Headless Marketing Performance Data
A smarter architecture for automating marketing performance data. Complete, automated, and accurate — delivered to every tool your team already uses.
Marketing ops teams are drowning in tools but starving for good data. You have data everywhere — ad platforms, CRMs, marketing automation, analytics — each one a silo, each one self-serving. Google Ads takes credit for the conversion. HubSpot says it was the email. LinkedIn claims the ad drove the deal. Every platform is telling the version of the story that makes itself look good.
No wonder nobody trusts the numbers. You spend half your time reconciling conflicting reports instead of acting on them.
Headless marketing performance data fixes that. It's an architecture that separates the data layer from the consumption layer — meaning your performance data lives in one clean, governed place and flows to every tool your team already uses.
But before you ask: yes, the data is actually good. Read on to learn more.
Three Pillars of Data You Can Trust
1. Complete
Incomplete data is worthless data. Most platforms track what's easy — ad clicks, form fills, email opens — then stop.
We go deeper. We capture the full buyer journey: website behavior, intent signals, account-level engagement, offline touchpoints, channel interactions, and pipeline events. Every touchpoint that matters is included in your attribution model, not just the ones that fit in someone else's dashboard.
Incomplete data leads to wrong budget decisions. Complete data leads to confident decisions.
2. Fully Automated
Your current approach probably looks like this: manual data pulls, scheduled syncs that break, reconciliation spreadsheets, weekend updates, CSMs asking “can you rerun that?”
Headless means automation end-to-end. Data flows continuously from your tech stack into a unified performance layer. No waiting for batch jobs. No manual refreshes. No reconciliation theater. When a deal closes in Salesforce, it's in your attribution model. When an account engages with content, it's reflected in real-time.
That's not just about speed. It's about trust. Automated data is accurate data. Manual data is debt.
3. Accurate Conversion Tracking
Attribution models are only as good as the conversion data feeding them. Most platforms use flawed tracking — cookie-based attribution that misses B2B's reality, delayed syncs that lose data, or rigid models that don't match your business logic.
We use comprehensive conversion tracking that captures offline interactions, account-level movements, and pipeline velocity. Your attribution reflects how your business actually works, not how a SaaS vendor thinks it should work.
That accuracy means your channel spend decisions are defensible. You can explain to leadership why you're doubling down — because the data backs it up.
Unifying Data Sources Across the Chaos
Connecting your tools has never been easier. Fivetran, Airbyte, native connectors — you can dump every silo into a cloud data warehouse in a weekend. That part is solved.
Here's what people underestimate: having all your data in one place doesn't mean it makes sense.
You now have Google Ads data sitting next to Salesforce data sitting next to HubSpot data sitting next to your product analytics. Each source has its own definitions, its own timestamps, its own version of what a conversion is. Google Ads calls it one thing. Salesforce calls it another. Your email platform has a third opinion. They're all in the same warehouse now — and they still disagree.
A data warehouse gives you storage. It doesn't give you understanding.
What's missing is the application layer — the logic, the business rules, the reconciliation engine that takes all those conflicting data sources and produces a single, authoritative version of the truth. One definition of what a conversion is. One attribution model applied consistently. One timeline that every downstream tool can trust.
That's what we built. Not another connector. Not another warehouse. The intelligence layer that sits on top of your data and makes it mean something — so when it flows into Salesforce, Looker, Claude, or any other tool, everyone sees the same story.
The Hidden Cost of Building It Yourself
Some teams try to do this in-house. They hire data engineers, invest in dbt, build custom transformation logic, and eventually get to a unified system. They do a good job. The business rules are solid. The data is clean. It works.
Then one of those two people leaves.
Now you're maintaining a complex, proprietary system that was custom-built for a company that no longer exists internally. The knowledge that took years to accumulate lives in someone's head — and they're gone. You're stuck reverse-engineering a black box you didn't build, trying to figure out why a field changed or what a transformation actually does.
You hire someone new. They spend months learning it. Then they leave too.
That's the real cost of in-house solutions: you don't just maintain the code. You maintain the institutional knowledge. And when that knowledge leaves, you're paying forever.
Why This Matters to You
You know the feeling: you spend 80% of your time getting data right and 20% using it. That's broken. With headless marketing performance data, the split flips. The data architecture handles the heavy lifting — ingestion, normalization, deduplication, validation — so you can focus on strategy, not plumbing.
Less time wrangling data
Data comes in clean, complete, and ready to use. No reconciliation spreadsheets.
Confidence in reporting
You're not explaining why Salesforce and Looker show different numbers. There's one source of truth.
Speed to insight
Answer “what's working” in minutes, not weeks.
Less dependency on analysts
Your team can self-serve reporting because the data is reliable.
The Homegrown Approach
You know what happens when MOPs teams decide to build their own: six months of engineering work, 200 hours of data modeling, custom integrations that break every time a vendor updates their API, and a maintenance burden that never ends.
- —$200K+ in engineering costs
- —Six-month timeline before any value
- —Brittle integrations that break on API changes
- —Only 2-3 people understand the system
- —Rebuilds needed when martech stack evolves
- —Plug in your stack and go
- —Live in days, not months
- —No custom code to maintain
- —Complete data, your control, full flexibility
- —Zero engineering debt
Centralized attribution platforms promise to solve everything. In reality, they create a new problem: your team has to adopt another tool, learn another interface, and live in another dashboard.
Headless flips that.
Your team gets complete, automated, accurate marketing performance data delivered to the tools they already use every day. No new workflows. No adoption friction. No learning curve. That's not just better design. It's the only architecture that actually scales across a modern martech stack.
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