Track macro and micro-conversion events

Good experiments have the right metrics to track impact. A conversion is an event that has a measurable impact on your business. A company's most important conversion events are often revenue and top-level metrics related to revenue, such as purchase rate or average order value. However, in experimentation, these conversion events rarely provide actionable results because they require major shifts in behavior or the collection of large sample sizes.

You can balance macro conversions (revenue) with micro-conversions that track visitor behaviors related to the changes you are testing. Knowing when to track micro-conversions helps improve your win rate and show value when improving the user experience.

Macro-conversions are events that translate directly into revenue. Each macro-conversion is tied to the industry's business model and occurs at the end of the funnel. A few examples of macro-conversions by industry include:

Macro-conversions translate into revenue easily and are popular events to track in experiments and campaigns. However, macro-conversions do not occur often, and when they do occur, they occur at the end of the funnel.

Micro-conversions occur as visitors travel from one step of the funnel to another. Each part of the visitor's experience may have different micro-conversions.

A few examples of micro-conversions for an ecommerce site include:

Each micro-conversion is a step to the purchase confirmation page view, which is the macro-conversion. Micro-conversions are important milestones on the way to revenue and occur more often.

Micro-conversions take more work to translate into revenue. If you increase the number of searches from the homepage, you get more visitors to the next step of the funnel, but you do not know how many more purchases to expect.

Tracking both macro and micro-conversions helps you optimize your metrics.

Imagine that you run an ecommerce website and are testing a new algorithm that selects products to feature on the homepage. Your main goal is to sell as many products as possible, so you decide to measure the number of purchases (a macro-conversion). If you sell more items with the new algorithm than the old one, it is a winner.

However, the purchase rate for the average ecommerce business usually does not exceed 3-4%. Detecting a change to that small percentage requires a high volume of visitors in the experiment. If your company has low-to-medium traffic, this could take a couple of months.

Tracking micro-conversions would be an effective strategy. They can occur more often than macro-conversions because they are closer to the tested change, making it easier to detect a change in a shorter period of time.

Imagine you run an experiment on the product page highlighting a certain discount to increase purchases and revenue.

The macro-conversion is purchases. You may find it hard to get a statistically significant answer from this metric because purchase completions are often a few steps away from the product page. Tracking a micro-conversion like clicks to Add-to-Cart is more effective because it is closest to the change on the page and is most likely to be affected. You have a higher likelihood of getting a statistically significant result.

But how do you measure overall impact? Assume these baseline conversion rates for each step in the funnel:

If you see a 10% increase in the add-to-cart rate, you can estimate the effect on purchases by multiplying the rates.

Estimated increase in purchases = .10 * .20 * .75 * .6 = 0.009

This calculation helps you estimate the overall value you would expect if you implemented the change on the site.

It is a good idea to use the Optimizely A/B test sample size calculator to estimate the sample size for each variation in your test on micro and macro conversions in advance.