In September of 2020, Netflix released a new documentary called The Social Dilemma. For many viewers, much of the information likely came as a surprise. Could social media and technology really be behind some of the biggest societal changes and rifts in the past decade?

For UX designers and UXers working in technology, the documentary likely surfaced these feelings – and more. And, after viewing the Social Dilemma, many of these same people will be asking themselves 2 questions: “Am I part of the problem?” and “How do I fix this?”.

The social dilemma: Some not-so-light viewing material

If you haven’t already seen it, the documentary is worth watching. It posits that technology (but more specifically social media) is influencing the way people think, leading to real-world impacts. The growing political divides, fracturing of democracies and rise in mental illness all have a direct line to technology.

With that said, the documentary does dramatize the issue somewhat and doesn’t give much weight to the positive effects that technology and social media have had on our society. It can’t be discounted just how useful social media has been for organizing positive movements and bringing people together as a force for good.

Now that we’ve recapped the documentary (you should definitely still watch it if you haven’t already), it’s time we explore exactly what’s going on here – and what you can do about it.

Persuasive design and technology

The next time you open a messaging app to talk to a friend, notice everything that’s layered over the core function of the app (to facilitate a conversation between 2 people). In the case of Facebook Messenger, there’s the ‘Active Status’ function to show you which of your contacts are online and when they were last online, chat bubbles to indicate that someone is typing and ‘Read receipts’ to indicate whether or not someone has read a message that you’ve sent.

These elements of user interfaces fall under a broader category called persuasive technology or persuasive design.

So what is persuasive technology?

According to the Interaction Design Foundation, persuasive design is an area of design practice focused on influencing user behavior through the characteristics of a product or service.

“Based on psychological and social theories, persuasive design is often used in e-commerce, organizational management, and public health. However, designers also tend to use it in any field requiring a target group’s long-term engagement by encouraging continued custom,” the Foundation notes.

Media has always had a large part to play in influencing human behavior, but the rapid proliferation of interactive technology in the 21st century has meant that the potential for technology to influence how we think and act has increased immensely.

“The advancing sophistication of resources available to designers means tailoring the user experience by weaving persuasive elements into it is achievable in increasingly discreet ways than were available in earlier years.”

This area of design was first pioneered by the Director of the Persuasive Technology Lab at Stanford University, B. J. Fogg. By understanding core factors such as motivation, triggers and ability, Fogg explains that designers can achieve their desired behaviors in users without needing to resort to tactics like deception and coercion.

The Social Dilemma reveals the dark side of persuasive design. So-called dark patterns run rife through social media apps; the previously-mentioned chat bubbles and the ‘pull to refresh feature’ (mirroring a slot machine) are just 2 examples.

The silver lining

By now it should be clear that persuasive design isn’t a force for evil – far from it, in fact. This subset of design can – and is – used for many positive purposes, like apps that encourage you to stand up, drink water and go for a walk. This makes persuasive design a useful area to understand – both for awareness of dark patterns and for the many ways in which these approaches can be used for good.

Guide: How to fix the problem

As much as many of us are drawn to the idea of the quick fix, fixing the problems we’ve outlined above will take time – and commitment. We’ve pulled together some thinking and resources for web designers, user researchers, usability testing experts and more.

Design ethics

When considering the implications of persuasive design, it’s a good idea to take one step back and think about design ethics. Trine Falbe, writing for Smashing Magazine, describes ethical design as “design made with the intent to do good”. 

Understandably, there’s a large number of areas that designers (and researchers) will want to consider when thinking about design ethics, including:

• Privacy
• Accessibility
• Usability
• Sustainability
• User involvement
• Focus

A List Apart has a great article which expands some of these areas and more. There’s also this article on Medium: ‘How to Design With Ethics’.

User research

Sitting beneath user involvement is UX research or user research. Primarily, UX research involves using various research methods to gather information about your end users. This is obviously useful from a design and product point of view, allowing us to test new functionality and draw out new insights.

Whether conducting usability tests or user interviews, user research is the best way to connect with the people you’re developing your product or service for.

From an ethical design standpoint, we need to consider both how we communicate with our users and what we do with the research data that we collect by talking to them. The Little Book of Design Research Ethics covers some of the key principles to follow when carrying out design research.

Practice good user habits

Beyond the work you do as a designer of products and services, you can also practice better user habits to build up your understanding of just how persuasive some of these persuasive design techniques can be.

Here are some things to try:

• Cut back: Turn off notifications for pesky apps and uninstall social apps from your phone or tablet.
• Change how you get news: Instead of relying on news delivered through your social feeds, find a selection of news websites and visit them directly.
• Reach for a book instead of your phone: When there’s a lull in whatever it is you're doing, think before you reach for your phone.
• Share more with friends and family, not your feed: Self explanatory. Reach out to your friends and family when you have exciting news to share, not your social media accounts. 

Wrap up

The social dilemma has raised some interesting questions about the ethicacy of modern technology – particularly social media. Technology can be a powerful force for good, but as we’ve seen, there are downsides and dark patterns we cannot afford ignore.

As UX designers and researchers, you’ve got a lot of power to drive positive change within your community and organization. Change can start in your next user interface design meeting.

Summary: A detailed UX research plan helps you keep your overarching research goals in mind as you work through the logistics of a research project.

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There’s nothing quite like the feeling of sitting down to interview one of your users, steering the conversation in interesting directions and taking note of valuable comments and insights. But, as every researcher knows, it’s also easy to get carried away. Sometimes, the very process of user research can be so engrossing that you forget the reason you’re there in the first place, or unexpected things that come up that can force you to change course or focus.

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This is where a UX research plan comes into play. Taking the time to set up a detailed overview of your high-level research goals, team, budget and timeframe will give your research the best chance of succeeding. It's also a good tool for fostering alignment - it can make sure everyone working on the project is clear on the objectives and timeframes. Over the course of your project, you can refer back to your plan – a single source of truth. After all, as Benjamin Franklin famously said: “By failing to prepare, you are preparing to fail”.

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In this article, we’re going to take a look at the best way to put together a research plan.

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Your research recipe for success

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Any project needs a plan to be successful, and user research is no different. As we pointed out above, a solid plan will help to keep you focused and on track during your research – something that can understandably become quite tricky as you dive further down the research rabbit hole, pursuing interesting conversations during user interviews and running usability tests. Thought of another way, it’s really about accountability. Even if your initial goal is something quite broad like “find out what’s wrong with our website”, it’s important to have a plan that will help you to identify when you’ve actually discovered what’s wrong.

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So what does a UX research plan look like? It’s basically a document that outlines the where, why, who, how and what of your research project.

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It’s time to create your research plan! Here’s everything you need to consider when putting this plan together.

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Make a list of your stakeholders

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The first thing you need to do is work out who the stakeholders are on your project. These are the people who have a stake in your research and stand to benefit from the results. In those instances where you’ve been directed to carry out a piece of research you’ll likely know who these people are, but sometimes it can be a little tricky. Stakeholders could be C-level executives, your customer support team, sales people or product teams. If you’re working in an agency or you’re freelancing, these could be your clients.

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Make a list of everyone you think needs to be consulted and then start setting up catch-up sessions to get their input. Having a list of stakeholders also makes it easy to deliver insights back to these people at the end of your research project, as well as identify any possible avenues for further research. This also helps you identify who to involve in your research (not just report findings back to).

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Action: Make a list of all of your stakeholders.

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Write your research questions

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Before we get into timeframes and budgets you first need to determine your research questions, also known as your research objectives. These are the ‘why’ of your research. Why are you carrying out this research? What do you hope to achieve by doing all of this work? Your objectives should be informed by discussions with your stakeholders, as well as any other previous learnings you can uncover. Think of past customer support discussions and sales conversations with potential customers.

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Here are a few examples of basic research questions to get you thinking. These questions should be actionable and specific, like the examples we’ve listed here:

• “How do people currently use the wishlist feature on our website?”
• “How do our current customers go about tracking their orders?”
• “How do people make a decision on which power company to use?”
• “What actions do our customers take when they’re thinking about buying a new TV?”

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A good research question should be actionable in the sense that you can identify a clear way to attempt to answer it, and specific in that you’ll know when you’ve found the answer you’re looking for. It's also important to keep in mind that your research questions are not the questions you ask during your research sessions - they should be broad enough that they allow you to formulate a list of tasks or questions to help understand the problem space.

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Action: Create a list of possible research questions, then prioritize them after speaking with stakeholders.

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What is your budget?

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Your budget will play a role in how you conduct your research, and possibly the amount of data you're able to gather.

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Having a large budget will give you flexibility. You’ll be able to attract large numbers of participants, either by running paid recruitment campaigns on social media or using a dedicated participant recruitment service. A larger budget helps you target more people, but also target more specific people through dedicated participant services as well as recruitment agencies.

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Note that more money doesn't always equal better access to tools - e.g. if I work for a company that is super strict on security, I might not be able to use any tools at all. But it does make it easier to choose appropriate methods and that allow you to deliver quality insights. E.g. a big budget might allow you to travel, or do more in-person research which is otherwise quite expensive.

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With a small budget, you’ll have to think carefully about how you’ll reward participants, as well as the number of participants you can test. You may also find that your budget limits the tools you can use for your testing. That said, you shouldn’t let your budget dictate your research. You just have to get creative!

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Action: Work out what the budget is for your research project. It’s also good to map out several cheaper alternatives that you can pursue if required.

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How long will your project take?

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How long do you think your user research project will take? This is a necessary consideration, especially if you’ve got people who are expecting to see the results of your research. For example, your organization’s marketing team may be waiting for some of your exploratory research in order to build customer personas. Or, a product team may be waiting to see the results of your first-click test before developing a new signup page on your website.

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It’s true that qualitative research often doesn’t have a clear end in the way that quantitative research does, for example as you identify new things to test and research. In this case, you may want to break up your research into different sub-projects and attach deadlines to each of them.

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Action: Figure out how long your research project is likely to take. If you’re mixing qualitative and quantitative research, split your project timeframe into sub-projects to make assigning deadlines easier.

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Understanding participant recruitment

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Who you recruit for your research comes from your research questions. Who can best give you the answers you need? While you can often find participants by working with your customer support, sales and marketing teams, certain research questions may require you to look further afield.

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The methods you use to carry out your research will also have a part to play in your participants, specifically in terms of the numbers required. For qualitative research methods like interviews and usability tests, you may find you’re able to gather enough useful data after speaking with 5 people. For quantitative methods like card sorts and tree tests, it’s best to have at least 30 participants. You can read more about participant numbers in this Nielsen Norman article.

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At this stage of the research plan process, you’ll also want to write some screening questions. These are what you’ll use to identify potential participants by asking about their characteristics and experience.

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Action: Define the participants you’ll need to include in your research project, and where you plan to source them. This may require going outside of your existing user base.

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Which research methods will you use?

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The research methods you use should be informed by your research questions. Some questions are best answered by quantitative research methods like surveys or A/B tests, with others by qualitative methods like contextual inquiries, user interviews and usability tests. You’ll also find that some questions are best answered by multiple methods, in what’s known as mixed methods research.

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If you’re not sure which method to use, carefully consider your question. If we go back to one of our earlier research question examples: “How do our current customers go about tracking their orders?”, we’d want to test the navigation pathways.

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If you’re not sure which method to use, it helps to carefully consider your research question. Let’s use one of our earlier examples: “Is it easy for users to check their order history in our iPhone app?” as en example. In this case, because we want to see how users move through our app, we need a method that’s suited to testing navigation pathways – like tree testing.

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For the question: “What actions do our customers take when they’re thinking about buying a new TV?”, we’d want to take a different approach. Because this is more of an exploratory question, we’re probably best to carry out a round of user interviews and ask questions about their process for buying a TV.

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Action: Before diving in and setting up a card sort, consider which method is best suited to answer your research question.

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Develop your research protocol

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A protocol is essentially a script for your user research. For the most part, it’s a list of the tasks and questions you want to cover in your in-person sessions. But, it doesn’t apply to all research types. For example, for a tree test, you might write your tasks, but this isn't really a script or protocol.

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Writing your protocol should start with actually thinking about what these questions will be and getting feedback on them, as well as:

• The tasks you want your participants to do (usability testing)
• How much time you’ve set aside for the session
• A script or description that you can use for every session
• Your process for recording the interviews, including how you’ll look after participant data.

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Action: This is essentially a research plan within a research plan – it’s what you’d take to every session.

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Happy researching!

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Related UX plan reading

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• What is mixed methods research? – Learn how a mixture of qualitative and quantitative research methods can set your research project up for success.
• How to encourage people to participate in your study – Attracting participants to your study can be a tough undertaking. We’ve got a few different approaches to help.
• Which comes first: card sorting or tree testing? – Card sorting, tree testing, which one should you use first? And why does it matter?
• How to write great questions for your research – Learn how you can craft effective questions for your next research project.
• 5 ways to increase user research in your organization – One of the best ways to get other teams and stakeholders on board with your own research projects is by helping to spread awareness of the benefits of user research.
• How to lead a UX team – With user-centered design continuing to grow in organizations across the globe, we’re going to need skilled UX leaders.
• How to benchmark your information architecture – Before embarking on any major information architecture projects, learn how you can benchmark your existing one.

AI-driven personalization is redefining digital experiences, allowing companies to tailor content, recommendations, and interfaces to individual users at an unprecedented scale. From e-commerce product suggestions to content feeds, streaming recommendations, and even customized user interfaces, personalization has become a cornerstone of modern digital strategy. The appeal is clear: research shows that effective personalization can increase engagement by 72%, boost conversion rates by up to 30%, and drive revenue growth of 10-15%.

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However, the reality often falls short of these impressive statistics. Personalization can easily backfire, frustrating users instead of engaging them, creating experiences that feel invasive rather than helpful, and sometimes actively driving users away from the very content or products they might genuinely enjoy. Many organizations invest heavily in AI technology while underinvesting in understanding how these personalized experiences actually impact their users.

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The Widening Gap Between Capability and Quality

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The technical capability to personalize digital experiences has advanced rapidly, but the quality of these experiences hasn't always kept pace. According to a 2023 survey by Baymard Institute, 68% of users reported encountering personalization that felt "off-putting" or "frustrating" in the previous month, while only 34% could recall a personalized experience that genuinely improved their interaction with a digital product.

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This disconnect stems from a fundamental misalignment: while AI excels at pattern recognition and prediction based on historical data, it often lacks the contextual understanding and nuance that make personalization truly valuable. The result? Technically sophisticated personalization regularly misses the mark on actual user needs and preferences.

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The Pitfalls of AI-Driven Personalization

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Many companies struggle with personalization due to several common pitfalls that undermine even the most sophisticated AI implementations:

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Over-Personalization: When Helpful Becomes Restrictive

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AI that assumes too much can make users feel restricted or trapped in a "filter bubble" of limited options. This phenomenon, often called "over-personalization," occurs when algorithms become too confident in their understanding of user preferences.

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Signs of over-personalization include:

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• Content feeds that become increasingly homogeneous over time
• Disappearing options that might interest users but don't match their history
• User frustration at being unable to discover new content or products
• Decreased engagement as experiences become predictable and stale

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A study by researchers at University of Minnesota found that highly personalized news feeds led to a 23% reduction in content diversity over time, even when users actively sought varied content. This "filter bubble" effect not only limits discovery but can leave users feeling manipulated or constrained.

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Incorrect Assumptions: When Data Tells the Wrong Story

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AI recommendations based on incomplete or misinterpreted data can lead to irrelevant, inappropriate, or even offensive suggestions. These incorrect assumptions often stem from:

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• Limited data points that don't capture the full context of user behavior
• Misinterpreting casual interest as strong preference
• Failing to distinguish between the user's behavior and actions taken on behalf of others
• Not recognizing temporary or situational needs versus ongoing preferences

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These misinterpretations can range from merely annoying (continuously recommending products similar to a one-time purchase) to deeply problematic (showing weight loss ads to users with eating disorders based on their browsing history).

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A particularly striking example occurred when a major retailer's algorithm began sending pregnancy-related offers to a teenage girl before her family knew she was pregnant. While technically accurate in its prediction, this incident highlights how even "correct" personalization can fail to consider the broader human context and implications.

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Lack of Transparency: The Black Box Problem

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Users increasingly want to understand why they're being shown specific content or recommendations. When personalization happens behind a "black box" without explanation, it can create:

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• Distrust in the system and the brand behind it
• Confusion about how to influence or improve recommendations
• Feelings of being manipulated rather than assisted
• Concerns about what personal data is being used and how

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Research from the Pew Research Center shows that 74% of users consider it important to know why they are seeing certain recommendations, yet only 22% of personalization systems provide clear explanations for their suggestions.

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Inconsistent Experiences Across Channels

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Many organizations struggle to maintain consistent personalization across different touchpoints, creating disjointed experiences:

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• Product recommendations that vary wildly between web and mobile
• Personalization that doesn't account for previous customer service interactions
• Different personalization strategies across email, website, and app experiences
• Recommendations that don't adapt to the user's current context or device

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This inconsistency can make personalization feel random or arbitrary rather than thoughtfully tailored to the user's needs.

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Neglecting Privacy Concerns and Control

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As personalization becomes more sophisticated, user concerns about privacy intensify. Key issues include:

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• Collecting more data than necessary for effective personalization
• Lack of user control over what information influences their experience
• Unclear opt-out mechanisms for personalization features
• Personalization that reveals sensitive information to others

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A recent study found that 79% of users want control over what personal data influences their recommendations, but only 31% felt they had adequate control in their most-used digital products.

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How Product Managers Can Leverage UX Insight for Better AI Personalization

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To create a personalized experience that feels natural and helpful rather than creepy or restrictive, UX teams need to validate AI-driven decisions through systematic research with real users. Rather than treating personalization as a purely technical challenge, successful organizations recognize it as a human-centered design problem that requires continuous testing and refinement.

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Understanding User Mental Models Through Card Sorting & Tree Testing

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Card sorting and tree testing help structure content in a way that aligns with users' expectations and mental models, creating a foundation for personalization that feels intuitive rather than imposed:

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• Open and Closed Card Sorting – Helps understand how different user segments naturally categorize content, products, or features, providing a baseline for personalization strategies
• Tree Testing – Validates whether personalized navigation structures work for different user types and contexts
• Hybrid Approaches – Combining card sorting with interviews to understand not just how users categorize items, but why they do so

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Case Study: A financial services company used card sorting with different customer segments to discover distinct mental models for organizing financial products. Rather than creating a one-size-fits-all personalization system, they developed segment-specific personalization frameworks that aligned with these different mental models, resulting in a 28% increase in product discovery and application rates.

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Validating Interaction Patterns Through First-Click Testing

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First-click testing ensures users interact with personalized experiences as intended across different contexts and scenarios:

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• Testing how users respond to personalized elements vs. standard content
• Evaluating whether personalization cues (like "Recommended for you") influence click behavior
• Comparing how different user segments respond to the same personalization approaches
• Identifying potential confusion points in personalized interfaces

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Research by the Nielsen Norman Group found that getting the first click right increases the overall task success rate by 87%. For personalized experiences, this is even more critical, as users may abandon a site entirely if early personalized recommendations seem irrelevant or confusing.

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Gathering Qualitative Insights Through User Interviews & Usability Testing

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Direct observation and conversation with users provides critical context for personalization strategies:

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• Moderated Usability Testing – Reveals how users react to personalized elements in real-time
• Think-Aloud Protocols – Help understand users' expectations and reactions to personalization
• Longitudinal Studies – Track how perceptions of personalization change over time and repeated use
• Contextual Inquiry – Observes how personalization fits into users' broader goals and environments

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These qualitative approaches help answer critical questions like:

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• When does personalization feel helpful versus intrusive?
• What level of explanation do users want for recommendations?
• How do different user segments react to similar personalization strategies?
• What control do users expect over their personalized experience?

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Measuring Sentiment Through Surveys & User Feedback

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Systematic feedback collection helps gauge users' comfort levels with AI-driven recommendations:

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• Targeted Microsurveys – Quick pulse checks after personalized interactions
• Preference Centers – Direct input mechanisms for refining personalization
• Satisfaction Tracking – Monitoring how personalization affects overall satisfaction metrics
• Feature-Specific Feedback – Gathering input on specific personalization features

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A streaming service discovered through targeted surveys that users were significantly more satisfied with content recommendations when they could see a clear explanation of why items were suggested (e.g., "Because you watched X"). Implementing these explanations increased content exploration by 34% and reduced account cancellations by 8%.

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A/B Testing Personalization Approaches

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Experimental validation ensures personalization actually improves key metrics:

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• Testing different levels of personalization intensity
• Comparing explicit versus implicit personalization methods
• Evaluating various approaches to explaining recommendations
• Measuring the impact of personalization on both short and long-term engagement

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Importantly, A/B testing should look beyond immediate conversion metrics to consider longer-term impacts on user satisfaction, trust, and retention.

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Building a User-Centered Personalization Strategy That Works

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To implement personalization that truly enhances user experience, organizations should follow these research-backed principles:

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1. Start with User Needs, Not Technical Capabilities

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The most effective personalization addresses genuine user needs rather than showcasing algorithmic sophistication:

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• Identify specific pain points that personalization could solve
• Understand which aspects of your product would benefit most from personalization
• Determine where users already expect or desire personalized experiences
• Recognize which elements should remain consistent for all users

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2. Implement Transparent Personalization

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Users increasingly expect to understand and control how their experiences are personalized:

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• Clearly communicate what aspects of the experience are personalized
• Explain the primary factors influencing recommendations
• Provide simple mechanisms for users to adjust or reset their personalization
• Consider making personalization opt-in for sensitive domains

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3. Design for Serendipity and Discovery

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Effective personalization balances predictability with discovery:

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• Deliberately introduce variety into recommendations
• Include "exploration" categories alongside highly targeted suggestions
• Monitor and prevent increasing homogeneity in personalized feeds over time
• Allow users to easily branch out beyond their established patterns

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4. Apply Progressive Personalization

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Rather than immediately implementing highly tailored experiences, consider a gradual approach:

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• Begin with light personalization based on explicit user choices
• Gradually introduce more sophisticated personalization as users engage
• Calibrate personalization depth based on relationship strength and context
• Adjust personalization based on user feedback and behavior

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5. Establish Continuous Feedback Loops

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Personalization should never be "set and forget":

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• Implement regular evaluation cycles for personalization effectiveness
• Create easy feedback mechanisms for users to rate recommendations
• Monitor for signs of over-personalization or filter bubbles
• Regularly test personalization assumptions with diverse user groups

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The Future of Personalization: Human-Centered AI

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As AI capabilities continue to advance, the companies that will succeed with personalization won't necessarily be those with the most sophisticated algorithms, but those who best integrate human understanding into their approach. The future of personalization lies in creating systems that:

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• Learn from qualitative human feedback, not just behavioral data
• Respect the nuance and complexity of human preferences
• Maintain transparency in how personalization works
• Empower users with appropriate control
• Balance algorithm-driven efficiency with human-centered design principles

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AI should learn from real people, not just data. UX research ensures that personalization enhances, rather than alienates, users by bringing human insight to algorithmic decisions.

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By combining the pattern-recognition power of AI with the contextual understanding provided by UX research, organizations can create personalized experiences that feel less like surveillance and more like genuine understanding: experiences that don't just predict what users might click, but truly respond to what they need and value.

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