The most valuable feature is the one a user can actually find. This is a story about stepping back from a disappointing launch, listening to our users, and proving that sometimes the highest-impact work isn't building something new, but building a better path to what you already have.
1. Context
- Product: A high-growth, on-demand logistics platform connecting users with truck drivers for moving goods.
- My Role: Product Manager for the core booking experience.
- Team: I led a cross-functional team of 1 Senior Designer, 4 Engineers, 1 QA Analyst, and collaborated closely with Data Science, Marketing, and Customer Support.
- Business Goal: Increase Average Order Value (AOV) and improve customer retention by solving key pain points in the moving process.
2. The Problem: A Solution Lost in the Flow
Our user research consistently flagged a major pain point: over half of our customers needed help loading items into their trucks. To address this, we launched the "Loading Assistant" feature—an in-app add-on to officially request and pay for driver assistance.
Two months after launch, the data painted a challenging picture:
- Customer Awareness: Only 35% of target users knew the feature existed.
- Adoption Rate: A mere 7% of eligible orders included the Loading Assistant.
We had built a solution for a proven need, but it was failing to connect with users. The investment was delivering minimal returns, and a core user problem remained unsolved. We needed to diagnose the root cause by answering two fundamental questions: Why weren't users finding our feature? And why weren't the few who found it, converting?
3. Discovery And Research: Following the User's Footsteps
I believe that data points you in the right direction, but direct user feedback draws the map. We launched a multi-pronged investigation to understand the story behind the numbers.
1. Data And Funnel Analysis
In partnership with our data analyst, we first looked for leaks in the funnel. The numbers suggested a potential performance issue: the success-to-click ratio on the feature was low, and the page where the option was located had a higher-than-average server response time. This was our first clue that technical friction might be a factor.
2. Qualitative User Research
This is where the real diagnosis happened. In collaboration with our UX researcher, we ran a mixed-method study:
- Surveys: These confirmed the massive awareness gap. 40% of participants had no idea the feature existed. Another 30% had vaguely heard of it but couldn't recall where to find it.
- Usability Testing (5 users): This gave us our "aha!" moment. We watched as user after user scrolled right past the feature. They overwhelmingly expected to make a decision about loading help at the very beginning of the booking flow, when choosing their vehicle. We had buried it three steps later, framing it as an "accessory" decision. For our users, it was a primary need that shaped their entire plan.
3. Cross-Functional Sync
To complete the picture, we connected with our operational teams:
- Customer Support: Their logs were a goldmine. We found numerous tickets from users asking if loading help was possible, confirming they were completely missing the in-app solution.
- Marketing: A check-in revealed that a planned push notification campaign to announce the feature had been delayed. Our go-to-market (GTM) plan hadn't fully executed, compounding the discoverability problem.
The evidence was clear: this wasn't a feature value problem; it was a fundamental discoverability and user experience problem.
4. Strategy And Goals: A Plan for Revitalization
Our diagnosis pointed to a focused, two-part strategy:
- Fix the Foundation: Prioritize changes that would make the feature impossible to miss and seamless to use.
- Relaunch and Educate: Execute a coordinated GTM plan to drive aware users into the improved experience.
We set a realistic goal that reflected a revitalization effort, not a blue-sky launch: Double the feature adoption rate from 7% to at least 15% within the next quarter.
5. Execution: Prioritizing the Path of Least Resistance
Our team worked in an iterative cycle, prioritizing the foundational fixes that addressed the most critical user friction first.
1. Redesigned the User Flow (A/B Test)
The most crucial change. We created a new flow that surfaced the "Loading Assistant" option directly on the first screen, alongside truck selection. We rolled this out as an A/B test to validate our hypothesis that this new placement was superior.
Before redesign: The loading assistant feature was placed in the last step of order flow, on the review and payment page among a lot of information to review, causing cognitive overload for the user.
After redesign: The feature has been moved forward in order flow, making it much easier to notice and fast to use in the first step.
2. Performance Optimization
Our engineers addressed the technical friction we'd spotted. They optimized the backend service for the load-type page, reducing latency by 80% and creating a smoother, more reliable interaction.
3. Coordinated GTM Relaunch
I worked with Marketing to align the release of the delayed push notification campaign with the rollout of the in-app improvements, ensuring we were driving users to the best possible version of the experience.
6. Results And Impact: Validating the Hypothesis
Our focus was on validating our core hypothesis: that poor discoverability was the primary blocker. The results confirmed this decisively and gave us a clear path forward.
- A/B Test Delivered a Clear Signal: After three weeks, the A/B test showed a conclusive winner. The new user flow had a 150% higher completion rate (orders with the feature successfully added) than the original flow.
- Steady Growth in Adoption: Over the following quarter, the overall adoption rate more than doubled, climbing from 7% to a new, stable baseline of 16%.
- Reduced User Friction: Customer Support reported a 60% decrease in tickets asking "how to get loading help."
- Positive Impact on Business Metrics: This steady increase directly contributed to a sustained 3% lift in Average Order Value (AOV) for the truck services category.
7. Reflections And Next Steps
This project was a powerful lesson in the value of iteration and humility. The path to growth isn't always about building the next big thing.
Key Learnings
- The Experience is the Product: Our initial mistake was architecting the feature based on our own logic ("it's an add-on") rather than our users' mental model ("it's a core need"). Aligning the product with the user's journey was the key that unlocked its value.
- GTM is a Required Checkpoint: This experience was a catalyst for a tangible process improvement. We've since integrated a "GTM Readiness" review into our development framework.
Next Steps - From One Problem to the Next
Our work isn't done. While we solved the discoverability problem, our research clearly showed that the current feature still doesn't serve a key user segment: those who need help with very heavy items. Our next step is to scope a dedicated discovery sprint to explore a "Two-Person Team" or "Heavy-Duty" offering, allowing us to build on our momentum and solve for the next layer of user needs.