Why marketing data connectors quietly inflate your ETL costs
The hidden cost of marketing data at scale
Marketing data has three properties that make it uniquely expensive to move:
First, API fragmentation. Every platform exposes data differently. Even within the same vendor, accounts, regions, and ad products often require separate pipelines.
Second, volume volatility. Campaign spikes, seasonal spend, and experimentation can multiply row counts overnight. Pricing models based on rows or sources punish success.
Third, refresh pressure. Marketing teams want intraday data. ETL pricing often assumes daily batch usage. The mismatch forces teams to choose between speed and cost.
Individually, none of these are fatal. Combined, they create connector sprawl.
This is where costs stop being proportional to value.
How teams usually respond (and why it backfires)
When costs rise, teams rarely redesign ingestion. They patch around it.
Common patterns include:
- Spinning up multiple connectors for the same platform to isolate accounts or regions
- Reducing refresh frequency and accepting stale dashboards
- Pushing logic downstream to transformation to avoid ingestion charges
- Accepting vendor defaults because “it works”
Each workaround feels rational. Each one compounds long-term cost and complexity.
MVF had reached this point.
MVF’s marketing data reality
MVF operates a global customer generation platform where paid media performance directly impacts revenue. Their marketing data estate spans multiple platforms, regions, and account structures.
Specifically, their paid media ingestion included:
- Google Ads
- GA4 Export
- Facebook Ads
- Bing Ads
- TikTok Ads
- LinkedIn Ads
- Outbrain
- Taboola
Together, these connectors accounted for hundreds of millions of rows per year, forming the single largest category of ingestion in their ETL stack.
The issue wasn’t visibility or attribution. All the data was there.
The issue was how it was being pulled.
Multiple parallel pipelines existed for the same platforms, often split by account, region, or historical workaround. Costs scaled linearly with connector count, while insight did not.
This is where the economics broke.
The shift: consolidating paid media connectors
The change MVF made was not switching marketing platforms or reducing data.
They redesigned how paid media connectors were implemented.
Instead of treating Google Ads, Facebook Ads, Bing Ads, TikTok Ads, and LinkedIn Ads as collections of independent pipelines, MVF consolidated ingestion so each platform was accessed through fewer, better-designed connectors.
This meant:
- One connector per platform pulling multiple accounts where possible
- Fewer duplicated API calls across regions
- Shorter runtimes per connector, enabling faster refresh cycles
For example, the Bing Ads connector was optimised from roughly 1.8 hours per run to around 30 minutes, making intraday refresh feasible without increasing cost.
The same consolidation principles applied across other paid media connectors, reducing both runtime and connector sprawl.
Why this reduced cost without losing insight
Three things changed immediately.
Connector count stopped growing. New accounts and regions no longer required new pipelines.
Refresh frequency became a choice, not a trade-off. Faster pipelines meant marketing teams could see performance sooner without penalty.
Cost became predictable. Spend aligned with outcomes, not accidental duplication.
Importantly, no reporting fidelity was lost. In fact, clarity improved because data models were no longer compensating for ingestion quirks.
What leaders should take from this
Marketing data rarely looks like infrastructure, but it behaves like it.
Every connector is a design decision. Every duplicate pipeline is future cost. Every workaround is deferred debt.
MVF did not “optimise spend” by negotiating harder. They changed how data entered the system.
That distinction matters.
If your ETL costs keep rising while insight stays flat, the problem is rarely volume alone. It is almost always connector design.