Digital consent is the process of asking and receiving permission for something to happen. A consentful digital experience respects the rights and interest of the people using the technology. The models in this article offer different ways to think about consent and how to integrate metrics with respect for humankind.
The models are broken down by format. Formats in which you engage your users to help your product learn. They include:
- In-App Surveys
- Failed State
- Regular Use
- Focus Groups
In-app surveys are touchpoints you have with users to learn about their experience and expectations of a product. They focus on customer needs, expectations, and understandings. The purpose of this data is to inform product improvement. Industry commonly refers to this data as voice of customer.
When a user encounters an error in the app, you have an opportunity to lessen their emotional friction and learn from them. This moment presents an opportunity to gather additional information that only the user can tell you or grant permission to have about the surrounding context.
The Model B Figma prototype will give you a feel for this consent experience.
So, you want to learn from your existing users about their experience in the app. You might measure things like how long it takes for someone to connect to your VPN service, or if and where they get lost during setup. This data will tell you (or give you tips into) valuable information about how users’ are experiencing your app. It can help you make decisions about where to focus design and development time and energy. Three techniques can be used in this format:
- a) invitation
- b) intervention
- c) invisible
A consent invitation is a quiet request to participate in metrics. It does not interrupt the user flow or require a response from the user. A banner, card or notification to invite users to participate can be used.
The Model A Figma prototype will give you a feel for this consent experience.
A consent intervention is a request for permission that is on the user’s critical path. They cannot continue without making a choice. The intervention may be with the full user base or only a % of users to minimize friction. If the opt-in consent can be placed at relevant moments of the user’s experience, friction may be lessened.
Invisible consent is opt out. With this technique, metrics part of the product’s practices, and done in a way that presents no harm to users. No personally identifiable data is collected and processed. We recommend that you cultivate a culture of transparency around your product and provide user-friendly policies and user education to build a confidence.
Taking a mixed method approach, you can compliment a traditional qualitative research study with instrumentation in the app. The metrics generated can give insight into participants’ experience using the app in normal life conditions.
The Model D Figma prototype will give you a feel for this consent experience.