Track macro and micro-conversion events

  • Updated
This topic describes how to:
  • Set the right goals for an experiment
  • Decide where you'll track macro or micro conversions
  • Get statistically significant results more often

Choosing the right metrics to track impact is a key part of good experiment design.

A conversion is an event that has a measurable impact on your business. Typically, the most important conversion events for a company are revenue and top-level metrics that tie directly into revenue, such as purchase rate or average order value.

In experimentation, however, you seldom end up with actionable results if you rely only on these conversion events because they require huge shifts in behavior or large sample sizes to detect.

In this article, we show you how to balance “macro” conversions like revenue with “micro” conversions that track visitor behaviors that are closely related to the changes you are testing. Knowing when to track micro-conversions instead of macro helps improve your win rate and show value when improving the user experience.

Learn more about macro and micro-conversions and practice generating effective hypotheses in our live, interactive training session: "Practice Your Ideation Skills."

Macro-conversions

Macro-conversions are events that translate directly into revenue. Below are a few examples, by industry. Each macro-conversion is tied to the vertical’s business model. Notice that they usually occur at the end of the funnel and the conversion rate likely to be rather low.

Industry

Most common macro-conversions

Ecommerce

Purchases

Travel

Bookings

Media

Subscriptions, ad clicks

B2B

Leads

Macro-conversions easily translate into revenue and are therefore very popular events to track in experiments and campaigns.

The problem with macro conversions is:

  • They do not occur very often

  • They usually occur at the end of the funnel

Micro-conversions

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

Here are a few examples of micro-conversions for an ecommerce site (more examples here):

Customer journey

Sample micro-conversions

Funnel step

Common macro-conversions

Homepage

Searches submitted, category clicks, featured products clicks

Search results / category page

Product clicks, filter/sort usage

Product page

Add-to-cart clicks

Shopping cart

Continue-to-cart clicks

Cart checkout

Fill-in payment details, purchase confirmation page views (macro)

Each one of these micro-conversions is a step on the way to the purchase confirmation pageviews, which is the macro-conversion in this case. Micro-conversions are important milestones on the way to revenue and occur far more often.

Micro-conversions are harder 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 it is hard to say how many more purchases you should expect from those events.

If you are using Optimizely Web Experimentation to test on a checkout page, you might need to configure your site for PCI compliance.

Optimize the metrics in your experiment design

The most efficient way to overcome the shortcomings of each type of event is to track both.

The problem with macro conversions is that they only measure home runs, but there are many ways to win in baseball.

- Hazjier Pourkhalkhali, Global Lead, Optimization Strategy at Optimizely

Imagine that you run an ecommerce website and you 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 with the old one, then it is a winner. Unfortunately, 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 is an effective strategy in this case. They usually occur more often than macro-conversions because they are closer to the tested change. So, you are more likely to detect a change in a reasonable period of time.

However important revenue is to a company, not all value that you will provide for your users will be directly captured as more revenue. Delighting your users by providing them with a top-notch experience is likely to increase important high-level goals such as user satisfaction, retention, or better engagement.

- Lev Tatarov, Strategy Consultant at Optimizely

Primary and secondary events are another way to think about your metrics strategy. Read about in this article.

Example: Ecommerce metrics

Imagine you run an experiment on the product page where you highlight a certain discount. You hope to make a purchase more attractive to visitors and increase revenue.

The macro-conversion for case is purchases. If you think you can get a statistically significant answer on that metric—that is great! But in most cases, purchase completions is a few steps away from the product page. So, it is more effective to track a micro-conversion like clicks to the Add-to-Cart button.

Clicks to Add-to-Cart button is the event that is closest to the change on the page and most likely to be affected. You have a much higher likelihood of getting a statistically significant result.

But how do you measure overall impact?

Let us assume these baseline conversion rates for each step in the funnel:

Product page -> Shopping cart: 20%
Shopping cart -> Cart checkout: 75%
Cart checkout -> Purchase completion: 60%

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

Estimated increase in purchases = 10% * 20% * 75% * 60% = 0.9%

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

Best practices

It is a good idea to estimate whether you are likely to reach statistical significance on micro and macro conversions in advance.