This story is centered around the “Saved Items” feature – a simple way for users to bookmark products they’re interested in, on an e-commerce website.
Week 1: Understand the Business Goal
The intent here is not to build a “Save Product Item” button. We’re here to:
- Increase return visits from interested users
- Improve conversion rates on repeat visits
- Reduce user frustration and task repetition
- Enable future retention levers like reminders and notifications
Success is measured by outcomes and impact—not just by the features delivered.
👉 Why does the business care?
Because this isn’t just a UX improvement—it’s a retention and conversion opportunity.
If we can help users track items they like, they’re more likely to come back and complete a purchase. It also opens the door to future features like personalized nudges, price drop alerts, and wishlist-driven marketing.
👉 Understand the market – US or worldwide
For this feature, we are specifically targeting the US market, which brings clear behavioral, regulatory, and technology expectations:
- User Behavior: US consumers are highly familiar with “Saved Items” and “Wishlist” features across leading e-commerce platforms like Amazon, Walmart, and Target. They expect these features to be easily accessible, intuitive, and seamlessly integrated across devices.
- Legal & Compliance Considerations:
- CCPA (California Consumer Privacy Act): Requires clear consent for collecting and using personal data, especially when reminders or marketing emails are sent based on saved behavior.
- CAN-SPAM Act: Governs how reminder emails are structured—users must have the ability to opt out easily.
- CCPA (California Consumer Privacy Act): Requires clear consent for collecting and using personal data, especially when reminders or marketing emails are sent based on saved behavior.
- Promotional Sensitivity: US consumers respond well to personalized discounts and urgency-based nudges (e.g., “Limited stock” or “Price dropped on your saved item”), but dislike generic or frequent marketing emails.
Designing with these US-specific expectations in mind ensures the feature aligns with market standards and directly supports business goals tied to retention and conversion.
📊 Competitor Analysis – How Do Industry Leaders Handle Saved Items?
| Competitor | Feature Name | Key Capabilities | Gaps/Opportunities |
| Amazon | Wishlist / Save for Later | Syncs across devices, personalized recommendations, price drop alerts | Complex UI for managing wishlists; reminders often hidden under promotional emails |
| Walmart | Saved for Later | Simple save button, clear “Saved Items” section | No reminder emails or price alerts by default |
| Target | Favorites | Intuitive save via heart icon, easy list access | No advanced nudges or personalized reminders |
| Best Buy | Saved Items | Allows comparison of saved products | No real-time notifications for stock changes or price drops |
Key Takeaways:
- Amazon sets the gold standard but overwhelms users with promotional content rather than contextual, value-driven reminders.
- Walmart and Target offer basic save functionality but miss out on timely reminders and nudges, leaving room for differentiation.
- Opportunity for Us:
- Focus on smart, value-driven reminders (only when price drops or stock is low).
- Keep the interface clean and frictionless, especially on mobile.
- Introduce contextual call-to-actions within the Saved Items page (e.g., “Price dropped yesterday!”).
- Focus on smart, value-driven reminders (only when price drops or stock is low).
👉 Who are the people impacted? (Personas)
To bring clarity to whom we’re building for, we define key personas:
- Priya the Planner – a budget-conscious shopper who browses multiple options and wants to compare before buying
- Ravi the Repeater – a returning user who keeps coming back to look at the same item but delays the purchase
- Anika the Impulse Buyer – occasionally saves items but forgets about them; often converts with a discount nudge
For simplicity purposes, we’ll consider one persona for this example
Persona 3: Anika the Impulse Buyer
A mobile-first user who makes quick decisions but forgets things if not prompted.
User Journey:
- Browses casually while commuting.
- Sees a product she likes but doesn’t want to buy immediately.
- Saves it mentally, plans to return later.
- Gets distracted and forgets about it entirely.
- Doesn’t buy unless reminded.
Pain Points:
- Can’t rely on memory
- Doesn’t want to re-search everything
- Misses out on items she liked
Opportunity for “Saved Items”:
- Easy tap-to-save on mobile
- Smart push/email reminders: “You saved this last week”
- Time-sensitive nudges if product is low in stock or on sale
👉 I collected data…..
A customer support lead had shared recurring feedback from users:
“I keep searching for the same product again and again,”
“I wish I could save it and come back later.”
I interviewed customers to validate those pain points
At the same time, our analytics team flagged two patterns:
- High bounce rate on product pages
- Low conversion rates among returning users
Let’s zoom in: Empathy Map — Anika the Impulse Buyer
To design a feature that truly resonates, we need to go beyond what users do and understand what they think, feel, and say.
That’s where empathy mapping helps—it bridges the gap between behavior and motivation, helping us make more emotionally intelligent product decisions.
| Says | “I liked something earlier, but I can’t find it now.”
“It would be great if the site reminded me when something I saved goes on sale.” |
| Thinks | “I don’t want to miss out on that product.”
“Maybe I’ll come back later… if I remember.” |
| Does | Browses casually on her phone
Takes screenshots or leaves tabs open Sometimes forgets entirely |
| Feels | Frustrated when she can’t find what she liked
Guilty about forgetting to follow up Delighted when reminded at the right time |
This map highlights a deeper truth: Anika doesn’t want just functionality—she wants peace of mind. She wants the system to remember for her.
In user conversations, she told us:
“Reminders are helpful… if there’s a deal. Otherwise, I’ll just ignore them.”
This kind of nuance helps us make smarter, more empathetic design decisions—like offering reminders with context, not spam.
🔍 Defining the Real Problem
We now translate our findings into user journeys and pain points. For Anika, the journey looks like this:
- Browses on mobile while commuting
- Sees a product she likes
- Plans to revisit later
- Gets distracted
- Forgets about it entirely
Pain Points:
- No simple way to save products
- Forgetfulness = lost intent
- No smart reminders or memory cues
Assumptions:
- A visible “Save” option will be used
- Persistent saves across devices are expected
- Smart, timely reminders could re-engage users
Week 2–3: Discovery with the Team
Now we enter solution discovery. I pull in design, engineering, and a data analyst. This is critical. Discovery is a team sport.
We kick off with a design studio workshop, sketching multiple ideas and validating our assumptions behind each. We remind ourselves that a roadmap isn’t a tunnel—we’re not picking a solution yet. We’re mapping possible paths to the same outcome.
🧭 To-Be Journey: Designing for Impact
We map out the To-Be Journey for each key persona, identifying pain points and moments of friction. The highest impact—the “biggest bang for the buck”—comes from enabling users to:
- Quickly save products with a heart icon
- Access them later from any device
- Receive meaningful reminders when action is relevant (e.g., price drops)
This becomes the north star for our MVP.
🧪 To-Be Solution: Prototypes + Options
We build three low-fidelity prototypes:
- A heart icon on product cards
- A dedicated “Saved Items” section in the top nav
- A price-drop reminder via email or push
We then return to our users and test multiple versions, allowing them to explore each design. We observe what they prefer, what they ignore, and where they hesitate.
Incorporating Feedback + Journey Shift
Here’s what changes based on feedback:
- Users understood the heart icon instantly—but wanted clear feedback that it was saved.
- They couldn’t easily find their saved items—prompting us to make the “Saved Items” tab more prominent.
- Reminders were appreciated only if they offered value, like a deal or restock notification.
These insights led to tweaks in the prototype—from visual cues to copy improvements to navigation flow—and ultimately reshaped the user journey for clarity, confidence, and continuity.
Week 4: Tech Team & Stakeholder Alignment
Now that we have validated prototypes, we engage with the tech team.
They ask excellent questions:
- Can saved items sync across devices without login?
- What about GDPR, marketing consent, and legal risks with sending reminders?
- Will marketing want to use this list for promotions?
These questions lead us to break down the solution into modular features:
- Save items (core UX)
- Syncing across sessions (requires login)
- Price-drop reminders (optional, gated behind user consent)
We strive to build independent features, allowing each to evolve on its own timeline without causing dependencies.
Beyond Shoppers: Customer Service Impact
Next, we engage customer support leaders. They raise important questions:
“If users say they saved something and can’t find it, how do we help?”
We realize support agents need visibility into a user’s saved items. This leads to:
- Adding a “Saved Items” tab in the support dashboard (for logged-in users)
- Updating knowledge base articles to answer common questions
- Creating tooltips and messages to reduce inbound queries
This step ensures we consider internal users, not just customers.
Opportunity Solution Tree
The Opportunity Solution Tree helps teams visually connect their product goals to customer opportunities, solutions, and experiments. It brings clarity to decision-making by ensuring we focus not just on delivering features, but on solving real user problems aligned with business outcomes.
We distill all our insights into an Opportunity Solution Tree:
- Desired Outcome: Increase return visits and purchases from previously viewed items
- ➝ Opportunities:
- Users forget products they liked
- No easy access to saved items
- Users want price-sensitive nudges
- Users forget products they liked
- ➝ Solutions:
- Heart icon to save
- Saved Items page
- Reminder engine
- Heart icon to save
- ➝ Experiments:
- Measure usage and revisit rate
- A/B test placement of Saved Items
- Test reminder effectiveness
- Measure usage and revisit rate
This tree becomes our single source of truth—clarifying what we’re doing, why it matters, and what to test next.
Week 5–6: Feasibility + Alpha Test
Engineering runs a technical spike.
They confirm saving locally is easy. But syncing across devices and sessions introduces complexity. To reduce risk, we limit v1 to logged-in users only.
We launch an internal alpha to employees. The feedback is useful:
“Where do I find my saved items?”
“I clicked save, but got no confirmation.”
These early learnings validate that we’re catching gaps before they reach real users. That’s the value of agile alpha testing.
Week 7–8: Iterative Changes + Beta Launch
We address alpha feedback with small but critical changes:
- Added visual feedback when items are saved
- Made the “Saved Items” section easier to spot
- Included a tooltip explaining how to revisit saved items
We then launch a beta release to 10% of our live user base.
We monitor:
- How many users save items
- How often they return
- How many convert to purchase
- Which parts of the journey are unclear or broken
We quickly notice a pattern:
✅ Items are being saved
❌ But many users don’t return to review them
To test this, we run a lightweight experiment:
We email users 3 days after saving, reminding them:
“Still thinking about this item? You saved it last week.”
Immediately, we see a spike in return visits and purchases.
That’s the power of lean experiments—simple actions with measurable impact.
Week 9–10: Scaling & Continuous Learning
With proven value, we prepare to roll out more broadly.
But we don’t stop discovery. We bake in:
- Instrumentation to track new user behaviors
- Monthly usability testing to gather fresh insights
- Continuous data monitoring to detect regressions or opportunities
We shift from a feature roadmap to an opportunity-focused roadmap. Instead of building a wishlist, we ask:
“What else might help users resume their shopping journey?”
From there, we plan:
- Save to folders
- Smart re-engagement nudges
- Sharing saved items with others
Each is treated as a separate opportunity, not an extension of the same feature.
Final Thoughts: What the PM Actually Did
Let’s recap what I, the Product Manager, actually did throughout this story:
✅ Identified and validated the user problem
✅ Co-created To-Be Journeys and Prototypes with cross-functional partners
✅ Offered multiple options to users and incorporated real feedback
✅ Adjusted the user journey based on behavioral insight
✅ Collaborated with the tech team to define scope, legal constraints, and modular features
✅ Considered internal personas like customer support
✅ Created and maintained an Opportunity Solution Tree to guide our roadmap
✅ Delivered an MVP that balanced value, speed, and learning
✅ Established a system for continuous iteration, not one-time delivery
✅ Kept the team outcome-driven instead of output-obsessed
That’s what modern Product Management is all about.
It’s not about shipping buttons.
It’s about solving problems—for real users, in the real world.
And most importantly: a roadmap is not a tunnel. It’s a compass that adjusts as we learn.
Interested!