MANSCAPED improved ad performance and revenue by unifying customer events with RudderStack
When performance marketing is your growth engine, the smallest data problems become expensive fast.
A dropped event is more than just an analytics gap. It’s broken attribution and undercounted conversions. It’s a platform learning from incomplete signals. And when every channel has its own pixel, SDK, and required parameters, the path to better data can quietly turn into a path to slower site performance and a stretched engineering team.
MANSCAPED ran into this exact set of tradeoffs as the brand scaled its paid media and lifecycle programs. They needed better event quality and faster iteration without turning every marketing request into a custom engineering project.
So they rebuilt how customer data moved from their product to their warehouse to their ad platforms.
Main takeaways
- Unify collection to eliminate pixel sprawl. MANSCAPED reduced the operational burden of managing separate tags for every platform by centralizing event collection through RudderStack, with more workflows handled server-side.
- Make Snowflake the shared source of truth. Streaming customer events into Snowflake aligned marketing and data teams on consistent, governed data for measurement, segmentation, and iteration.
- Improve conversion signals, not just reporting. Upgrading Conversions API delivery and deduplication improved event match quality, giving ad platforms stronger inputs for optimization.
- Use in-flight transformations to enforce control at speed. Allowlists, destination-specific filtering, and enrichment kept data relevant and compliant while reducing engineering involvement for ongoing changes.
- Performance gains compound when data is trustworthy. Better event quality and faster iteration translated into measurable outcomes: higher revenue and improved CPA across major paid channels.
The problem with one-off pixels: Slow sites, slow teams, and inconsistent data
MANSCAPED’s marketing team works closely with advertising partners to collect customer data for segmentation, targeting, and measurement. Over time, the normal way to support those programs often creeps in:
- New pixels and tags accumulate on the site.
- Engineering gets pulled into ongoing instrumentation changes and troubleshooting.
- Data arrives differently in each destination, making it hard to trust performance reporting.
- Page load suffers, which impacts customer experience and conversion rate.
MANSCAPED wanted to fix this at the root. Not by adding another tool, but by creating a single, governed customer data layer that could reliably serve both analytics and activation.
A phased approach: Centralize collection first, then modernize the warehouse
Rather than trying to replace everything at once, MANSCAPED mapped the work into two phases:
Phase 1: Move ad tags server-side and centralize data collection and governance.This reduces client-side bloat and makes it easier to control what data flows to each destination.
Phase 2: Redefine tagging and tracking and stand up Snowflake for advanced analytics use cases.This created a stronger foundation for attribution modeling, audience enrichment, and campaign analytics.
To accelerate time-to-value, MANSCAPED partnered with fifty-five, a martech consulting firm, to implement and operate the customer data layer using RudderStack.
The solution: a single customer data layer powered by RudderStack and Snowflake
RudderStack acts as customer data infrastructure: It collects customer events, applies transformations and governance in-flight, and delivers those events to downstream systems, including Snowflake and paid media destinations.
With fifty-five leading the implementation, MANSCAPED put three foundational capabilities in place quickly:
1. A consistent tracking foundation for ecommerce events
The team started with an ecommerce tracking baseline and then extended it with MANSCAPED-specific events to cover the customer journey, especially the checkout flow.
The goal was simple: Standardize the most important conversion signals and make them consistent everywhere they land.
2. In-flight transformations to enforce control, reduce noise, and move faster
As the business scaled, two pressures increased at the same time:
- Compliance and governance requirements (what data can go where, and why)
- The cost of shipping too much data to too many tools
Using RudderStack Transformations, fifty-five implemented rules to shape event payloads in real time.
That included an allowlist model so only relevant fields reached each destination, plus destination-specific enrichment when ad platforms required additional parameters.
This became a force multiplier for speed. Marketing could get what they needed without turning every change into a front-end release, and engineering got pulled out of the loop for many day-to-day instrumentation updates.
3. Better Conversions API performance through deduplication and match quality improvements
MANSCAPED also upgraded how it sent conversion signals to ad platforms, including Meta’s Conversions API (CAPI).
Before, they struggled with matching and deduplication, which lowered event quality scores and reduced the effectiveness of optimization.
After switching to RudderStack’s Facebook Conversions integration, MANSCAPED improved event match quality substantially, nearly eliminating events rated “okay” or below. Better matching meant cleaner conversion signals and stronger feedback loops for platform optimization.
The results: Stronger performance signals, lower CPA, and measurable revenue impact
With a unified data layer in place, MANSCAPED reduced the number of independent tags loaded on the front end. In just six months, they moved from managing multiple platform tags separately (TikTok, X, Snap, Pinterest, Dynamic Yield, Meta CAPI, and more) to running collection through a single, consistent approach, with more workflows handled server-side.
That shift created three outcomes:
- Faster iteration for marketing because the data was consistent and easier to activate
- Less engineering burden because many changes no longer required front-end code modifications
- Better performance marketing efficiency because ad platforms received higher-quality conversion signals
Reported impact included:
- 37% increase in revenue
- 10% improvement in Meta CPA
- 29% improvement in TikTok CPA
- 162% increase in revenue from Snap Ads after implementing CAPI
These gains came from making customer event data more reliable, more governable, and easier to deliver everywhere it needed to go.
Why this matters now: AI raises the cost of bad customer data
This story is not just about better ad performance. It is about building the kind of customer data foundation modern teams need as systems become more automated.
As AI and automation move closer to customer-facing decisions, the tolerance for drift drops. Teams need customer context they can trust, delivered quickly, with governance enforced before data fans out to dozens of tools.
That is the direction MANSCAPED moved in:
- One source of truth in Snowflake
- Real-time transformations and routing controls
- Higher-quality conversion signals for paid platforms
- Faster iteration loops without slowing the product
Ready to learn more?
For the complete story and implementation details, read the full MANSCAPED case study.
FAQs
Server-side tracking sends conversion and behavioral events from your servers (or a controlled collection layer) instead of relying only on browser-based pixels. It can improve reliability, reduce data loss from blockers, support better deduplication, and produce higher-quality conversion signals for ad platforms.
Server-side tracking sends conversion and behavioral events from your servers (or a controlled collection layer) instead of relying only on browser-based pixels. It can improve reliability, reduce data loss from blockers, support better deduplication, and produce higher-quality conversion signals for ad platforms.
Event match quality reflects how well an ad platform can match your conversion events to real users. Higher match quality typically improves optimization and attribution because platforms can learn from more complete, correctly-identified signals.
Event match quality reflects how well an ad platform can match your conversion events to real users. Higher match quality typically improves optimization and attribution because platforms can learn from more complete, correctly-identified signals.
Transformations let you modify event payloads in-flight, including filtering fields, standardizing properties, and adding destination-specific parameters. This reduces the need for repeated front-end engineering changes and makes it easier to iterate on activation requirements.
Transformations let you modify event payloads in-flight, including filtering fields, standardizing properties, and adding destination-specific parameters. This reduces the need for repeated front-end engineering changes and makes it easier to iterate on activation requirements.
Streaming events into Snowflake creates a shared source of truth for marketing, analytics, and data teams. It supports faster measurement, more consistent attribution analysis, and better downstream activation because teams are working from the same governed dataset.
Streaming events into Snowflake creates a shared source of truth for marketing, analytics, and data teams. It supports faster measurement, more consistent attribution analysis, and better downstream activation because teams are working from the same governed dataset.
When every destination adds its own pixel and SDK, page weight increases and performance suffers. A unified layer reduces tag sprawl by centralizing collection and sending data to multiple tools from one controlled pipeline, with more workflows handled server-side.
When every destination adds its own pixel and SDK, page weight increases and performance suffers. A unified layer reduces tag sprawl by centralizing collection and sending data to multiple tools from one controlled pipeline, with more workflows handled server-side.
Published:
February 9, 2026
