Interview Questions for IA

Information Architecture (IA) is the structural design of shared information environments. It’s about organizing and labeling websites, apps, and other digital products so users can find what they need quickly and intuitively. Think of it as the blueprint for a user’s journey through a digital space. The core principles revolve around:

• Organization: Structuring content in a logical and meaningful way. This might involve hierarchical structures (like a tree) or faceted classifications (allowing users to filter and refine their search).
• Labeling: Using clear, concise, and consistent language for navigation and content identification. This helps users understand where they are and where they can go.
• Navigation: Providing intuitive pathways for users to move through the information space. This includes menus, breadcrumbs, search functions, and other aids.
• Search: Enabling users to easily find specific information using keywords or filters. A robust search function is critical for large information spaces.
• Accessibility: Ensuring the IA is usable by all users, regardless of their abilities or technological limitations. This includes considerations for users with disabilities.

Essentially, good IA makes finding information effortless, much like finding a specific book in a well-organized library.

Several IA models exist, each suited to different contexts. Choosing the right model depends on the complexity and nature of the content.

• Hierarchical: This is the most common model, organizing information in a tree-like structure with parent-child relationships. Think of website menus – they usually follow a hierarchical structure. It’s ideal for relatively simple websites or applications with clear, well-defined categories.
• Faceted: This model allows users to filter and refine their searches using multiple criteria or facets. For example, an e-commerce site might let users filter products by brand, price, color, etc. This model is excellent for complex information spaces with many attributes.
• Network: This model shows relationships between different pieces of information without a strict hierarchy. It’s useful for showing connections and associations between concepts that don’t neatly fit into categories. Think of a knowledge graph or a social network.
• Matrix: This model arranges information in a grid, showing relationships along two or more dimensions. It’s useful when presenting comparisons or showing data with multiple variables.

For example, a small blog might be best served by a hierarchical model, whereas a large online retailer would likely benefit from a faceted model to help users find the exact products they are looking for.

Card sorting is a user research method used to understand how users categorize information. It helps uncover intuitive groupings and inform the structure of a website or application. Here’s how to conduct one:

1. Define Objectives: Clearly state what you want to learn from the card sorting exercise. What aspects of the information architecture are you trying to understand?
2. Create Cards: Write down each content item (e.g., pages, features) on a separate card. Keep the wording concise and consistent.
3. Choose a Method: Select either open card sorting (users create their own categories) or closed card sorting (users organize cards into predefined categories). Open card sorting is better for exploratory research, while closed sorting is suitable when you have some pre-existing ideas.
4. Recruit Participants: Recruit a representative sample of your target users.
5. Conduct the Sorting: Provide instructions to participants and let them sort the cards. It’s helpful to observe the participants as they work to gain insight into their thought processes.
6. Analyze Results: Use software or manual methods to analyze the results. Look for patterns and recurring themes in how users group the cards. This will reveal how users naturally categorize information and guide your IA decisions.

For example, if you are designing an online store, a card sorting exercise could help determine how users group products into meaningful categories.

Taxonomies and folksonomies are both ways of organizing information, but they differ significantly.

• Taxonomy: A formally structured, hierarchical classification system, usually created by subject matter experts. It’s controlled and consistent, ensuring uniform labeling and navigation. Think of the Dewey Decimal System in a library.
• Folksonomy: A user-generated system of tagging and classifying information. It’s organic and reflects the users’ perspectives and understanding. Think of hashtags on social media.

Benefits of a Taxonomy:

• Consistency: Ensures a standardized approach to labeling and organization.
• Control: Allows experts to curate and maintain a controlled vocabulary.
• Scalability: Can handle large volumes of information effectively.

Benefits of a Folksonomy:

• Flexibility: Adapts to user needs and evolving perspectives.
• User Involvement: Directly reflects user understanding and preferences.
• Discoverability: Can reveal unexpected relationships and connections within the information.

Choosing between the two depends on the context. A website with highly specific information likely needs a formal taxonomy, while a social media platform might benefit from a folksonomy to leverage user-generated tags.

Metadata is data about data. In IA, it’s crucial for enriching content and improving searchability and discoverability. It provides context and additional information about items in the information space.

Examples of metadata include:

• Title: The name of the document or content item.
• Author: The creator of the content.
• Date Created: When the content was created.
• Keywords: Relevant terms associated with the content.
• Description: A summary of the content.

The importance of metadata in IA lies in:

• Improved Search: Metadata provides search engines and users with more information to locate relevant content.
• Enhanced Organization: Metadata enables better categorization and filtering of content.
• Better Discoverability: Metadata helps users find content even if they don’t know the exact name.
• Content Management: Metadata facilitates efficient management and organization of large information sets.

For instance, a digital library might use metadata such as publication date, author, ISBN, and subject keywords to help users find specific books. Without metadata, searching and managing a large collection would be incredibly difficult.

User research is integral to successful IA. It ensures the information architecture aligns with user needs and expectations. My approach involves a multi-faceted strategy:

• User Interviews: Conducting one-on-one interviews to understand users’ tasks, goals, and information seeking behaviors. This helps identify pain points and potential areas for improvement.
• Surveys: Gathering quantitative data on user preferences and behaviors through online surveys.
• Card Sorting (as discussed earlier): Determining how users categorize information.
• Tree Testing: Evaluating the effectiveness of the website’s navigation structure by asking users to find specific information.
• Usability Testing: Observing users as they interact with the website or application to identify usability issues related to information finding and navigation.
• Analytics Review: Examining website analytics to identify patterns in user behavior, such as frequently accessed pages and common search terms. This data complements qualitative findings from other research methods.

By combining these methods, I get a comprehensive understanding of user needs and preferences, informing the design of an intuitive and effective information architecture.

Sitemaps and wireframes are essential tools in IA. I have extensive experience creating both, leveraging them effectively throughout the design process.

Sitemaps: I create sitemaps to visually represent the hierarchical structure of a website or application. This provides a high-level overview of the information architecture, showing the relationships between different pages and sections. I use both XML sitemaps for search engine optimization and visual sitemaps for internal design communication. My sitemaps always reflect the user’s perspective, prioritizing clarity and logical organization.

Wireframes: I create wireframes to visualize the layout and functionality of individual pages. These low-fidelity mockups focus on the information architecture and page structure, without concerning themselves with aesthetics. I use wireframes to refine navigation elements, placement of content blocks, and overall page flow. Wireframing helps test different layout options and identify potential navigation challenges before proceeding to higher-fidelity designs. I often use tools like Balsamiq or Figma for creating wireframes.

For example, in a recent project redesigning a university website, I created a detailed sitemap outlining the navigation structure across multiple departments. Then, I developed wireframes for key pages, focusing on user-centered design principles. The wireframes facilitated testing and refinement of the navigation and content layout, resulting in a significant improvement in user experience.

Defining and measuring the success of an IA project goes beyond simply launching a website or application. It’s about understanding user behavior and achieving defined business goals. Success is measured through a combination of qualitative and quantitative data.

Qualitative measures often involve user testing, usability studies, and feedback surveys. We look for indicators like task completion rates, user satisfaction scores (e.g., SUS – System Usability Scale), and qualitative feedback on navigation ease and clarity. For example, a high task completion rate combined with positive user feedback on the intuitive nature of the site navigation suggests a successful IA.

Quantitative measures include website analytics such as bounce rates, time on task, pages per visit, and conversion rates. A low bounce rate, short time on task for key actions (e.g., finding a product), and high conversion rates demonstrate an efficient and effective IA. Let’s say a redesign reduced bounce rate by 15% and increased conversion rate by 10%; that’s a clear indicator of success.

Ultimately, success hinges on aligning IA goals with business objectives. Did the IA improvements lead to increased sales, higher user engagement, or improved customer satisfaction? These are the key performance indicators (KPIs) that truly validate the project’s success.

Throughout my career, I’ve encountered several common IA challenges. One recurring issue is information silos. Different departments often manage content independently, leading to duplicated information, inconsistencies, and difficulty in creating a unified user experience. For instance, a marketing team might have one version of a product description, while the sales team has another, creating confusion for users.

Another significant hurdle is legacy systems. Migrating from outdated systems with poorly structured content can be incredibly time-consuming and complex. You often need to balance the urgency of a modern IA with the constraints of a pre-existing technical architecture. Imagine dealing with a website built on a platform that doesn’t support robust search functionalities; adapting the IA to the limitations presents a substantial challenge.

Finally, evolving business requirements are a constant source of adaptation. As the business grows or shifts strategy, the information architecture must evolve to reflect those changes. Failing to adapt results in an IA that is no longer relevant or efficient, impacting the user experience and the company’s goals.

Handling conflicting stakeholder requirements demands a structured approach. My strategy involves several key steps:

• Facilitation and Collaboration: I bring stakeholders together in workshops or meetings to openly discuss their needs and priorities. This creates a collaborative environment where everyone can express their perspectives.
• Prioritization and Compromise: Using techniques like MoSCoW (Must have, Should have, Could have, Won’t have) analysis, we prioritize requirements based on their importance and feasibility. This helps identify areas where compromise might be necessary.
• Data-Driven Decision Making: User research and analytics play a crucial role in resolving conflicts. Data can support decisions and demonstrate the impact of certain features or functionalities.
• Documentation and Transparency: All decisions and compromises are documented clearly and shared with all stakeholders to maintain transparency and buy-in.
• Iteration and Feedback: The IA is an iterative process. We test prototypes, gather feedback, and adjust based on the results. This iterative approach allows for continuous refinement and addresses any unmet stakeholder needs.

Essentially, the process is about finding a balance that satisfies the core needs of all key stakeholders while maintaining a user-centered approach. It’s not about picking a “winner,” but about creating the best possible solution for everyone involved.

Creating a robust navigation system follows a methodical process:

1. Understanding User Needs and Goals: I start by conducting user research to understand how users interact with the information and what tasks they need to complete. This might involve user interviews, surveys, and card sorting exercises.
2. Content Inventory and Analysis: A complete inventory of existing content is created, analyzed for relationships, and organized into logical groupings.
3. Card Sorting and Tree Testing: These methods help determine the optimal organization of content by asking users to group and label information. Tree testing helps validate the proposed navigation structure.
4. Information Architecture Diagram: A visual representation of the website’s structure (sitemaps, wireframes) is created to map out the navigation pathways and relationships between content.
5. Navigation Design and Implementation: Based on the architecture diagram, I design the actual navigation elements (menus, breadcrumbs, search) ensuring consistency and clarity.
6. Usability Testing: The designed navigation is tested with users to identify any usability issues or areas for improvement.

This iterative process ensures that the final navigation system is intuitive, efficient, and aligns perfectly with the user experience goals.

Accessibility is paramount in IA design. It’s about creating an inclusive experience for all users, including those with disabilities. My approach includes:

• Following WCAG Guidelines: I adhere to the Web Content Accessibility Guidelines (WCAG) to ensure compliance with accessibility standards.
• Keyboard Navigation: All interactive elements must be easily navigable using only a keyboard.
• Screen Reader Compatibility: Proper semantic HTML, ARIA attributes, and alt text for images ensure screen readers can interpret the content accurately.
• Color Contrast: Sufficient color contrast between text and background is crucial for readability, especially for users with visual impairments.
• Clear and Concise Language: Using simple and straightforward language makes the content accessible to a wider range of users.
• Alternative Input Methods: Providing support for alternative input methods such as voice control or switch access enhances inclusivity.

By integrating these principles throughout the IA design process, we create a website that’s usable and enjoyable for everyone.

Content modeling is the process of defining the structure, attributes, and relationships of content items within a system. My experience encompasses various content modeling techniques, including:

• Developing Content Models: I create structured content models using tools like spreadsheets or specialized content modeling software, defining content types, fields, and relationships (e.g., one-to-many, many-to-many).
• Defining Content Taxonomies: I develop taxonomies to categorize and organize content logically, facilitating efficient retrieval and navigation. This might involve hierarchical structures or faceted classification.
• Using Schemas (e.g., Schema.org): I incorporate structured data markup (like Schema.org) to improve search engine optimization and data integration with other systems.
• Working with Content Management Systems (CMS): I’m proficient in adapting content models to various CMS platforms, ensuring seamless integration and content management. For example, I’ve adapted complex product catalog structures for use within Magento and WordPress.

Effective content modeling is vital for scalability, maintainability, and a consistent user experience. A well-defined content model facilitates efficient content creation, management, and retrieval, ultimately leading to a more user-friendly experience.

Collaboration with content creators is crucial for maintaining consistent information architecture. My approach involves:

• Establishing Style Guides and Content Standards: Creating comprehensive style guides and content standards ensures uniformity in terminology, formatting, and tone. This provides clear guidelines for content creators.
• Training and Workshops: I conduct training sessions and workshops to educate content creators on the IA principles and content standards, providing hands-on examples and practical guidance.
• Template Design and Content Management System Customization: Developing well-structured templates and customizing the content management system (CMS) simplifies the content creation process, reducing inconsistencies and errors.
• Regular Feedback and Review: I provide regular feedback on the created content, ensuring it aligns with the established IA and content standards. This includes reviewing content for accuracy, clarity, and consistency.
• Content Audits and Refinements: Periodic content audits are conducted to identify inconsistencies and areas for improvement, enabling continual refinement of the IA and content strategy.

Open communication and collaborative workflow are key to achieving consistency and efficiency in content creation and maintenance, ensuring the long-term success of the IA.

My toolkit for IA projects is multifaceted and adapts to the project’s scale and complexity. It includes a blend of qualitative and quantitative methods. For example, I heavily rely on card sorting (both open and closed) to understand how users mentally categorize information. This helps define intuitive navigation structures. Tree testing allows me to validate the effectiveness of a proposed sitemap before it’s built. I also utilize user interviews and surveys to gather user needs and expectations directly. These methods, when combined, help to inform design choices based on user behavior, not assumptions. On the quantitative side, I use web analytics tools like Google Analytics to understand existing user behavior patterns on a website, identifying areas for improvement. Finally, diagramming tools such as Miro and Figma are crucial for visually representing information architecture, facilitating collaboration and communication among stakeholders.

For example, in a recent project redesigning a university website, card sorting revealed a significant preference for grouping information by academic department instead of by administrative function. This finding directly impacted the site’s information hierarchy and dramatically improved user experience.

User experience (UX) and information architecture (IA) are deeply intertwined; IA forms the structural backbone upon which a positive UX is built. IA focuses on the organization, labeling, navigation, and search of content within a system – essentially, the skeletal structure. UX, on the other hand, is concerned with the overall user experience encompassing all aspects of the interaction, including visual design, ease of use, and emotional response. Think of IA as the blueprint and UX as the finished building; you need a solid blueprint for a well-functioning building.

A well-crafted IA directly impacts UX. If users can’t easily find what they need, the UX will suffer regardless of the visual appeal. Conversely, a beautiful design can’t compensate for a poorly structured site. For example, intuitive navigation, resulting from a good IA, significantly reduces user frustration and improves satisfaction. Clearly labelled pages and a logical sitemap contribute to a positive and efficient user experience.

Staying current in IA requires a proactive approach. I regularly subscribe to industry publications like Boxes and Arrows and read blogs from leading IA practitioners. I actively participate in online communities and forums, such as those on LinkedIn and other professional networks, engaging in discussions and learning from others’ experiences. Attending conferences and workshops, like those offered by IA Summit and other relevant organizations, provides invaluable insights into the latest trends and best practices. I also frequently audit websites and apps, analyzing their information architectures to learn from both successful and unsuccessful examples. This combination of formal and informal learning helps me stay ahead of the curve and adapt my methods as the field evolves.

My approach to creating a user-centered IA is iterative and research-driven. I begin by deeply understanding the users and their needs through user research methods like surveys, interviews, and usability testing. This helps identify user goals and tasks, which are central to designing a site that meets their needs effectively. Following that, I conduct content audits to inventory existing content, determining what is relevant, outdated, or missing. Then, I leverage techniques like card sorting and tree testing to validate the proposed information structure and ensure that it aligns with user mental models. This process is iterative; I continually refine the IA based on feedback from user testing until a satisfactory level of usability is achieved. Throughout, clear communication with stakeholders is paramount to ensure that the IA supports organizational goals alongside user experience.

For instance, in a recent e-commerce project, user research revealed a significant preference for filtering products based on specific features. This insight led to a redesigned filtering system that directly enhanced the user’s ability to find products quickly and easily, ultimately increasing sales conversion rates.

Managing large-scale IA projects necessitates a structured and collaborative approach. I typically begin by breaking down the project into smaller, manageable modules. This allows for parallel workstreams, accelerating the overall process. I utilize project management methodologies like Agile, employing iterative development and frequent feedback loops. A robust content model is essential – a structured way to organize and manage content at scale. This model, frequently expressed in a taxonomy or ontology, guides content creation and ensures consistency across the platform. Strong collaboration with stakeholders – including developers, content editors, and business owners – is crucial for successful implementation. Utilizing a collaborative tool like a shared document or online whiteboard greatly facilitates this collaboration.

For example, when working on a large-scale government website redesign, we divided the project into thematic sections, each with its own team and delivery schedule. This allowed us to manage complexity and ensure timely completion.

Content migration and restructuring are core components of many IA projects. My approach involves a thorough analysis of the existing content, identifying duplicates, inconsistencies, and outdated information. This often involves automating the process using scripts or specialized tools where possible, but also involves a manual review of crucial content. I create a detailed migration plan outlining the steps involved, including data mapping, content cleansing, and quality control processes. The plan addresses any technical challenges, such as data transformation or database updates. Following migration, robust testing is paramount, including functional testing and user acceptance testing to ensure that the migrated content is accurate, complete, and accessible.

For example, during a recent website migration, we used a custom script to map old URLs to new ones, minimizing disruption to users and search engine rankings. This automated process significantly reduced the time and resources needed for migration.

I possess extensive experience with various content management systems (CMS), including WordPress, Drupal, Sitecore, and Adobe Experience Manager (AEM). My familiarity extends beyond basic usage; I understand their underlying architectures and how they impact IA decisions. For instance, I am adept at leveraging the features of each CMS to optimize content organization, navigation, and search functionality. I know how to create custom taxonomies and content types within different CMS platforms to meet specific project needs. Understanding the technical capabilities and limitations of different CMSs allows me to make informed recommendations during the planning phase of a project, ensuring a seamless integration between IA and the chosen technology.

A recent project involved migrating a website from WordPress to AEM. My understanding of both platforms’ strengths and limitations allowed me to design an IA that leveraged AEM’s advanced features while ensuring a smooth transition from the existing WordPress structure.

SEO, or Search Engine Optimization, is crucial for Information Architecture (IA) because it bridges the gap between how users search for information and how that information is organized within a website or application. Effective IA directly supports SEO by ensuring content is easily discoverable and understandable by both users and search engines. My experience involves leveraging SEO best practices from the outset of IA projects. This means considering keyword research to inform sitemaps and navigation structures. For example, if we’re designing an e-commerce website for hiking boots, understanding search terms like ‘best hiking boots for women,’ ‘waterproof hiking boots,’ and ‘cheap hiking boots’ informs the taxonomy and navigation, ensuring users can easily find what they’re looking for. This improves not only user experience but also search engine rankings.

I’ve worked on several projects where integrating SEO principles early in the IA process resulted in significant improvements in organic traffic. One project involved redesigning a large company’s website. By carefully mapping keywords to content and structuring the site logically, we improved organic search rankings by over 30% within six months. This demonstrates the direct impact of well-executed IA on SEO success.

Evaluating IA effectiveness involves a multifaceted approach, combining quantitative and qualitative data. Quantitative metrics might include:

• Task completion rates: How successfully do users accomplish their goals within the system?
• Task time: How long does it take users to complete tasks? Faster times usually indicate more efficient IA.
• Error rates: How often do users make mistakes or encounter dead ends?
• Bounce rate: For websites, this metric indicates the percentage of users who leave after viewing only one page—a high bounce rate may signal IA issues.
• Clickstream data: Analyzing user navigation patterns can reveal areas of confusion or unexpected behavior.

Qualitative data is equally important and involves:

• Usability testing: Observing users interacting with the system to identify pain points and areas for improvement. (This is discussed further in the next question).
• User feedback: Collecting feedback through surveys, interviews, or feedback forms can directly capture user opinions and pain points.
• Expert reviews: Having experienced IA professionals review the system to provide objective insights and identify potential issues.

By combining these quantitative and qualitative methods, I can create a comprehensive understanding of how effectively the IA supports user goals and overall system effectiveness.

Usability testing is an indispensable part of IA. It’s a user-centered research method where we observe real users interacting with a system (website, app, etc.) to identify usability problems. In IA, this means seeing how effectively users navigate, find information, and complete tasks. We don’t just ask users what they think; we watch them interact, identify their struggles, and note their successes. This provides invaluable insights that quantitative data alone can’t offer.

Think of it like this: Imagine building a house without ever checking if the doors and windows are in the right places. Usability testing is that check. It helps reveal flaws in the IA design—for example, a poorly labeled navigation menu, confusing search results, or a cumbersome checkout process. By conducting usability testing early and often, we can identify and address these issues before they negatively impact user experience and ultimately, business objectives.

I typically employ a variety of usability testing techniques, such as think-aloud protocols (where users verbalize their thought process while interacting with the system), eye-tracking studies, and A/B testing of different IA designs. The data gathered informs design iterations and improvements to the IA, creating a more user-friendly and effective system.

Analytics are indispensable for iterative IA improvements. Website analytics tools like Google Analytics provide valuable data regarding user behavior. I use this data to understand several key aspects of IA effectiveness:

• Navigation patterns: Where are users dropping off? Which pages are most visited? Are there unexpected navigation paths? This helps identify sections needing restructuring or improved labeling.
• Search behavior: What terms are users entering into the search bar? Are they finding the information they’re looking for? This feedback directly influences keyword strategy and taxonomy enhancements.
• Conversion rates: Are users successfully completing their goals (e.g., making a purchase, filling out a form)? Low conversion rates may indicate issues with navigation, discoverability, or the clarity of information within the system.
• Error rates: Analytics can track error messages, broken links, and other issues that disrupt the user experience.

For example, if analytics show a high bounce rate on a specific product page, it suggests that the product information is not engaging or easily accessible. This could mean improving the page layout, adding better images or videos, or updating the content with more relevant keywords. By constantly monitoring and analyzing this data, I ensure that the IA continuously adapts to user needs and system goals.

Creating and maintaining taxonomies is a core aspect of my IA work. A taxonomy is a hierarchical classification system that organizes information into meaningful categories and subcategories. A well-structured taxonomy is essential for users to easily find the information they need, and for search engines to understand and index content appropriately. The process involves:

• Keyword Research: Understanding the language users employ when searching for information related to the system.
• Content Audit: Analyzing existing content to identify key themes and topics.
• Hierarchical Structure: Defining the relationships between categories and subcategories, ensuring a logical and intuitive structure.
• Labeling: Choosing clear, concise, and user-friendly labels for each category.
• Regular Maintenance: Taxonomies are not static; they require ongoing maintenance to reflect changes in content and user needs. This includes adding new categories, merging outdated ones, and adjusting labels based on user feedback and analytics data.

I’ve built and maintained taxonomies for numerous projects, including an online encyclopedia and an e-commerce platform. In the encyclopedia project, ensuring consistency and accuracy across thousands of articles was crucial; for the e-commerce site, facilitating easy navigation and discovery of products was paramount. The challenge lies in striking a balance between granular detail and simplicity, to avoid overwhelming the user with excessive categories.

Ambiguity and uncertainty are inherent in many IA projects, especially during the early stages. I address these challenges by:

• Iterative Design: Embracing a design process that allows for adjustments based on user feedback and evolving understanding. We don’t aim for perfection on the first try.
• User Research: Conducting thorough user research to understand user needs and expectations, which helps reduce ambiguity surrounding requirements.
• Prototyping: Creating low-fidelity prototypes early on allows for rapid testing and validation of design decisions, minimizing risks associated with uncertainty.
• Stakeholder Collaboration: Open communication and collaboration with stakeholders helps establish shared understanding and reduce conflicting interpretations.
• Data-Driven Decisions: Using analytics and usability testing data to inform decisions, making the process less reliant on intuition and assumptions.

For instance, if a client’s requirements are initially vague, I’ll use user interviews and card sorting exercises to clarify their needs and translate them into a functional IA. The iterative approach allows for course correction along the way, reducing risks associated with uncertainty.

In one project for a large non-profit organization, we faced a difficult decision regarding the organization of their website’s content. They had a vast amount of information covering many programs and services, and the existing site was confusing and difficult to navigate. We proposed two distinct IA solutions:

• Solution A: A topic-based structure, grouping information according to themes and overarching topics. This was more user-friendly, but potentially less effective for promoting specific programs.
• Solution B: A program-based structure, organizing information by individual programs. This prioritized promoting specific initiatives but risked making it harder for users to find information across programs.

The decision was challenging because it involved balancing user experience with the organization’s marketing goals. Ultimately, we opted for a hybrid approach, incorporating elements of both solutions. We used a topic-based structure at the highest level of the site, but within each topic, we provided clear links to relevant programs. This compromise allowed us to strike a balance between intuitive user navigation and targeted program promotion. This solution involved thorough stakeholder discussions and a series of iterative prototypes to ensure we were meeting both user needs and organizational goals. Post-launch analytics confirmed the success of the hybrid approach, achieving better user engagement and improved program promotion compared to the previous website structure.