How to Leverage Anticipatory Design to Create Better Products
What is anticipatory design? How can product managers use it to develop better products?
If you are after the answers to these questions, you are in the right place!
In the article, we explore what anticipatory design is, discuss its benefits, identify its elements and look at a few good examples in B2B and B2C products.
Are you ready to dive in? Let’s get to it!
TL;DR
- Anticipatory design focuses on predicting user needs before they even experience them. When that happens, the solutions are ready and available to use.
- Anticipatory design aims to remove friction from the user journey and improve efficiency by eliminating unnecessary choices.
- Predicting user requirements allows you to identify the happy path for them and help them experience value in less time.
- Anticipatory design promotes simplified user interfaces with fewer options to reduce cognitive overload.
- The approach is very customer-centric so it helps you design products that not only are functional but also satisfy users’ psychological needs. This improves overall customer satisfaction.
- To be effective, anticipatory design needs data. You can get it from users directly by asking them to fill in forms or provide feedback. Some of the most reliable data come from watching users use the product.
- Modern products use machine learning algorithms to improve the accuracy of predictions. The algorithms ‘learn’ about user behavior patterns and all the adjustments to the user experience happen automatically. However, you can still use anticipatory design even if you don’t have the resources to implement AI.
- Powerful technology, like machine learning and AI, is of limited use if you don’t use along the key design principles of familiarity, consistency, predictability, and usability.
- To comply with legal requirements, you need to store user data securely.
- Use the data to enrich user experience instead of limiting it, and make sure not to cross ethical boundaries by excessively interfering in people’s private lives.
Some good examples of anticipatory design applications include:
- Canva uses anticipatory design to simplify and personalize user dashboards so they can create their artwork in less time.
- Kommunicate uses user data to identify friction points and contextual interactive walkthroughs to smoothen the user journey.
- Netflix uses powerful machine learning algorithms to recommend the best content for its viewers.
- Uber anticipates user journeys and speeds up lift-booking by storing addresses and providing shortcuts to the most common features.
- Curious how Userpilot can help you anticipate and meet user needs? Book the demo!
What is anticipatory design?
Aaron Shapiro, the co-founder and the CEO of Huge, defined anticipatory design as a ‘design that is one step ahead of you.’
Cool, but what does this mean in practice?
It is an approach that involves predicting future user needs and designing ways to satisfy them based on existing data, like previous choices. As a result, the solutions are ready and present themselves at the exact moment the user experiences the need.
How does anticipatory design help create better products?
The three main principles of anticipatory design are:
- flow, not friction
- convenience, not choice
- efficiency, not freedom
In addition to that, anticipatory design relies on personal data, is based on human psychology, and uses AI. The combination makes it sound almost Orwellian.
Not quite. In fact, anticipatory design can benefit users a lot.
Faster time to value with less friction across the user journey
To start with, anticipatory design aims to remove friction from the user journey and reduce the time to value.
This is done by restricting the choices available to the user. However, it’s not about removing the choice altogether. Instead, it is about getting rid of the irrelevant choices that distract the user from completing their tasks.
How do you achieve that?
It starts by anticipating what objectives your users may want to achieve. Next, you identify the best ways for the particular user segment to reach its goal. We call such an optimized route the happy path.
Finally, you present them only with the options that will help them complete the journey as quickly as possible.
Anticipatory design example of simplified user interfaces
Let’s look at the Notion branched onboarding flows.
As you can see, the users have only two choices. This depends on who they are and how they’re planning to use the application.
Each of the options takes them down one of the onboarding flows. Each of these leads them to their activation points in the most direct way.
Decluttered user interfaces that reduce cognitive load
Have you ever heard of the paradox of choice?
Basically, having the choice is important for people to feel content unless there are too many options available. If that’s the case, it may overwhelm the user and lead to product fatigue in the long run.
By simplifying the processes and removing needless choices, anticipatory design reduces the mental effort needed to complete the task.
If you manage to solve the problem of cognitive overload by decluttering and personalizing the user interface, you improve usability and make the experience more enjoyable.
Anticipatory design example with reduced cognitive load
Empty states are just the opposite of what we’re trying to avoid. There’s nothing there to get the user going, so why don’t we start here?
Just add a relevant CTA to the page to set them off on the user journey.
However, never ask them to focus on more than one thing at a time. Only when they complete the action, reveal the next step, and then the next, until they adopt the product.
Anticipating user needs will reduce the time spent on a task
If you can predict user needs accurately, you can also design solutions that will help them complete their jobs faster.
Anticipatory design example of reducing time on task
Pre-filled forms are a great example of how anticipatory design can save time.
First, the web browser or another app stores your data. Whenever you need to complete a form, it offers to pre-fill it for you so that you don’t have to enter the data manually again.
This can smoothen some repetitive sequences of actions like shopping.
You can go even further and instead of leading the user step by step, you can take them directly to their destination.
That’s what online retailers like Amazon or Ebay do with their Buy Now buttons. You hit the button and the purchase is ready. No need to go through all the other steps like entering the payment method details or the delivery address.
Using anticipatory design leads to an overall improved customer satisfaction
Anticipatory design helps teams build products that customers love.
How?
It has a solid foundation in human psychology. It is a very customer-centered approach that offers a personalized experience and solves unique user needs.
As a result, it doesn’t only allow users to complete their tasks with the least possible effort. It also satisfies the need to be understood and feel important, and amplifies the positive experience with surprise.
Anticipatory design elements
What do you need to be aware of when implementing anticipatory design? Let’s look at a few design aspects.
Data collection to ”inform” the design
User data is the key to the effectiveness of anticipatory design. You can’t make accurate predictions without knowing user habits and their previous preferences.
The data often comes directly from the user.
For example, during the sign-up process you can ask them to provide you with the details necessary to design the best onboarding flow for them.
User feedback is another source of valuable insights, so make sure to collect it regularly and offer the user a chance to provide passive feedback.
The most objective data comes from tracking user behavior in the product. By observing how they engage with the product, you can identify behavior patterns and use them to optimize your predictions and the user experience.
Machine learning algorithms to ”predict”
Apart from data collection, anticipatory design uses machine learning algorithms to predict users’ needs and their preferred choices.
AI analyzes historic information, like past orders, and contextual information, like the time of the day or weather, to make predictions. Whenever it receives more user input, it automatically adjusts the predictions to make them more accurate.
While it’s more easily available than before, Machine Learning has one main drawback: the cost.
Fear not though, as there are still ways to anticipate user needs if you don’t have the resources to incorporate machine learning into your product.
Would you like to see some examples?
Check out the final sections below.
User experience design principles to ”guide”
Even if you have access to machine learning algorithms, you still need to pay attention to the basic UX design principles.
Remember that your focus is on solving user problems in the simplest and most direct way. Prioritize familiarity, consistency, predictability, and usability, and only use AI to enhance the experience to delight users.
Data security and ethical considerations
Collecting data to learn and predict user needs comes with a lot of responsibility.
To begin with, you need to store it in a safe way and protect it from breaches that would compromise your customers’ safety and well-being.
Such breaches could have serious legal implications. For example, you could be liable under the GDPR even if you are not based in the European Union.
Anticipatory design, especially when powered by algorithms, can create filter bubbles that restrict user experiences instead of enriching them.
For example, a playlist recommendation may be a good way to discover new songs that match user tastes. However, if it’s the only way they find new music, they will end up listening to similar music all the time.
Finally, there’s the really creepy stuff like political marketers targeting users with tailored messages that play up their fears and phobias or online retailers exploiting customer trust and private data to sell their products.
B2B Examples of anticipatory design in practice
Let’s look at some examples of anticipatory design that don’t require sophisticated machine learning algorithms.
Canva personalized getting-started screen
Canva, like many other SaaS products, collects data about user goals during the sign-up process.
Next, they use the information to design relevant onboarding experiences and a personalized dashboard that allow the user to jump straight into their workflows. Simple but effective.
All Canva use is customer input without the need for any algorithms. The customers tell them what they want to achieve, and Canva makes the process simple and intuitive.
Kommunicate contextual interactive walkthroughs
Kommunicate, the Chat-based Customer Support tool, uses anticipatory design to improve user onboarding and boost adoption.
First, they collected product usage data by recording user interactions with their product. This allowed them to identify the friction points.
Next, they used the knowledge to design a checklist that guided the user through the setup process as this posed the biggest challenge to them.
They also used Userpilot to develop an interactive walkthrough. The unique part about it is its contextualization. The guidance is triggered at the exact time the user needs it.
This distinguishes it from product tours which don’t differentiate between different users and show them the same guidance whether they need it or not.
B2C Examples of anticipatory design in practice
How about B2C products? They too apply anticipatory design to great effect to improve user experience.
Netflix recommendations
Netflix is one of the best-known examples of anticipatory design.
To start with, the streaming platform uses the signup process to collect data about user content preferences.
Powerful machine learning algorithms use the data to create a curated list of recommended titles that match the tastes of the users.
That’s not the end though. As the user continues to use the product, the AI learns more about their preferences and automatically adjusts the content recommendations.
Uber anticipating your route
Uber has revolutionized the way we move around cities. Every day it helps users all over the world help users get from one place to another faster and with less hassle, and anticipatory design has contributed greatly to their success.
By analyzing your travel habits, Uber can anticipate where you may want to go next. For example, if you’ve traveled somewhere, there is a 90% chance you will want to get back to the starting point.
To simplify the process of booking your return lift, they’ve included the return button in the UI. It appears automatically after you’ve finished a trip.
They also automatically label the location with most pick-ups and drop-offs as home and store addresses, so that you don’t have to manually enter the destinations every time your order a lift.
Thanks to that and the simple yet functional user interface, ordering a lift is dead easy.
Conclusion
The approach called anticipatory design allows product teams to develop products that delight users. More importantly, they enable them to complete their tasks easily and quickly by limiting available choices and simplifying the decision-making process.
If you would like to learn how Userpilot can help you design appealing UI experiences and guide your users to value in the most direct way, book a demo!
