The Challenge
Paire's ad spend wasn't telling the full story
Nathan had already tried several tracking tools promising big performance improvements.
He previously used Instant, which claimed to boost Klaviyo flows. While Instant dashboards showed significant improvements, further analysis showed no meaningful lift in top-line revenue or efficiency. The cost was also nearly 2x that of Upstack.
Meta performance was strong, but upon review, their CAPI provider simply wasn't equipped to provide advanced enrichment capabilities.
As Paire scaled, acquisition costs were climbing. Roughly $25,000 in sales went unattributed before they tightened their setup. That meant they couldn't link revenue back to the campaigns that deserved more investment. Spend was increasing, but efficiency was dropping.
Over the 60 days prior to Upstack:
- Revenue scaled 25%
- Spend increased 47%
- MER dropped 17%
After trying other tools without results, Paire was skeptical of what Upstack could do differently.
Paire wasn't feeding the algorithm with high-quality data to drive efficient spend. They needed sharper visibility into what drove sales. Missing insights left the brand unsure whether to invest more, cut back, or shift their campaign strategy.
Our problem was a very classic Triple Whale solving problem — we didn't know which creative or campaign was actually bringing the return in revenue. Facebook has a tendency to bloat the ROAS. We just wanted to get the real ROAS.
The Solution
Building confidence in campaign spend and signal optimization
Before Upstack, Paire was running campaigns without a clear picture of returns. Events weren't firing consistently, and revenue tracking often missed the mark. The brand knew they were wasting money but didn't have the data to act on it.
Despite his doubts, Nathan decided to trial Upstack. In less than 30 minutes, Upstack replaced their existing solutions with:
- No developers needed
- No creative changes required
- No migration headaches
From day one, Upstack's Identity Platform went to work:
With higher-quality identity data flowing into Meta, the algorithm could finally optimize toward the right audience.
How they did it
- Stitching fragmented sessions - into real customer journeys
- Resolving anonymous traffic - into real people
- Feeding Meta enriched conversion events - via Upstack Signal
The Results
Smarter data led to increased spend efficiency and drove incremental performance improvements
When Paire came on board, event gaps and missing attribution made it difficult to trust campaign performance.
Upstack cleaned up event capture, stitched signals across devices, and tied campaigns to true purchase behavior. Within months, Paire could see which ads were profitable and which weren't — letting the team pull back wasted spend without losing revenue momentum.
After 60 days with Upstack Signal and Upstack Flow:
- Blended CAC decreased by 20%
- MER improved by 24%
- RAAD (Revenue After Ad Spend) up 18%
- Drove $28K incremental revenue with Upstack Flow
Paire was now able to meaningfully improve their CAC and MER. Meta's algorithm began targeting better and performing predictably, and the team could finally scale spend with confidence.
- Blended CAC decreased by 20%
- MER improved by 24%
- RAAD (Revenue After Ad Spend) up 18%
- Drove $28K incremental revenue - with Upstack Flow
The Bottom Line
From skeptics to believers
The Results:
- Reduction in CAC
- -20%
- Improvement in MER
- +24%
- ROI
- 25x
