Top-of-Funnel Optimization Case Study: When Healthcare Ads Thrive Without Purchase Data

Top-of-funnel optimization is an underused but powerful strategy in digital advertising. While most brands claim to understand its value, few truly prioritize it in practice. This case study shows how a healthcare advertiser adapted to a major platform shift and achieved excellent results—not by focusing on purchase events, but by embracing a broader, interest-based signal earlier in the customer journey.

This shift wasn’t a choice at first. It was a reaction to policy changes on Meta’s advertising platform. In January, Meta introduced new restrictions on how healthcare and wellness brands could use sensitive data to optimize their campaigns. These restrictions included a ban on using bottom-of-funnel conversion events, such as purchases or customer data, to improve ad performance.

That change left many advertisers scrambling. One healthcare brand, in particular, faced a serious challenge. They had long relied on purchase events to train Meta’s ad delivery algorithm. Once that path was shut off, they had to get creative.

Rather than panic, they turned to an existing piece of their web infrastructure: a survey. This survey lived near the top of the funnel and was designed to gauge interest in the product, not close a sale. But it offered one critical feature—it fired an event that Meta could still use for optimization.

Testing the Top-of-Funnel Signal

The team decided to run an incrementality test. The core question was simple: If Meta ads were optimized for the survey signal instead of a purchase event, would performance drop?

The outcome was a surprise. The top-of-funnel optimization worked just as well as the bottom-of-funnel event.

This test opened the door to a whole new approach. Rather than trying to reverse-engineer ways to get close to a purchase in an increasingly privacy-restricted landscape, the brand began focusing on signals of intent that occurred earlier. This new strategy allowed the brand to continue running effective ad campaigns without relying on conversion data that was now off-limits.

The results held up across various campaign types, suggesting that the algorithm could learn and adapt just as effectively using interest signals.

Why Top-of-Funnel Optimization Worked

This result challenges a common assumption in ad buying: that the closer a signal is to the purchase, the better it is for optimization. For years, marketers have prioritized lower-funnel data, assuming that only those events could deliver meaningful improvements to campaign performance.

But that model has become harder to implement. Platform policies, privacy regulations, and technical limitations (like cookie restrictions) have made it more difficult to use precise conversion data. That’s especially true in sensitive categories like healthcare.

In this case, the survey event had a few important qualities:

• It was tied to genuine user interest
• It occurred within the brand’s owned environment (the website)
• It happened frequently enough to provide Meta with consistent data

These attributes allowed the signal to function effectively as an optimization target, even though it was not tied directly to revenue.

Implications for Meta Ad Strategy

The success of this approach is not limited to healthcare brands. Many businesses in both B2B and B2C sectors can benefit from testing top-of-funnel events as optimization points. For example:

• In B2B, a whitepaper download or webinar registration might serve as a strong signal of intent
• In ecommerce, an “add to wishlist” or email subscription can indicate interest even if a purchase is not completed
• In SaaS, actions like product page engagement or demo video views might offer early signals that can drive downstream results

The key is to identify interactions that reflect real interest and can be tracked consistently. Meta’s machine learning models are designed to optimize toward meaningful user behavior. If a signal reflects genuine interest and occurs at scale, the platform can work with it effectively—even if it’s not a transaction.

Rethinking Incrementality and Measurement

Another important takeaway from this case study is the role of incrementality testing. The team did not simply assume the top-of-funnel signal would perform well. They tested it using a structured methodology to measure the lift in conversions caused by the campaign.

This allowed them to confidently compare the results of the new optimization approach against the previous one. The test showed that signups remained strong and incrementality held steady, even with the policy change in place.

More brands should adopt this practice. Rather than chasing perfect attribution, use incrementality testing to evaluate real-world outcomes. This provides a more reliable view of campaign effectiveness and helps validate new strategies like top-of-funnel optimization.

Why Brands Need to Shift Now

This case study comes at a moment when the digital advertising landscape is changing rapidly. Between privacy laws, platform restrictions, and increasing user skepticism, marketers can no longer rely solely on bottom-of-funnel tactics. The days of over-optimized retargeting based on purchase data are fading.

Top-of-funnel optimization is not just a workaround—it’s a necessary evolution. The brands that succeed in the coming years will be the ones that invest in broader audience engagement, long-term brand building, and smarter data strategy.

For this healthcare advertiser, what began as a reactive shift became a long-term advantage. They were forced to rethink their approach, but the results proved the potential of a new model. Rather than shrinking their goals, they expanded their strategy.

Final Takeaways for Marketers

This case study illustrates five core lessons for anyone running Meta ads:

• Do not panic when policy shifts disrupt existing models. Look for signals of interest you already own.
• Top-of-funnel optimization can perform just as well as lower-funnel tactics, especially when data is limited.
• Meta’s algorithm can adapt to earlier signals, as long as they are consistent and reflect genuine user behavior.
• Incrementality testing is a powerful tool to validate performance beyond vanity metrics.
• A proactive mindset allows marketers to turn compliance challenges into performance breakthroughs.

Whether you are running healthcare ads or building a B2B funnel, this is a model worth testing. Start by identifying what early-stage actions indicate buyer interest. Then train your campaigns to optimize for those signals. You may discover that the best way to drive results is to think upstream.