Feedback Is Just the Start: How Acting on It Increased Zipcar’s NPS by Over 50% | Nishaat Vasi, CPO
Here's how Zipcar's CPO rebuilt a culture of member obsession using in-context feedback, P&L-connected metrics, and disciplined experimentation to drive a 50%-point NPS gain.
Nishaat Vasi has spent eight-plus years at Zipcar – one of the original pioneers of the sharing economy – most recently as CPO. When he stepped back into the role after a stint incubating a startup spun out of Zipcar, he found a company that had done something easy to do and hard to notice: it had drifted away from its members.
Years of platform migration work had quietly pushed customer experience to the back seat. NPS was being captured the old-fashioned way through an email form after a ride, and barely 3% of members were responding. Nishaat’s mission was to change that, not just by capturing better feedback but by building it into the team’s operational processes.
The result?
Zipcar’s NPS jumped from 36 to 56, a +50% jump, driven not by a single feature, but by a sustained, cross-functional commitment to member obsession.
In this episode, Nishaat shares how he rebuilt that user-obsessed culture, what it looks like to turn unstructured feedback into operational change, and why most “AI strategy” is just theater.
1. Moving from post-ride surveys to in-context feedback (8:12)
When Nishaat returned to Zipcar, the feedback loop was broken in a predictable way: members were asked for their opinions hours after a trip ended, via email, and most ignored it entirely.
The first move was switching to in-context feedback – capturing it immediately after a trip, inside the app, while the experience was still fresh. The goal wasn’t just higher response rates. It was better signal.
“It was really about how do we get in-context feedback? Then what do we do with that in-context feedback? Outside of just getting feedback and leveraging it, it was also about how do we develop that DNA back into our product and UX teams.”
The shift paid off in ways that went beyond the data. When PMs, engineers, and operations teams could watch real session recordings and hear real user frustrations, they didn’t need to be convinced to care. The problem became visible.
Product takeaway: In-context feedback isn’t just a response rate trick. It changes what you learn and who in your organization feels accountable for fixing it.
2. Connecting NPS to P&L (16:31)
Plenty of product teams track NPS. Far fewer connect it to the business outcomes that give leadership a reason to prioritize it. Nishaat’s team did both.
After identifying their top three problem areas – car cleanliness, damage, and maintenance issues – they built out a financial model showing the cost of each. Damages don’t just hurt member experience: they accelerate asset depreciation and reduce resale value. Poor car condition drives rash behavior, which drives accidents.
“For every 100 such events, here is our hypothesis — all backed by data. We modeled this out. We look at different conditions. Here’s how many millions we’re going to lose. And this is why it’s not only damages, it actually leads to how people treat your cars, which could be accidents.”
Product takeaway: NPS is hard to act on when it lives in a product dashboard. The moment you can show leadership that moving it 10 points saves millions of dollars, it stops being a vanity metric and starts being a business case.
3. The pilot framework: Small bets, real data, then scale (21:07)
Not every problem has an obvious software solution. When Nishaat’s team decided to tackle damage detection, they took a two-stage approach: try a cheap pilot first, then build the business case for real investment.
One idea was to require photos of the car’s exterior at check-in and check-out. It was a technology solution that didn’t require a massive build, but the behavioral effect was immediate.
“Just the fact of making someone do it…changes the game, believe it or not. Whether I do anything with that data or not makes no difference. The big value is in the way people perceive the product at that point.”
When that still wasn’t enough, the team piloted off-the-shelf in-car devices with gyroscopes and impact detection – zero CapEx, no software investment, tested in two markets over three months. They expected to improve damage detection. They got a bonus: the same device also caught smoking, another major problem for Zipcar, saving the company millions of dollars.
Product takeaway: Design your experimentation model with two tiers – small pilots (sub-$100K, team-level authority, three-month timelines) and major investments (full business case required). The goal isn’t to democratize all decisions; it’s to make the cost of learning cheap enough that you don’t skip it.
4. From unstructured feedback to operational alerts using AI (32:53)
Nishaat has a clear-eyed view of AI.
His best example: a fleet manager in one market quietly removed the fuel cards kept in the visor of every Zipcar. No one filed a ticket. No one called to complain with a subject line that said “fuel card missing.” But users noticed.
Zipcar now pumps all of that unstructured signal into a single place and uses AI to identify week-over-week changes in emerging themes. When the fuel card issue spiked, the system caught it and fired a Slack alert to the right people in the right market in under 24 hours.
“It would have taken me or us at least three to four days to figure out what this problem was. This got solved, end to end, in under 24 hours. I’m like, okay, that was worth the cost of the tool itself.”
The key takeaway: The AI isn’t responding to members directly. There’s a human in the loop. But the time to detection collapsed from days to hours.
5. Why most AI strategy is “AI theater” (29:36)
Nishaat isn’t anti-AI. Far from it. But he’s skeptical of the way most organizations are approaching it – chasing the headline, not the outcome.
His framing splits AI investment into two distinct problems: getting AI into the product experience AND using AI to make internal teams more efficient. Both require the same foundation: clean, well-governed, well-documented data.
“At its core, you gotta have the data first…And so really it’s about ensuring we have the right data set up. People underestimate how much effort that takes to really create that walled garden – but that is crucial to get right.”
Product takeaway: Before you greenlight another AI initiative, ask what operating goal it’s connected to. If the answer is vague like “improve efficiency,” “enhance the member experience,” it’s probably theater. If it maps to a specific metric someone owns and is measured on, it has a chance of being real.
Chapters
00:00 Intro
01:07: Meet Nishaat Vasi, CPO at Zipcar
04:50: Zipcar and the customer experience wake-up call
05:50: Ditching the email survey for real-time feedback
08:39: Zipcar's user session watch parties
11:46: Turning NPS into OKRs
15:19: The top 3 priorities: Cleanliness, damage, and maintenance
19:13: The zero-cost pilot that now saves millions of dollars
31:03: How genAI spotted a hidden fuel card problem
35:57: Conclusion
Links
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