Conversion Rate Calculator

TL;DR:

$CR = \dfrac{Conversions}{Visitors}$

Calculating the conversion rate of a website can be done in many layers. You can either calculate the conversion rate of your entire online property, spanning all your pages. Or you can zero in on a particular page or blog and then assess its conversion potential.

1. In order to calculate the conversion rate of your entire website, set up a segment on Google Analytics that bypasses traffic from subdomains and focuses only on your main domain. Then set up a conversion goal - either destination or custom event - for the most important action on your site. This can be a form submit, a paid transaction or even a blog view. Once these two aspects are in place, you can simply divide the total goals triggered over a period of time by the total traffic to your domain.
2. In order to calculate the conversion rate of an individual page, you can leverage the analytics of a platform like LanderApp or Unbounce. They have the ability to directly display the desired number of actions (pre-defined) divided by the total number of traffic, over a given period of time. But in case you are using a WordPress website, you can dig into Google Analytics.
   Instead of simply taking the total number of events fired over a time period, take into account the events that are fired for a particular page. You can train your Google Tag Manager to send this data to your analytics account when setting up the tag.

Conversion rate is a straightforward metric. Most marketers do not struggle with calculating the conversion rate.

But there can be technological constraints that hold them back:

• If the action that contributes towards calculating conversion rate - like say a Free Trial for a SaaS website - is from a call-to-action button that’s spread out across an entire site (in the header or footer), unless the tag in Google Tag Manager is trained to send the URL on which the goal has been fired, calculating the conversion rate of individual pages might be a hassle.
• In most cases, privacy compliance laws call for cookie consent. This applies to the entire analytics stack of marketers and results in capturing only partial data on goals and actions. The resulting conversion rate is lower than reality, especially if the traffic count stays the same and is accurately logged.
• Traffic spikes due to bots and automated page views can skew conversion rates. The fix is to ensure your analytics account understands how to spot and discount these unwanted guests.

The statistical significance in A/B testing is when the p-value becomes inferior to our significance threshold. It signifies that our null hypothesis is highly unlikely to be true, hence it proves that we have the effects we are observing are not due to random chance.

Let us assume you’re running an A/B test on two landing pages selling the same product. The first one (we’ll call it A, or the control) does not have 3D product images. The second one (we’ll call it B, or the variation) has them.

The conversion rate in terms of “Add to Cart” is 7% for A and 9% for B. So should you just add 3D images to your product pages across the site?

No! Because all the visitors to your website have not seen page B and you can’t make assumptions about their preferences simply from observing the behavior of a much smaller sample size. Right? (PS: Don’t make assumptions in marketing or optimization… being data-driven is the way to go).

How to solve this little problem?

P-value comes to the rescue, as it gives you the probability that you have seen a 2 percentage point increase in the “Add to Cart” KPI for your variation (Page B) or a more extreme result if the null hypothesis (that there is really no change) was true. If it is inferior to or less than the “risk” you are willing to accept - the standardized value of this being 5% (the significance level) - then you can be reasonably confident of your test (at your chosen level of confidence, in that case, 100% - 5% significance level = 95% confidence level).

Both the confidence level and the significance level are two sides of the same coin! $$Confidence = 1 - Significance$$ $$Significance = 1 - Confidence$$

Hence if we have a confidence level of 95%, it entails a significance level of 1 - 95% = 5% = 0.05. We can then use the significance level value to compare against our p-value. If our p-value < 0.05, we say that our A/B test has reached “statistical significance”, with a confidence level of 95%.

The power of an A/B test is a measure of how likely we are to observe an effect if it is present. Usually, we want it to be at least 80%. The higher the better, it assures us that we are not missing anything!

It plays an important role in sample size calculations because, along with the MDE (minimum detectable effect) and the confidence level, it allows us to determine the required sample size to wait before analyzing a test.

Typical website conversion rates have an average value of around 2.5%, and top-performing ones usually hover around 5%. Rarely, some landing pages can squeeze in +10%.

In some of the cases yes, when the user wants to optimize that particular metric. But there are also many other metrics that can be optimized, like the two most important revenue ones that we support, ARPV and AOV.