We did a soft launch for FigPii in late January of this year. But we had a problem!

Background: Our conversion optimization clients tend to be on the large side. The cost of the experimentation software for these clients is expensive. In 2013, we built FigPii, an internal platform to help clients conduct split testing, heatmaps, video recording, and online polling. The platform is available as part of our consulting work. 

FigPii was internal for many years, but more and more clients were suggesting that we need to make it available to the public. 

Fast forward to 1/22/2020 – FigPii …

When we talk about Conversion Rate Optimization, it’s nearly impossible not to mention A/B testing (or split testing). Actually, many companies think that AB testing and CRO are completely synonymous. But that’s not true. A/B testing is a part of the greater umbrella that is CRO – but that’s a topic for another day.

From Saas to e-commerce to lead generation websites, many companies now understand how the targeted audience responds to certain changes on their websites, thanks to A/B testing. 

Most of the website elements you see on popular sites such as Google, eBay and Amazon were evaluated for …

How well do you know A/B testing? Admittedly, as an Optimizer or Digital Marketer, you are probably best friends or you know a lot. But one of the things I like about split testing is that even though you think you know it best, it always surprises you. 

Each A/B testing case study comes with its own set of differences and lessons.   

Having questions to ask about A/B testing isn’t just a great way to learn more about split testing, but it’s a great way to avoid making futile mistakes. To do that, though, you need to know all the …

“We always test to see what visitors like” Of course, there may be other tactical ways to come up with compelling lead generation strategies, but most of the digital marketers rely on AB testing and they have actually developed a “we should test this and test that” mentality.   In times like these, your site has to remain relevant and keep on generating leads. So, if you need some AB test ideas, this article could be just what you needed. 

Any experiment that involves later statistical inference requires a sample size calculation done BEFORE such an experiment starts. A/B testing is no exception. Calculating the minimum number of visitors required for an AB test prior to starting prevents us from running the test for a smaller sample size, thus having an “underpowered” test.

I will take you back to 2007 to one of the very first CRO projects we conducted for a large online retailer. The website received a massive amount of traffic (100k per day). Since it was one of our first projects, and the team was a bit trigger happy with AB testing, our first test included 30 different designs to run against the control. The headache of implementing all of these designs was overwhelming. None the less, we launched the test after about six weeks of implementation.

Seven of our challengers beat the original design within five days. The team …

Both user usability testing and a/b testing are important techniques used during conversion optimization. They aren’t poles apart as far as their purpose is concerned. You can say that they are indeed two sides of the same coin.

The ultimate purpose of user testing and a/b testing is to improve the overall on-site experience for users and to improve the conversion rates of websites. However, one must understand the 8 differences between the two and decide when to pick the right test based on its objective. So, let’s find out what is the purpose of each of these tests.

I have meant to write this post for a long time. There has been a lot written about one-tailed vs. two-tailed tests. However, most of the articles approach the topic from a purely statistical perspective providing many formulas but do not show how to do the calculations. Others articles approach the issue from a high-level business perspective. So, here is my attempt to delve deep into the topic. Some sections in this article are heavy on stats and formulas. If you are not interested in these, you can skip them! Special thanks to our data sciences team that checked and …