# Wishlist or Favorites Page Design [Image] This experience sits in the middle of shopping, not at the start or the end. Users are trying to remember what caught their eye and come back when they’re ready to decide or share. For the business, it supports longer consideration cycles and gives shoppers a reason to return instead of starting over elsewhere. The test focused on Zazzle’s wishlist or favorites page and how people access it through account navigation. Participants were asked to imagine saving items while browsing, then show where they would go to find or share those saved products. Usability, Effort, and Usefulness were used to observe how quickly users orient themselves, how hard the interaction feels, and whether the feature supports real shopping needs. This kind of testing surfaces where momentum quietly breaks down. It helps teams see whether saved-item features actually support return behavior or simply exist in the background. For product and business leaders, these signals matter because even small moments of hesitation can reduce follow-through during high-value decision moments, without ever showing up as outright failure in conversion data. * * * ### **Define Goals for Your Wishlist or Favorites Page** A wishlists or favorites page should balance user needs like organization, control, and future value with business goals such as retention, return visits, and delayed conversion. Users aren’t always ready to buy, but they want a reliable place to save ideas and come back later. Businesses want to respect that pause while keeping momentum alive over time. **Audience:**
 This concept was tested with online shoppers in the United States who reviewed Zazzle’s Likes page. Participants were asked to look at items they had saved, imagine returning to them later, and share how useful, motivating, or confusing the experience felt as a place to manage future purchase intent. **User Needs** At this stage, users are planning, comparing, or waiting—not committing. The experience should support that mindset without pressure. - Saved items should feel worth coming back to, not like forgotten leftovers (**valuable**). - The page should be simple to scan, manage, and update without effort (**usable**). - Items users have liked should be easy to locate and revisit later (**findable**). - Users should feel in control of what they save, remove, or organize (**empowering**). - The experience should make users want to return and keep browsing (**engaging**). Together, these needs ensure the wishlist feels like a helpful planning space, not a dead end between visits. **Business Goals **From the business side, wishlists are about patience, not pressure. - **Increase Return Visits** – Give shoppers a reason to come back when they’re ready to decide. - **Support Delayed Conversions** – Capture intent even when users aren’t ready to purchase immediately. - **Strengthen Retention** – Keep the brand top of mind through saved items and future reminders. - **Improve Merchandising Insights** – Learn what users save, not just what they buy. - **Enable Re-Engagement Opportunities** – Support follow-ups like price drops, availability alerts, or recommendations. When these goals are aligned, wishlists become a quiet growth lever—supporting future conversions while respecting how people actually shop. * * * ### **Choose Metrics to Test Your Wishlist or Favorites** This experience tests how shoppers return to products they’ve already shown interest in. A focused design stack of UX metrics was selected to map key user needs—finding saved items, using them confidently, and feeling the feature is worth relying on—to observable signals in behavior and perception. The metrics used were Usability, Effort, and Usefulness. **Findable → Usability**
 When users come back to a site, they want to know where their saved items live without stopping to reason it out. Usability captures whether people can correctly locate the wishlist through navigation and account areas. The signal here comes from click behavior—where users expect favorites to be and whether that expectation matches reality. **Usable → Effort**
 Once users are oriented, the interaction itself should feel light. Effort reflects how hard or easy it feels to get to saved items after deciding to look for them. This metric captures perceived friction, helping distinguish between brief hesitation and sustained difficulty. **Useful → Usefulness**
 A wishlist only matters if it supports real shopping behavior. Usefulness reflects whether users feel the feature meets their needs once they engage with it—reviewing items, holding ideas, or sharing with others. This signal comes from attitudinal agreement, revealing whether the wishlist earns a place in a shopper’s decision process. Together, these metrics explain not just whether the wishlist exists, but whether it supports memory, confidence, and follow-through during longer shopping cycles. * * * ### **Establish Hunches to Direct Your Testing** Teams often feel that wishlists are “nice to have,” but it’s not always clear where they help or quietly get in the way. Starting with hunches helps narrow that uncertainty before testing, turning assumptions about saved-item behavior into questions that can be observed and measured. **Example: Zazzle Wishlist / Favorites Page**

Hunches

Questions

UX Metric


Saved items may feel buried inside account navigation, causing users to hesitate before acting. If people have to scan multiple personal areas, confidence drops at the moment they want to return to products.

Where would you click to find all of your favorited items?

Usability

Labels like “Likes” may require interpretation, slowing users down even if the feature works once found. This could create brief friction that doesn’t feel “hard,” but still interrupts momentum.

On a scale of 1–7, how difficult or easy was it to find your favorited items?

Effort

Even with some navigation friction, users may still see clear value in having a place to save items for later. The risk is not usefulness, but discoverability.

Do the website’s features meet my needs?

Usefulness

Sharing saved items may feel secondary or hidden compared to viewing them, reducing how often people act on that intent.

Where would you click if you want to send your list of favorited items to someone else?

Usability

These hunches aim to evaluate whether the wishlist supports confidence and follow-through, or whether small moments of uncertainty slow users just enough to reduce return behavior. * * * ### **Turn Hunches into Test Questions** Turning hunches into concrete questions makes uncertainty measurable. Pairing each UX metric with a specific question type ensures we’re capturing real behavior and perception, not opinions in the abstract. - **Usability (First-click test)**
 Question type: First-click task
 Example: Click where you would go to find all of your favorited items. This question captures where users expect saved items to live before they reason it out. The first click shows whether navigation placement matches user mental models. [Image] - **Effort (Likert scale)**
 Question type: 1–7 ease/difficulty scale
 Example: On a scale of 1–7, how difficult or easy was it to find your favorited items? This question captures perceived friction after the task. It helps distinguish between momentary hesitation and experiences that feel genuinely hard. [Image] - **Usefulness (Agreement scale)**
Question type: Agreement statement
 Example: “The website is easy to use.” This reflects whether the wishlist experience fits smoothly into the broader site experience. It captures overall confidence rather than task success alone. [Image] * * * ### **Calculate UX Metric Scores from User Feedback** This concept examined how shoppers return to and use saved items after browsing. Participants were trying to locate their favorites, assess how easy that felt, and reflect on whether the feature supported their needs. The design stack included Usability, Effort, and Usefulness, combining behavioral click data with attitudinal signals to capture both action and perception. - **Very Good** \= 90% and above - **Good** \= 70%–89% - **Average** = 50%–69% - **Poor** = 30%–49% - **Very Poor** = below 30% 74% — Good. At a high level, this score reflects a wishlist experience that delivers value once users engage with it, but shows signs of friction at the moment of re-entry. The experience supports return behavior, though not without brief hesitation. **Usability (58% — Average):**
 Click behavior showed that many participants paused or explored multiple areas before landing on favorites. Users often debated between account navigation and labeled shortcuts, signaling uncertainty about where saved items live. This suggests orientation friction rather than interaction difficulty. **Effort (83% — Good):**
 Once users understood where to go, the experience felt easy to move through. Ratings indicate that the task didn’t feel demanding or exhausting, even when users hesitated initially. The effort signal reflects light friction rather than sustained difficulty. **Usefulness (81% — Good):**
 Participants largely agreed that the feature met their needs and fit into their shopping behavior. Once accessed, the wishlist was seen as a practical way to hold and revisit ideas. This score shows that the value of the feature is clear, even if access isn’t immediate. [Image] Taken together, the scores point to an experience that users value and don’t find hard to use, but don’t always immediately understand how to access. The main tension isn’t whether the wishlist works—it’s whether users can re-enter it without stopping to think. This positions the wishlist as a supportive but slightly fragile part of the shopping journey, where clarity at the moment of return matters most. Click here to check out the [raw survey data and UX metric scores for Zazzle’s favorites page](https://my.helio.app/report/01KC82A75QV4E7W23XT6GPF48B). * * * ### **Draw Signals from Your Design Stack** **1\. Focus on poorly scoring metrics** [Image] Zazzle’s Likes page earned an overall score of 74% (Good), with Effort (83%) and Usefulness (81%) performing strongly, while Usability lagged behind at 58%. This gap indicates friction before users ever benefit from the feature—participants struggled to locate the Likes page, even though once there, interactions felt straightforward and valuable. The key signal: the wishlist experience supports planning and comparison, but poor discoverability limits its impact. **2\. Use design intuition to identify patterns across metrics** [Image] The metric pattern suggests a clear split between entry and execution. Users who reached the Likes page found it easy to scan, manage, and revisit items, reinforcing its usefulness as a planning tool. However, the difficulty in finding the feature breaks the natural shopping flow, making the wishlist feel secondary rather than integral. This creates unnecessary pressure on shoppers who want to save items without committing—undermining the mindset the feature is meant to support. **3\. Determine if user needs are being met** [Image] - Usable: Partially met — the page itself is easy to use, but accessing it requires unnecessary effort. - Findable: Not met — users struggled to locate liked items or return to them easily. - Empowering: Met — users felt in control of what they saved, removed, or organized once on the page. - Engaging: Met — the wishlist encouraged return visits and continued browsing. - Valuable: Met — users viewed the Likes page as a helpful planning and comparison space rather than a dead end. **4\. Compare outcomes to your business goals** - Increase Return Visits: Partially supported — saved items create intent, but poor visibility reduces re-engagement. - Support Delayed Conversions: Supported — the feature aligns well with non-committal shopping behavior. - Strengthen Retention: At risk — hidden access weakens the habit-forming potential of wishlists. - Improve Merchandising Insights: Limited — reduced usage lowers the volume and reliability of saved-item data. - Enable Re-Engagement Opportunities: Partially supported — value exists, but access friction constrains scale. **5\. Surface signals & establish a direction** Signals derived from the data: - The wishlist experience is effective once users reach it, confirming strong internal UX. - Discovery—not interaction—is the primary barrier to value. - Poor findability disrupts the low-pressure planning mindset users expect from wishlists. Direction based on business context: **To better support planning behavior and increase long-term conversion value, design priorities should focus on:** [Image] - Making the Likes page more visible through persistent navigation, account menus, or saved-item indicators. - Reinforcing access points after “like” actions so users know where items live. - Treating wishlists as a core shopping tool, not a secondary feature hidden behind navigation depth. The signal is clear: ***Zazzle’s wishlist experience supports planning and re-engagement—but until users can easily find and return to it, its ability to drive long-term value remains underutilized.***