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Ask users to do less - reduce their survey fatigue

· 5 min read

Well designed digital health products utilize wellbeing data to retain users.


NPS, CSAT, CES…PHQ-9, GAD-7, WHO-5…Question after question…Survey fatigue is a common problem with many customer experiences, especially in digital health products, because product creators are often lacking in enough data points to understand their users’ mental (or other) health conditions.

Many companies approach this problem by asking the question “how can we use surveys more effectively?”, trying to break up longer surveys into shorter ones, send them out less frequently, ask for one-click answers (i.e. stars, numbers, or emoticons), or know when the right time is to send them out at all.

Not many companies, however, are asking what we think is a better question: “how can we understand our users in addition to or without using surveys?” Asking users questions is an important part of the engagement and information gathering process, but it can be highly subjective and is biased towards users who are inherently inclined to participate anyway. As creators of digital health products aimed at helping people, how do we collect objective information to supplement the subjective from these surveys? How can we have a longitudinal view on how everyone is doing, not just an acute view on those who respond? How do we do this without intruding on people’s individual lives or collecting personal information?

With the introduction of longitudinal objective data in the developers’ toolkit, we have another perspective on how people engage with the products we build and how we can help them achieve their unique goals through that engagement.

Solely relying on the user to tell us through surveys, questionnaires, and assessments is no longer the only option. Objective wellbeing data used in combination with subjective user-driven data provides a richer total dataset that gives a more complete, more powerful picture of what a user is experiencing and what they need in return.

What is survey fatigue, and why don’t we like it?

Survey fatigue is the lack of user participation in product-generated forms such as questionnaires, surveys, or assessments that stems from being asked too much, too often, at the wrong time, for any combination therein or for other reasons altogether. This not only impacts the data quality and data quantity for a given user, but can also lead to lower product engagement overall (when not dealing with surveys) and missed opportunities to provide the user with genuine, actionable assistance they need.

Because of survey fatigue, many digital health apps do not have adequate data to identify user needs. This means they are limited in the interventions they can deliver as well as the products they need to build to serve their user communities. The result of this limitation often leads to user churn, poor adoption or retention, lower revenue, and lower positive impact.

Customer acquisition cost (CAC) can be expensive to find, communicate with, and attract new users, so retention is paramount. If we need to keep users for the sake of our businesses and for the sake of their wellness, why would we leave something so mutually important to a series of tools that are intrusive, subjective, biased, and result in all parties losing out?

Digital health companies need to be aware of the perceived value equation that users have with regards to their products. Requiring the user to do too much work (in this case, surveys) before they receive equivalent or more value from the product is a recipe for disaster for the company, the user, and the stakeholders on both sides.

Are surveys bad?

Not at all. When used wisely, sparingly, and on a case-by-case basis, surveys offer a way to capture valuable types of data that can be difficult to come by through other means. They are powerful tools that can build trust, foster a safe environment, and invite deeper conversation.

There are many surveys in use today that should continue to be used. One of our roles as innovators will be thinking of new ways to deliver these surveys as well as come up with new ones. Similarly, we have a responsibility to create new ways to combine them with other types of subjective and objective data, all for the betterment of the people we serve - our users.

Moving forward

Capturing health data via mobile applications is much more accessible today than it was just a decade ago. Both Android and iOS provide APIs that can deliver many different types of health data to a wide variety of software applications. At Sahha, we use some of this data in our machine learning models to infer predictions of psychometric surveys for different mental health conditions, such as risk of depressive episode, allowing product creators, business executives, and other digital health leaders to decide when and where to use surveys, if at all.


More and more people regularly use mobile apps that know us as individuals and, hence, provide great user experiences. We believe this puts the onus on digital health app creators to ensure the products being built match user expectations for value while retrieving the data essential for continual value delivery. By reducing survey fatigue and harnessing predictive technology, new products have a better chance of attracting and keeping users, and, above all else, earning the right to deliver them immense value for a long time.

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