How Fair.com Slashed CAC by 90% Using Automated, Personalized Ads | Shaan Rupani (Growth Leader at Dropbox, Fair.com, and More)
In this interview, Shaan Rupani, currently leading Growth at Knowde, formerly Head of Growth at NexHealth, and previously at Fair.com and Dropbox, shares how Fair.com transformed its customer acquisition model by building a fully automated, personalized ad system that slashed CAC by 90%. We discuss the strategy, execution, and lessons learned, as well as his broader thoughts on the emerging role of Growth Operations.
Growth marketing isn’t just about spending money efficiently—it’s about architecting systems that drive sustained, compounding success. Few people understand this better than Shaan.
Joining the conversation is Aditya Vempaty, currently VP of Marketing at MoEngage and a Mentor at First Round Capital, who brings his own perspective on the evolution of growth teams and operational efficiency.
Fair.com’s Growth Strategy: Automating Ads for Hyper-Personalization
Prasid: Let’s talk about Fair.com. What’s one specific marketing or growth initiative that really stood out?
Shaun: When I joined Fair.com, we were relying entirely on referrals, so our CAC was essentially $0. But once we needed to scale, we had to introduce paid acquisition. The conventional approach would have been to run generic ads across Google and Facebook, but we knew that wouldn’t be cost-effective. Instead, we developed an automated system that dynamically created thousands of personalized car ads in real-time, matching users with relevant inventory. This dropped our CAC by 90%.
Aditya: What was the conventional approach in the industry at the time, and how did your strategy differ?
Shaun: Think about an ad you see for something like Autotrader.com. The default playbook is generic ads about the platform and it’s benefits. For mobile apps the typical acquisition playbook was to focus on the benefits of the app, and run Google Universal App Campaigns and Facebook ads with generic creatives.
We flipped that by integrating our ad-buying systems with our live inventory data. Instead of serving generic “Get a car with Fair.com” ads, we dynamically built thousands of real-time ads showing actual cars available in the user’s area, with accurate pricing and direct deep links into the app. This level of specificity, relevance, and personalization had a massive impact on CTRs and CAC.
Prasid: Awesome. Let’s get into the weeds.
Initial Situation: The Challenges Fair.com Faced
Prasid: Paint a picture for us of the initial situation.
Shaun: When I joined, Fair.com’s marketing team was tiny—just a few brand marketers. We had no structured performance acquisition, no lifecycle marketing, and no scalable customer acquisition strategy beyond referrals. The problem was, once we started buying ads, we had no way to make them effective at scale.
We needed a way to bring in new customers without inflating CAC, and we couldn’t afford to spend months on engineering-heavy custom solutions.
Aditya: Why was this crucial at that moment?
Shaun: Fair.com had just launched, and we needed predictable growth to prove out the business model. Without scalable paid acquisition, growth would stagnate. If we didn’t crack this problem early, we risked either overspending on acquisition or running out of new users to onboard.
Implementation: The Technical Marketing Stack That Powered Growth
Prasid: You’ve already told us that the big unlock was to create thousands of individual ad units with specific cars that were currently available on the platform. So tell us more about how you built this.
Shaun: The key was automating the entire process. Here’s how we built it:
Data Integration: We pulled real-time vehicle inventory from our internal databases and formatted it into structured datasets.
Automated Creative Generation: We used Smartly to dynamically generate ad creatives, overlaying real-time pricing onto professional car images.
Deep Linking: We leveraged Branch to create custom deep links, taking users directly to the relevant car listing inside the Fair.com app.
Programmatic Deployment: The ads were pushed automatically to Facebook and Google, ensuring we showed the right car to the right person at the right time.
This system eliminated the need for a manual creative pipeline while maintaining high-quality, highly relevant ads.
Outcome: The 90% CAC Reduction
Aditya: HowI love this so much. Can you share more about the results?
Shaun: The impact was massive. Our cost-per-pre-approval, which was our leading indicator of marketing efficiency, dropped by 90%. Instead of targeting broad audiences with generic creative, we were delivering personalized, high-intent experiences that converted at a much higher rate.
Prasid: Did you continue iterating on this approach?
Shaun: Definitely. As we scaled, we refined our dynamic creative strategy to improve engagement and conversion rates. We introduced A/B testing on ad variations, experimented with different price anchoring strategies, and fine-tuned how we retargeted users who engaged but didn’t convert.
Lessons Learned: Takeaways for Growth Leaders
Aditya: What were the biggest takeaways?
Shaun:
Automation unlocks scale. Manually creating thousands of personalized ads is impossible. Automating that process was the key to making it work.
The closer you get to real user intent, the better your results. Showing real, available inventory instead of generic ads made a huge difference.
Growth Ops is about making things happen despite constraints. We didn’t have a huge engineering team, but we found creative ways to execute.
Prasid: Any predictions for the future of Rev Ops?
Shaun: Rev Ops will only become more critical. Companies will need marketers who understand data, automation, and engineering to scale efficiently. The best growth leaders will be the ones who can bridge those gaps.
Aditya: If you were advising a company facing a similar challenge, what’s your top piece of advice?
Shaun: Invest in automation early. The earlier you can eliminate manual bottlenecks in growth, the faster you can scale efficiently.