In the past few years, digital marketing’s standard for success is one that has continuously been defined by the vitality of proper testing on the effectiveness of one’s efforts.

Nowadays, the world of digital marketing has stood its ground on the importance of testing as much as possible. It offers different ways to gauge the effectiveness of various strategy components until the “perfect” combination is followed. This crucial practice has become such a fixture in modern digital marketing that no strategy could subsist and be as effective as it should be unless a proper set of techniques is in place.


Among all the different types of testing that have emerged in recent years, however, A/B or Split testing has set itself as a cut above the rest with all the different possibilities that it unlocks.
The impact of split testing on modern marketing

Split testing has become far more of a decisive component in most strategies. It maps out and documents the effectiveness of two approaches that are slated to work better.

Generally, what makes this approach such a crucial part of modern marketing methods lies in the fact that it’s as easy as it is effective. That’s why so many businesses and digital marketers use them in the first place. As opposed to most standard testing methods that simply seek to tell “good” and “bad” apart, the A/B methodology makes it far easier to figure out which details work best to truly see a post’s potential.

A crucial set of mistakes to watch out for

Despite the effectiveness, applicability, and ease of use that split testing brings to the table, this game-changing testing approach is no stranger to a fair share of pitfalls. Nonetheless, these pitfalls are easy to avoid as long as you are aware of what they are.

Here are a few common A/B testing mistakes that you should be well aware of:

Mistake #1: Overlooking the crucial need for a control group

Most beginner marketers or business owners get their testing efforts wrong right off the bat because they overlook a crucial guiding component: their control groups.

Control groups are a must-consider when building an effective testing process because they help determine how much you can change and what can be tinkered around with, sans the risk of wasting time, money, and effort. In cases like these, a controlled variable helps establish a reference point that you can use to gauge whether or not your tweaks are headed in the right direction.

Mistake #2: Too many variables, too little time

As much as we’d all like to truly maximize our efforts and investments, trying to juice the most out of an A/B testing process is one that entails upholding a proper balance between the time you have and the factors you’ll play around with. While it may seem quite tempting to test every variable—may it be a website, campaign, or video—you’re far better off working with a manageable number of factors to gauge which changes work.

Mistake #3: Trying to work with ambiguous goals

Among all the different factors that could possibly hamper or damage your split testing performance and effectiveness, none are far more limiting than a lack of a clear-cut and validated goal.

Simply testing to achieve the effect of “improving conversions” will rob your efforts or proper guidance because they don’t set a standard that’s actionable or narrow enough to the point of being open-ended. Instead of throwing all precision and detail out the window, you may want to quantify your goals so that you know what you’re working with, what you need to do, and how you’ll be able to achieve the goals you’ve set more systematically.

Conclusion

With the use of a thorough A/B or split-testing strategy, you get to unlock an abundance of different opportunities to boost the overall performance of any digital marketing strategy. Likewise, it’s important to ensure that you avoid potential mistakes along the way. By taking the time to keep track of the three common mistakes mentioned above and circumventing the possibility of committing them, it will be far easier to keep everything optimized in the long run.

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