Unlocking the Power of Perceived Progress: Motivating Users Through Design
A list of UX concepts and examples for improving onboarding, checkout processes, and low-converting user flows.
Have you ever felt excited to try a new product or service, only to have your motivation dampen after seeing a long tedious onboarding process?
As designers, it’s not only our responsibility to craft visually appealing and intuitive interfaces, but also to tap into the psychology of motivation and engagement to help users achieve their goals within our products.
In this article, we’ll uncover the secret to keeping users on track and motivated with a deep dive into the psychology of perceived progress. From progressive disclosure to the Zeigarnik Effect and beyond, you’ll learn how to harness the power of cognitive biases and psychological principles to create a sense of accomplishment and drive users to completion.
But first, let’s uncover the underlying reasons for why we all struggle to stay motivated when facing time-consuming tasks.
The Stanford Marshmallow Experiment
One important aspect of motivation is how well an individual handles ‘delayed gratification’. To understand this concept better, let’s look at the Stanford Marshmallow Experiment.
In this experiment, children were presented with a marshmallow (small short-term reward) and were told that if they could resist eating it for a period of time, they would be rewarded with a second marshmallow (larger long-term reward).
In the results, 54 percent of the children failed, while the remaining children who were able to wait for the second marshmallow were found to have better life outcomes, such as higher SAT scores and lower rates of obesity.
Unfortunately, humans have a cognitive bias to prefer immediate over delayed gratification, meaning we tend to favor short-term rewards over long-term ones. So when users have the intention to use a product to achieve their goals, we can help them with the following design techniques.
The first technique is known as ‘progressive disclosure’. Progressive disclosure involves breaking a large task into smaller, more manageable steps (typically leading with the most critical or relevant steps). Progressive disclosure reduces the mental interaction costs for complex tasks.
This is supported by the fact that the ‘activation energy’ required to complete a large task is generally higher than the activation energy required to complete a series of smaller tasks. This is because larger, more daunting tasks can be overwhelming and can make people feel unmotivated or hesitant to even start. In contrast, smaller tasks are often less intimidating and can be more manageable for users to undertake.
Goal Gradient Effect
Imagine you’re a mountain climber, reaching for the summit with every step. But with each step, the peak seems to move farther away. It might feel like you’re stuck and making progress feels impossible. However, as you get closer to the mountain peak, it becomes more attractive and draws you in, and pushes you to reach the summit. This phenomenon is known as the ‘Goal Gradient Effect’; which states people get closer to a reward, their motivation towards achieving their goal increases.
This is why having users complete smaller tasks (with smaller incremental rewards) is more motivating than completing a large task with a large reward. Short-term rewards motivate us more compared to long-term, future ones. This concept has even made its way into management science; for example, Nucor restructured its entire bonus incentive structure to be based on monthly targets instead of annual ones.
This means two things: 1) we should break up large complex tasks into smaller tasks, and 2) we need to provide small incremental rewards along the way. For breaking up large tasks into smaller tasks, you should always turn progress bars into segments or individual steps.
You can also get creative with making a large number of small tasks feel like smaller groups of tasks (i.e., chunking). For example, let’s say you need to collect 10 inputs (e.g., name, email, profile preference, etc.) as part of onboarding. Instead of asking for all 10 inputs up front, you should ask for email (doubles as their login username) and password. Then, you can surface another couple of inputs as part of an ‘onboarding checklist’, and then use progressive disclosure to introduce the remaining inputs when they are contextually relevant.
One of the most interesting design patterns I’ve seen is Discord’s ‘unclaimed account’ feature, which is a brilliant way to eliminate the first step of ‘account creation’ friction. In addition, this also increases the user’s motivation to complete the sign-up tasks because they’ve already tried out the product.
The Endowed Progress Effect
By now, I’ve hopefully convinced you to use progressive disclosure to break up your large complex tasks into smaller tasks and to provide incremental rewards along the way to activate the Goal Gradient Effect.
Now, there’s a bit of a trick to designing the first step of a task’s progress bar. The best practice is to actually give users a ‘freebie’ as a head start, which provides them with a sense of immediate progress.
Let’s imagine you’re at a coffee shop and they give you the choice of either A) a ten stamp loyalty card with no free stamps or B) a twelve stamp loyalty card with two free stamps. Which card would you take? If you answered B, then you followed the same behavior as most other people. Even though both rewards require the same number of stamps, you chose the option with the artificial head-start because it came with the illusion of progress.
Incremental rewards in products can take the form of ‘intrinsic rewards’ and ‘extrinsic rewards’. Intrinsic rewards are those that come from within the user, such as a sense of accomplishment or satisfaction from completing a task. Extrinsic rewards are those that come from outside the user, and tend to be more tangible (i.e., checkmarks, bonus points, badges, etc.). Advanced designers can take advantage of these principles to design an intentional progression with a gamification loop leading up to the ‘tipping point metric’ in ‘time-to-value (TTV).
The reason we do this is because of the ‘endowed progress effect’, which is a phenomenon where people perceive a task as being more valuable or worthwhile when they have made some progress towards completing it. Therefore, you should always start your progress bars and checklists with the first step auto-completed.
Imagine you’re at a comedy club, and the first comedian of the night is terrible. You can’t help but check your watch every five minutes, wishing for their set to end. But then, the next comedian comes on stage, and they have you laughing so hard that you’re practically in tears. Finally, the last comedian of the night takes the stage, they’re still pretty funny.
According to the peak-end rule, you’re more likely to remember the experience of the comedy club as a positive one, because at the peak of the experience was the second comedian who was hilarious, and at the end of the experience was the third comedian who was still pretty funny. Even though the first comedian was terrible, they would not be remembered as much and don’t weigh as much as the peak and the end of the experience.
The peak-end rule is a cognitive bias that states people tend to remember the most intense point of an experience (the ‘peak’) and the end of the experience more than the average level of the experience. This means you really want to design your last step to leave a good impression since it influences how people remember and evaluate your product (which is crucial after sign-up and onboarding).
Some ideas of how to implement this principle into your designs include 1) a ‘gift’ when the user tries to exit the site or open another browser tab, 2) encouragement notifications at certain milestones, and 3) a sense of excitement and anticipation for their next session. The timing may be difficult to pull off, but timing around the ‘time-to-value’ would be a good place to start.
Task Abandonment and Re-activation
Now let’s say we’ve done all we can, and despite our best efforts, the user abandons their session before completing all their tasks. We can motivate users to finish their tasks in their next session by understanding the ‘Zeigarnik effect’ and the ‘Ovsiankina effect’. Luckily, if we’ve done everything right with our segmented progress bars, the first effect should already be triggered. The Zeigarnik effect refers to the tendency for people to become more motivated to complete an incomplete task (or remember to come back) once they have started it.
The Ovsiankina effect refers to the phenomenon in which people are more motivated to complete a task if they feel that it is aligned with their values and goals. For example, when a user signs up for Duolingo to learn a new language, they might have a specific goal in mind such as “being able to have a conversation with a native speaker” or “being able to read a book in the target language”.
If Duolingo collects this information during onboarding, and later on in the user’s journey if the system detects that the user’s motivation is low (e.g., a period of inactivity), it can leverage the Ovsiankina effect to improve our push notification with more targeted messaging. Instead of a generic reminder to “Learn 5 new Spanish words today”, it could remind the user of their initial goal for learning Spanish.
By gathering the user’s ‘why’ behind their goal and aligning the in-product tasks with the user’s goal and values, designers can create sources of intrinsic motivation to help users complete tasks related to their goals.
Uber’s Income Targeting and the Arbitrary Endpoint Trap
Of course, these techniques can also be leveraged in other ways. Let’s take Uber’s ‘income targeting’ as an example, and how the hidden ‘arbitrary endpoint trap’ mechanism works. Uber’s income targeting is a feature that allows drivers to set a financial goal for themselves at the start of every workday. So in other words, this pattern encourages the driver to set an arbitrary, but ambitious and achievable goal for themselves. It’s a brilliant (but questionably unethical) way to increase their supplier liquidity (drivers) by getting them to voluntarily ‘round up’ the number of trips they complete in a day.
Duolingo also features this exact same pattern in a more ethical way that aligns user goals with business goals.
The ‘arbitrary endpoint trap’ refers to the phenomenon that occurs when people set arbitrary goals for themselves, and then lose motivation to continue working towards those goals once they are reached. So although these Uber drivers might occasionally ‘round up’ and complete a few additional trips to hit their income target, they would be much less likely to continue after they’ve reached their goal.
To get around this, it’s important to design natural ‘end-of-session hooks’ that would keep your product top of mind, or better yet have them opt into push notifications and emails. A brilliant execution of this is the egg incubator feature in Pokemon Go. Each of these ‘egg slots’ acts as both arbitrary endpoint traps and end-of-session hooks. As you get closer to hatching an egg, you’re likely to keep on walking with the app open to hatch the egg to receive your ‘variable reward’.
However, at the end of your session, when you notice another egg is close to hatching as well, something interesting happens. You’ll subconsciously feel a need to check the app again in a few hours for another potential variable reward. This creates a positive feedback loop where the end of one session motivates the user to start another session. Of course, this is much easier executed in video games, and much more difficult to orchestrate in a consumer product.
By keeping the principles and techniques we discussed in mind, designers can create more motivating and satisfying experiences for users, leading to increased engagement and retention. It’s important to remember that the design process is not just about making a product aesthetically pleasing, but also about understanding how to effectively guide and motivate users toward achieving their goals.
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Thanks for another insightful article Richard! Excited to see new issues :)