all, a failed test can offer plenty of insights for subsequent tests.
After going through a couple of sections above, it’s quite evident that A/B testing not only involves technicality but also critical thinking and decision-making.
While understanding and implementing isn’t rocket science, getting the maximum utility out of it doesn’t come easy. This is critical considering that marketing is a costly affair!
This is exactly why you need the helping hand of the A/B testing experts, who make sure that your A/B testing journey is rewarding. 📈
Our dedicated team will understand your requirements, help you with conducting it, and then stand by you until you navigate the A/B testing process seamlessly for a healthy ROI from your marketing efforts.