5 Things Impacting A/B Testing Result

5 Things Impacting AB Testing Result
A/B test success is a big boost to your capacity to synergize and randomize split testing. However, we invite complexities to endow the process in due course and end up screwing the testing. It eats into the excitement and your efforts look overpowered. The condition impacts the overall earning.
Still you have the chance to turn things in your favor. There must be effective understanding of A/B testing intricacies and a cautious understanding of each situation. The following are five key points that demonstrate where you may go wrong during A/B testing and how to bounce back.

·         Traffic Sources

Usually we tend to create a test at the starting point and ignore additional traffic routed through the middle pages, i.e. category and product pages, of an ecommerce site. We follow the typical way — Home > Category > Merchandise > Checkout > Payment – and forget that traffic flow can also be through category and product pages. In fact, these pages are highly optimized.
Now look at the concern. Your A/B test on the starting page reported lower than expected revenue because the traffic through other pages remains static.
Fix the problem by checking all possible traffic streams – regular, additional, and potential. Always calculate the specific traffic area you are testing and exclude the rest. Check the Google Analytics for custom variables to have a clear view of the result.

·         Statistical Confidence

It is not your cup of tea to feel nonchalant with statistical confidence. Still, you feel trapped to subjectively end the test and be content with 70, 80, or 90 percent coverage. This leads to bottlenecks and results fall below expectations.
It is not too overpowering to fix. Set your A/B testing coverage limit at above 95 percent. A five percent difference may not be risk. Overwhelming availability of top-class A/B testing tools ensures your statistical confidence remains high even without being involved in calculations.

·         Sample Size

We often think that sample size does not hold much significance though A/B test require us to figure out the customer behavior based on what a sample percentage of them do. We rely on data, but falter to take it into full cognizance. Fifty people may not be an assertive sample for 50,000 users. Even if changes allow revenue growth, the real improvements cannot be assured with this petite sample.
It is always better to achieve the requisite level of sample numbers to carry out A/B testing. Be mindful of sample size as much as the website values to have a definitive test results. Let the test go on until you have a respectable sample number.

·         Psychological Tactics vs. Customers’ Requirements

Knowingly or unknowingly we allow psychological tactics to overrun the exact customer needs. We resort to color, button, screen view, etc, changes considering that these thing impact consumer behavior and rate of conversion. But, alas, it lacks a scientific basis and we ensure that unsupervised thinking overpower our efforts resulting in losses.
Our motto should be to offer more value prospects to consumers and achieve optimization and conversion goals through it. No doubt psychology is important, but it cannot be a stand out strategy. No doubt it ensures that viewers are pushed further in to the funnel, but it may not reflect their exact needs. Better, always focus on the perfect understanding of consumer needs, behavior, and concerns and create your strategy accordingly.

·          Goals and Results

For websites with multiple conversion goals, it is difficult to focus on all. Tracking all goals may also lead to false A/B test conclusions. In a SaaS business, websites have manifold conversion goals manifested in free sign-ups, compensated sign-ups, eBook downloads, etc. If you focus on signups only, it is highly likely that you miss other areas, including the bounce rate. Customers may also get distracted by other non-focused areas. Instances are also there when failure to select the right A/B testing tool result in puffed-up results.
The most effective way to fix is tracking all vital performance indicators. Ensure that all metrics are behaving favorably and influence the bottom line positively while accepting the pattern change. Never forget to go for reputed A/B testing tools and double check the result accuracy.
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