Anatomy of a Website Footer: Key Elements, UX Best Practices, and Examples
Definition of a website footer
The footer of a website sits at the very bottom of every single web page and contains links to various types of content on your website. It’s an often overlooked component of a website, but it plays several important roles in your information architecture (IA) – it’s not just some extra thing that gets plonked at the bottom of every page.
Getting your website footer right matters!
The footer communicates to your website visitors that they’ve reached the bottom of the page and it’s also a great place to position important content links that don’t belong anywhere else – within reason. A website footer is not a dumping ground for random content links that you couldn’t find a home for, however there are some content types that are conventionally accessed via the footer e.g., privacy policies and copyright information just to name a few.
Lastly, from a usability and navigation perspective, website footers can serve as a bit of a safety net for lost website visitors. Users might be scrolling and scrolling trying to find something and the footer might be what catches them and guides them back to safety before they give up on your website and go elsewhere. Footers are a functional and important part of your overall IA, but also have their own architecture too.
Read on to learn about the types of content links that might be found in a footer, see some real life examples and discuss some approaches that you might take when testing your footer to ensure that your website is supporting your visitors from top to bottom.
What belongs in a website footer
Deciding which content links belong in your footer depends entirely on your website. The type of footer, its intent and content depends on its audience of your customers, potential customers and more — ie your website visitors. Every website is different, but here’s a list of links to content types that might typically be found in a footer.
Legal content that may include: Copyright information, disclaimer, privacy policy, terms or use or terms of service – always seek appropriate advice on legal content and where to place it!
Your site map
Contact details including social media links and live chat or chat bot access
Customer service content that may include: shipping and delivery details, order tracking, returns, size guides, pricing if you’re a service and product recall information.
Website accessibility details and ways to provide feedback
‘About Us’ type content that may include: company history, team or leadership team details, the careers page and more
Key navigational links that also appear in the main navigation menu that is presented to website visitors when they first land on the page (e.g. at the top or the side)
Website footer examples
Let’s take a look at three diverse real life examples of website footers.
IKEA US
IKEA’s US website has an interesting double barrelled footer that is also large and complex – a ‘fat footer’ as it’s often called – and its structure changes as you travel deeper into the IA. The below image taken from the IKEA US home page shows two clear blocks of text separated by a blue horizontal line. Above the line we have the heading of ‘All Departments’ with four columns showing product categories and below the line there are seven clear groups of content links covering a broad range of topics including customer service information, links that appear in the top navigation menu and careers. At the very bottom of the footer there are social media links and the copyright information for the website.
IKEA US home page footer (accessed May 2019)
As expected, IKEA’s overall website IA is quite large, and as a website visitor clicks deeper into the IA, the footer starts to change. On the product category landing pages, the footer is mostly the same but with a new addition of some handy breadcrumbs to aid navigation (see below image).
IKEA US website footer as it appears on the product category landing page for Textiles & Rugs (accessed May 2019).
When a website visitor travels all the way down to the individual product page level, the footer changes again. In the below image found on the product page for a bath mat, while the blue line and everything below it is still there, the ‘All Departments’ section of the footer has been removed and replaced with non-clickable text on the left hand side that reads as ‘More Bath mats’ and a link on the right hand side that says ‘Go to Bath mats’. Clicking on that link takes the website visitor back to the page above.
IKEA US website footer as it appears on the product page for a bath mat (accessed May 2019).
Overall, evolving the footer content as the website visitor progresses deeper into the IA is an interesting approach - as the main page content becomes more focussed as does the footer while still maintaining multiple supportive safety net features.
M.A.C Cosmetics US
The footer for the US website of this well known cosmetics brand has a four part footer. At first it appears to just have three parts as shown in the image below: a wide section with seven content link categories covering a broad range of content types as the main part with a narrow black strip on either end of it making up the second and third parts. The strip above has loyalty program and live chat links and the strip below contains mostly links to legal content.
MAC Cosmetics US website footer with three parts as it appears on the home page upon first glance (accessed May 2019).
When a website visitor hovers over the ‘Join our loyalty program’ call to action (CTA) in that top narrow strip, the hidden fourth part of the footer which is slightly translucent pulls up like a drawer and sits directly above the strip capping off the top of the main section (as shown in the below image). This section contains more information about the loyalty program and contains further CTAs to join or sign in. It disappears when the cursor is moved away from the hover CTA or it can be collapsed manually via the arrow in the top right hand corner of this fourth part. It’s an interesting and unexpected interaction to have with a footer, but it adds to the overall consistent and cohesive experience of this website because it feels like the footer is an active participant in that experience.
MAC Cosmetics US website footer as it appears on the home page with all four parts visible (accessed May 2019).
Domino’s Pizza US
Domino’s Pizza’s US website has a reasonably flat footer in terms of architecture but it occupies as much space as a more complex or deeper footer. As shown in the image below, its content links are presented horizontally over three rows on the left hand side of the footer and these links are visually separated by forward slashes. It also displays social media links and some advertising content on the right hand side. The most interesting feature of this footer is the large paragraph of text titled ‘Legal Stuff’ below the links. Delightfully it uses direct, clear and plain language and even includes a note about delivery charges not including tips and to ‘Please reward your driver for awesomeness’.
Domino’s Pizza US website footer as it appears on the home page (accessed May 2019).
How to test a website footer
Like every other part of your website, the only way you’re going to know if your footer is supporting your website visitors is if you test it with them. When testing a website’s IA overall, the footer is often excluded. This might be because we want to focus on other areas first or maybe it’s because testing everything at once has the potential to be overwhelming for our research participants.
Testing a footer is fairly easy thing to do and there’s no right or wrong approach – it really does depend on where you are up to in your project, the resources you have available to you and the size and complexity of the footer itself!
If you’re designing a footer for a new website there’s a few ways you might approach ensuring your footer is best supporting your website visitors. If you’re planning to include a large and complex footer, it’s a good idea to start by running an open card sort just on those footer links. An open card sort will help you understand how your website visitors expect those content links in your footer to be grouped and what they think those groups should be called.
If you’re redesigning an existing website, you might first run a tree test on the existing footer to benchmark test it and to pinpoint the exact issues. You might tree test just the footer in the study or you might test the whole website including the footer. Optimal's tree testing is really flexible and you can tree test just a small section of an IA or you can do the whole thing in one go to find out where people are getting lost in the structure. Your approach will depend on your project and what you already know so far. If you suspect there may be issues with the website’s footer, for example, if no one is visiting it and/or you’ve been receiving customer service requests from visitors to help them find content that only lives in the footer, it would be a good idea to consider isolating it for testing. This will help you avoid any competition between the footer and the rest of your IA as well as any potential confusion that may arise from duplicated tree branches (i.e. when your footer contains duplicate labels).
If you’re short on time and there aren’t any known issues with the footer prior to a redesign, you might tree test the entire IA in your benchmark study, iterate your design and then along with everything else, include testing activities for your footer in your moderated usability testing plan. You might include a usability testing scenario or question that requires your participants to complete a task that involves finding content that can only be found in the footer (e.g., shipping information if it’s an ecommerce website). Also keep a close eye on how your participants are moving around the page in general and see if/when the footer comes into play – is it helping people when they’re lost and scrolling? Or is it going unnoticed? If so, why and so on. Talk to your research participants like you would about any other aspect of your website to find out what’s going on there. When resources are tight, use your best judgement and choose the research approach that’s best for your situation, we’ve all had moments where we’ve had to be pragmatic and do our best with what we have.
When you’re at a stage in your design process where you have a visual design or concept for your footer, you could also run a first-click test. First-click tests are quick and easy and will help you determine how your website visitors are faring once they reach your footer and if they can identify the correct content link to complete their task. Studies can be run remotely or in person and just like the rest of the tools in Optimal's user research platform, are super quick to run and great for reaching website visitors all over the world simply by sharing a link to the study.
Despite AI being the buzzword in UX right now, there are still lots of concerns about how it’s going to impact research roles. One of the biggest concerns we hear is: is AI just going to replace UX researchers altogether?
The answer, in our opinion, is no. The longer, more interesting answer is that AI is fundamentally transforming what it means to be a UX researcher, and in ways that make the role more strategic, more impactful, and more interesting than ever before.
What AI Actually Does for Research
A 2024 survey by the UX Research Collective found that 68% of UX researchers are concerned about AI's impact on their roles. The fear makes sense, we've all seen how automation has transformed other industries. But what's actually happening is that rather than AI replacing researchers, it's eliminating the parts of research that researchers hate most.
According to Gartner's 2024 Market Guide for User Research, AI tools can reduce analysis time by 60-70%, but not by replacing human insight. Instead, they handle:
Pattern Recognition at Scale: AI can process hundreds of user interviews and identify recurring themes in hours. For a human researcher that same work would take weeks. But those patterns will need human validation because AI doesn't understand why those patterns matter. That's where researchers will continue to add value, and we would argue, become more important than ever.
Synthesis Acceleration: According to research by the Nielsen Norman Group, AI can generate first-draft insight summaries 10x faster than humans. But these summaries still need researcher oversight to ensure context, accuracy, and actionable insights aren't lost.
Multi-language Analysis: AI can analyze feedback in 50+ languages simultaneously, democratizing global research. But cultural context and nuanced interpretation still require human understanding.
Always-On Insights: Traditional research is limited by human availability. Tools like AI interviewers can run 24/7 while your team sleeps, allowing you to get continuous, high-quality user insights.
AI is Elevating the Role of Researchers
We think that what AI is actually doing is making UX researchers more important, not less. By automating the less sophisticated aspects of research, AI is pushing researchers toward the strategic work that only humans can do.
From Operators to Strategists: McKinsey's 2024 research shows that teams using AI research tools spend 45% more time on strategic planning and only 20% on execution, compared to 30% strategy and 60% execution for traditional teams.
From Reporters to Storytellers: With AI handling data processing, researchers can focus on crafting compelling narratives.
From Analysts to Advisors: When freed from manual analysis, researchers become embedded strategic partners.
Human + AI Collaboration
The most effective research teams aren't choosing between human or AI, they're creating collaborative workflows that incorporate AI to augment researchers roles, not replace them:
AI-Powered Data Collection: Automated transcription, sentiment analysis, and preliminary coding happen in real-time during user sessions.
Human-Led Interpretation: Researchers review AI-generated insights, add context, challenge assumptions, and identify what AI might have missed.
Collaborative Synthesis: AI suggests patterns and themes; researchers validate, refine, and connect to business context.
Human Storytelling: Researchers craft narratives, implications, and recommendations that AI cannot generate.
Is it likely that with AI more and more research tasks will become automated? Absolutely. Basic transcription, preliminary coding, and simple pattern recognition are already AI’s bread and butter. But research has never been about these tasks, it's been about understanding users and driving better decisions and that should always be left to humans.
The researchers thriving in 2025 and beyond aren't fighting AI, they're embracing it. They're using AI to handle the tedious 40% of their job so they can focus on the strategic 60% that creates real business value. You have a choice. You can choose to adopt AI as a tool to elevate your role, or you can view it as a threat and get left behind. Our customers tell us that the researchers choosing elevation are finding their roles more strategic, more impactful, and more essential to product success than ever before.
AI isn't replacing UX researchers. It's freeing them to do what they've always done best, understand humans and help build better products. And in a world drowning in data but starving for insight, that human expertise has never been more valuable.
In the field of user research, every method is either qualitative, quantitative – or both. Understandably, there’s some confusion around these 2 approaches and where the different methods are applicable. This article provides a handy breakdown of the different terms and where and why you’d want to use qualitative or quantitative research methods.
Qualitative research
Let’s start with qualitative research, an approach that’s all about the ‘why’. It’s exploratory and not about numbers, instead focusing on reasons, motivations, behaviors and opinions – it’s best at helping you gain insight and delve deep into a particular problem. This type of data typically comes from conversations, interviews and responses to open questions. The real value of qualitative research is in its ability to give you a human perspective on a research question. Unlike quantitative research, this approach will help you understand some of the more intangible factors – things like behaviors, habits and past experiences – whose effects may not always be readily apparent when you’re conducting quantitative research. A qualitative research question could be investigating why people switch between different banks, for example.
When to use qualitative research
Qualitative research is best suited to identifying how people think about problems, how they interact with products and services, and what encourages them to behave a certain way. For example, you could run a study to better understand how people feel about a product they use, or why people have trouble filling out your sign up form. Qualitative research can be very exploratory (e.g., user interviews) as well as more closely tied to evaluating designs (e.g., usability testing). Good qualitative research questions to ask include:
Why do customers never add items to their wishlist on our website?
How do new customers find out about our services?
What are the main reasons people don’t sign up for our newsletter?
How to gather qualitative data
There’s no shortage of methods to gather qualitative data, which commonly takes the form of interview transcripts, notes and audio and video recordings. Here are some of the most widely-used qualitative research methods:
Usability test – Test a product with people by observing them as they attempt to complete various tasks.
User interview – Sit down with a user to learn more about their background, motivations and pain points.
Contextual inquiry – Learn more about your users in their own environment by asking them questions before moving onto an observation activity.
Focus group – Gather 6 to 10 people for a forum-like session to get feedback on a product.
How many participants will you need?
You don’t often need large numbers of participants for qualitative research, with the average range usually somewhere between 5 to 10 people. You’ll likely require more if you're focusing your work on specific personas, for example, in which case you may need to study 5-10 people for each persona. While this may seem quite low, consider the research methods you’ll be using. Carrying out large numbers of in-person research sessions requires a significant time investment in terms of planning, actually hosting the sessions and analyzing your findings.
Quantitative research
On the other side of the coin you’ve got quantitative research. This type of research is focused on numbers and measurement, gathering data and being able to transform this information into statistics. Given that quantitative research is all about generating data that can be expressed in numbers, there multiple ways you make use of it. Statistical analysis means you can pull useful facts from your quantitative data, for example trends, demographic information and differences between groups. It’s an excellent way to understand a snapshot of your users. A quantitative research question could involve investigating the number of people that upgrade from a free plan to a paid plan.
When to use quantitative research
Quantitative research is ideal for understanding behaviors and usage. In many cases it's a lot less resource-heavy than qualitative research because you don't need to pay incentives or spend time scheduling sessions etc). With that in mind, you might do some quantitative research early on to better understand the problem space, for example by running a survey on your users. Here are some examples of good quantitative research questions to ask:
How many customers view our pricing page before making a purchase decision?
How many customers search versus navigate to find products on our website?
How often do visitors on our website change their password?
How to gather quantitative data
Commonly, quantitative data takes the form of numbers and statistics.
Here are some of the most popular quantitative research methods:
Card sorts – Find out how people categorize and sort information on your website.
First-click tests – See where people click first when tasked with completing an action.
A/B tests – Compare 2 versions of a design in order to work out which is more effective.
Clickstream analysis – Analyze aggregate data about website visits.
How many participants will you need?
While you only need a small number of participants for qualitative research, you need significantly more for quantitative research. Quantitative research is all about quantity. With more participants, you can generate more useful and reliable data you can analyze. In turn, you’ll have a clearer understanding of your research problem. This means that quantitative research can often involve gathering data from thousands of participants through an A/B test, or with 30 through a card sort. Read more about the right number of participants to gather for your research.
Mixed methods research
While there are certainly times when you’d only want to focus on qualitative or quantitative data to get answers, there’s significant value in utilizing both methods on the same research projects.Interestingly, there are a number of research methods that will generate both quantitative and qualitative data. Take surveys as an example. A survey could include questions that require written answers from participants as well as questions that require participants to select from multiple choices.
Looking back at the earlier example of how people move from a free plan to a paid plan, applying both research approaches to the question will yield a more robust or holistic answer. You’ll know why people upgrade to the paid plan in addition to how many. You can read more about mixed methods research in this article:
Now that you know the difference between qualitative and quantitative research, the best way to build confidence is to start testing. Hands-on experience is the fastest path to deeper insight. At Optimal, we make it easy to run your first study, no matter your role or research experience.
As AI takes on a bigger role in product decision-making and user experience design, ethical concerns are becoming more pressing for product teams. From privacy risks to unintended biases and manipulation, AI raises important questions: How do we balance automation with human responsibility? When should AI make decisions, and when should humans stay in control?
These aren't just theoretical questions they have real consequences for users, businesses, and society. A chatbot that misunderstands cultural nuances, a recommendation engine that reinforces harmful stereotypes, or an AI assistant that collects too much personal data can all cause genuine harm while appearing to improve user experience.
The Ethical Challenges of AI
Privacy & Data Ethics
AI needs personal data to work effectively, which raises serious concerns about transparency, consent, and data stewardship:
Data Collection Boundaries – What information is reasonable to collect? Just because we can gather certain data doesn't mean we should.
Informed Consent – Do users really understand how their data powers AI experiences? Traditional privacy policies often don't do the job.
Data Longevity – How long should AI systems keep user data, and what rights should users have to control or delete this information?
Unexpected Insights – AI can draw sensitive conclusions about users that they never explicitly shared, creating privacy concerns beyond traditional data collection.
A 2023 study by the Baymard Institute found that 78% of users were uncomfortable with how much personal data was used for personalized experiences once they understood the full extent of the data collection. Yet only 12% felt adequately informed about these practices through standard disclosures.
Bias & Fairness
AI can amplify existing inequalities if it's not carefully designed and tested with diverse users:
Representation Gaps – AI trained on limited datasets often performs poorly for underrepresented groups.
Algorithmic Discrimination – Systems might unintentionally discriminate based on protected characteristics like race, gender, or disability status.
Performance Disparities – AI-powered interfaces may work well for some users while creating significant barriers for others.
Reinforcement of Stereotypes – Recommendation systems can reinforce harmful stereotypes or create echo chambers.
Recent research from Stanford's Human-Centered AI Institute revealed that AI-driven interfaces created 2.6 times more usability issues for older adults and 3.2 times more issues for users with disabilities compared to general populations, a gap that often goes undetected without specific testing for these groups.
User Autonomy & Agency
Over-reliance on AI-driven suggestions may limit user freedom and sense of control:
Choice Architecture – AI systems can nudge users toward certain decisions, raising questions about manipulation versus assistance.
Dependency Concerns – As users rely more on AI recommendations, they may lose skills or confidence in making independent judgments.
Transparency of Influence – Users often don't recognize when their choices are being shaped by algorithms.
Right to Human Interaction – In critical situations, users may prefer or need human support rather than AI assistance.
A longitudinal study by the University of Amsterdam found that users of AI-powered decision-making tools showed decreased confidence in their own judgment over time, especially in areas where they had limited expertise.
Accessibility & Digital Divide
AI-powered interfaces may create new barriers:
Technology Requirements – Advanced AI features often require newer devices or faster internet connections.
Learning Curves – Novel AI interfaces may be particularly challenging for certain user groups to learn.
Voice and Language Barriers – Voice-based AI often struggles with accents, dialects, and non-native speakers.
Cognitive Load – AI that behaves unpredictably can increase cognitive burden for users.
Accountability & Transparency
Who's responsible when AI makes mistakes or causes harm?
Explainability – Can users understand why an AI system made a particular recommendation or decision?
Appeal Mechanisms – Do users have recourse when AI systems make errors?
Responsibility Attribution – Is it the designer, developer, or organization that bears responsibility for AI outcomes?
Audit Trails – How can we verify that AI systems are functioning as intended?
How Product Owners Can Champion Ethical AI Through UX
At Optimal, we advocate for research-driven AI development that puts human needs and ethical considerations at the center of the design process. Here's how UX research can help:
User-Centered Testing for AI Systems
AI-powered experiences must be tested with real users to identify potential ethical issues:
Longitudinal Studies – Track how AI influences user behavior and autonomy over time.
Diverse Testing Scenarios – Test AI under various conditions to identify edge cases where ethical issues might emerge.
Multi-Method Approaches – Combine quantitative metrics with qualitative insights to understand the full impact of AI features.
Ethical Impact Assessment – Develop frameworks specifically designed to evaluate the ethical dimensions of AI experiences.
Inclusive Research Practices
Ensuring diverse user participation helps prevent bias and ensures AI works for everyone:
Representation in Research Panels – Include participants from various demographic groups, ability levels, and socioeconomic backgrounds.
Contextual Research – Study how AI interfaces perform in real-world environments, not just controlled settings.
Cultural Sensitivity – Test AI across different cultural contexts to identify potential misalignments.
Intersectional Analysis – Consider how various aspects of identity might interact to create unique challenges for certain users.
Transparency in AI Decision-Making
UX teams should investigate how users perceive AI-driven recommendations:
Mental Model Testing – Do users understand how and why AI is making certain recommendations?
Disclosure Design – Develop and test effective ways to communicate how AI is using data and making decisions.
Trust Research – Investigate what factors influence user trust in AI systems and how this affects experience.
Control Mechanisms – Design and test interfaces that give users appropriate control over AI behavior.
The Path Forward: Responsible Innovation
As AI becomes more sophisticated and pervasive in UX design, the ethical stakes will only increase. However, this doesn't mean we should abandon AI-powered innovations. Instead, we need to embrace responsible innovation that considers ethical implications from the start rather than as an afterthought.
AI should enhance human decision-making, not replace it. Through continuous UX research focused not just on usability but on broader human impact, we can ensure AI-driven experiences remain ethical, inclusive, user-friendly, and truly beneficial.
The most successful AI implementations will be those that augment human capabilities while respecting human autonomy, providing assistance without creating dependency, offering personalization without compromising privacy, and enhancing experiences without reinforcing biases.
A Product Owner's Responsibility: Leading the Charge for Ethical AI
As UX professionals, we have both the opportunity and responsibility to shape how AI is integrated into the products people use daily. This requires us to:
Advocate for ethical considerations in product requirements and design processes
Develop new research methods specifically designed to evaluate AI ethics
Collaborate across disciplines with data scientists, ethicists, and domain experts
Educate stakeholders about the importance of ethical AI design
Amplify diverse perspectives in all stages of AI development
By embracing these responsibilities, we can help ensure that AI serves as a force for positive change in user experience enhancing human capabilities while respecting human values, autonomy, and diversity.
The future of AI in UX isn't just about what's technologically possible; it's about what's ethically responsible. Through thoughtful research, inclusive design practices, and a commitment to human-centered values, we can navigate this complex landscape and create AI experiences that truly benefit everyone.
