In Consent Models, we explore different approaches to consent. For example:
- In an in-app survey, the consent is inherent. This approach eliminates the need to interupt the user to ask for consent to do something. They simply respond to a question.
- When you meet a person in a moment of failure, the consent you’re asking for is very relevant and connected to the user’s power to influence product improvements.
- If you’re complimenting traditionals processes with instrumentation, you have a focused group of people whom you are engaging outside of the app. They have a deeper understanding and connection to contributing to improvements. Consent is more meaningful for them as they have more context.
Once we start exploring the consent approaches for learning of experiences beyond these transactional moments, we need more clever ways to respectfully get consent without annoying users. This is why the Regular Use format breaks down into 3 techniques. It’s also where we explore an opt-out, or invisible, approach. In this article, we’ll explore the benefits and recommendations for opt-in and opt-out experiences.
Opt-In
Benefits Asking for consent increases awareness. It is the easiest way for users to have control, because the opportunity to decide is right in front of them. Users are less likely to feel that you’ve made a choice for them. If done well, you have an opportunity to generate positive feelings toward your brand.
Challenges The opt-in requirement can lead to a small or biased data sample. This outcome can cause skewed results.
If not done well, opt-in experience can create friction in the user experience. There are several examples of ‘consent gone wrong’. Website Cookie agreements have become very disruptive in the browsing experience. Too many are in your face right away, interrupting your task at hand. The act of opting out to any level of cookies typically requires users to open a new page view, further removing them from what they came to do.
Recommendations Opt-in can work really well in certain cases, if it aligns with your goals. Below we’ve outlined some sample uses.
- In-App Surveys
- Failed States
- Focus Groups
- If you can implement a clever, intuitive user experience that you believe will capture a representative sample.
Opt-Out
Benefits If done well, an opt-out model can be used in situations where it’s required to optimize your sample size for accurate representation.
Challenges In an opt-out model, you want to make sure you are preserving trust with your user base. People don’t need to think about it. They don’t need to be disturbed. But your metrics practices should be fully transparent and available for users.
Recommendations Opt out is a fitting approach if you practice harm reduction techniques throughout the data lifecycle. This starts with data collection and continues throughout storage and removal of content.
In this model, make sure users are aware of your practices and are presented with an option to disable metrics. One way to do this is to occasionally remind them or show them which measurements are being shared. When you do, present the option to opt-out.
If you only need the data for a certain set of time (not forever), consider setting timeframes for data collection.
Refer to the ‘Harm Reduction’ section for more details on how to protect the safety and privacy of your users when measuring.
Before You Go…
Keep in mind the goals of consent. Whether it’s opt in or out, you always want to satisfy these goals.
- Build trust. (for both product and end-users)
- Ensure safety. (for both product and end-users)
- Build confidence. (for both product and end-users)