Campaign Operations
Eloqua
A global software company running content syndication at enterprise scale: 400,000+ lead records, 700+ active campaigns, and a single Marketing Operations analyst responsible for verifying lead quality across all of them. The result: manual analysis time reduced by 50% and consent errors eliminated entirely.
The reporting process looked functional on paper. In practice, it was held together by a single Excel file that was never designed for the volume it was carrying.
Every week, the analyst pulled data from multiple sources, merged it manually, and ran verification checks across 400,000 lead records spanning 700+ active campaigns. The file crashed. Merges introduced errors. Identifying a bad batch from an external vendor meant going back through rows that had already been checked. The process consumed 6 hours a week, not because the analyst was slow, but because the architecture could not keep up.
The deeper problem was not time. It was confidence. When a report took that long to produce, there was always a question of whether the numbers were current. Incorrect marketing consents were slipping through. Data issues from external sources were caught late, sometimes after leads had already moved downstream.
The team knew something needed to change. They had no clear path forward that would not require months of disruption during an active campaign season.
We started with the reporting layer, not the data model. Fixing the wrong layer first would have cost months of rework during live campaign operations.
Power BI became the centralised platform for all lead quality analysis. The implementation focused on four specific changes.
The decision to build inside the existing stack, integrating with Marketo, Salesforce, Workfront and the broader Adobe Experience Cloud environment, avoided a platform migration entirely. The team got a fundamentally better reporting capability without changing the tools they already relied on.
The impact showed up faster than expected, and in places beyond the obvious time saving. When the reporting layer stopped being a bottleneck, the analyst's entire working rhythm shifted. Weekly reviews that used to start with data cleanup started with actual analysis. Vendor performance patterns became visible for the first time. The compliance gap, previously unquantifiable, closed entirely.
Before, 6 hours a week disappeared into a process that produced a report the team was not fully confident in. After, the same coverage takes 3 hours, and the output is live, not assembled.
The 50+ hours freed per quarter did not just reduce workload. They shifted what the analyst was actually doing. Less time spent verifying whether data was correct meant more time spent on what the data was saying: pattern analysis, vendor performance, campaign-level quality trends that were previously invisible.
The consent issue deserves specific mention. Incorrect marketing consents reaching downstream systems is a compliance risk, not just an operational one. Eliminating that failure point was not a side effect of the project. It was one of the clearest signals that the verification logic was now working the way it was always supposed to.
Three months in, the team had a reporting setup that scaled with the volume. Not one that buckled under it. Lead quality verification that once consumed 6 hours a week now runs automatically, at full scale, every day.
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