The following two metrics are the most common KPIs tracked for lead generation websites:

For websites that rely on selling premium content to registered users, the registration process abandonment metric tracks the percentage of visitors who start the registration process compared to those who complete it. This metric is similar to the checkout process abandonment rate for an ecommerce website, and it tracks the following KPIs:

Free-to-paid account conversion ratio

In many instances, these websites will offer free access to their content for a limited time. The goal is to allow website visitors to experience the content and determine whether a paid membership will bring them additional value. Ultimately, the goal is to convert these free accounts into paid ones. This ratio tracks the percentage of visitors who start with a free account and then convert into a paid account. This ratio will vary tremendously based on the type of website, the cost of membership, and the perceived value of the content.

Active paid membership base

At the surface, this metric may seem to have little business value, as it doesn’t affect revenue directly. However, it can be useful for predicting the possibility of subscribers canceling their membership and thus reducing the website’s revenue. Some subscription websites measure the average activity for their membership and how frequently they log in to the website. For example, if a customer logs in to the site once every two weeks initially, but then does not log in for four weeks, the chances of that user canceling the subscription increase.

Subscription cancellation ratio

This ratio tracks the number of cancellations within a time period. Tracking this ratio allows subscription-based websites to react to spikes in cancellation by understanding why this happens and coming up with a plan to address the causes.

Average subscription length

This number tracks the average period a person remains subscribed to the website. This will help the website operators understand their revenue model. Analyzing the different subscriber behavior between long-time subscribers and short-term subscribers can help reduce the number of short-term subscriptions.