Commitment filters are steps where users have to provide information (e.g. credit cards, identification, phone numbers, etc) before being allowed to use the product. A typical example here is to ask for a credit card before providing a free trial.
There are different approaches to using these filters. Some people advocate for removing them altogether and letting the user experience the full product without any constraints while others believe this approach attracts lower quality users.
From our perspective, we think you should let data answer this question for your product. Data will let you measure the short and long-term impact of using commitment filters. For example, let’s imagine that you’re considering asking for a credit card during the onboarding flow.
In the short term, you will likely see a drop in overall conversion rate for your onboarding flow but in the long term, you might see an increase in retention (and decrease in churn). Having the right data should make answering these questions relatively straightforward.
Actions for This Week:
Does your team have the right data structure to be able to measure the short and long-term impact of commitment filters? If not, what do you need to get in place to be able to do this?
Interesting Reads and Resources