AdPlunge · Advertising Network
22.4% improvement in advertising revenue per thousand impressions.
Connecting publisher inventory, demand participation, advertising yield and revenue performance to identify monetisation inefficiencies.
AdPlunge operated across multiple publishers, advertising placements and demand integrations. Revenue performance depended on several interacting variables, including available impressions, fill rates, publisher traffic composition and demand-source participation. A revenue decline did not necessarily indicate weaker traffic, and traffic growth did not always translate into proportionate monetisation. Investigating these relationships manually made it difficult to isolate the cause of performance changes quickly.
What Argus examined
LampCal analysed performance across 18 publishers, 146 advertising placements and six demand integrations. Argus connected publisher revenue to monetisable inventory, fill rate, RPM, geography, device and demand-source participation. The investigation distinguished changes in traffic volume from changes in monetisation efficiency and highlighted the placements and segments requiring technical or commercial review.
What it found
Uneven revenue efficiency across publisher inventory
The analysis identified differences in revenue efficiency across placements, devices and geographic segments. High impression volume was not consistently associated with strong advertising yield.
Fill rate averaged 68.4%
A material proportion of available advertising opportunities was not being filled. Argus connected fill-rate performance to placement and demand-source participation, helping prioritise configuration reviews.
43 monthly reconciliation discrepancies
Revenue and reporting discrepancies required investigation across publisher and demand records. These exceptions increased the effort needed to understand reported monetisation performance.
Performance investigations required 11 hours
The existing investigation process required repeated comparisons across reporting sources. Connecting these relationships reduced the time needed to isolate the relevant publisher, placement or demand signal.
How it connected the pieces
Argus created a connected view of publishers, advertising placements, traffic characteristics, demand participation and revenue. When a meaningful performance change occurred, the investigation traced the affected segment and compared contributing metrics. Recommendations were linked to evidence and routed into configuration or commercial review, with subsequent results compared against the original baseline.
From finding to decision
Publisher yield investigations were connected to operational review. The process identified a performance deviation, isolated the contributing variables, assigned the relevant technical or commercial action and compared performance after the approved change.
- Detect material changes in publisher yield or fill rate
- Investigate placement, geography, device and demand-source relationships
- Prioritise technical or commercial configuration reviews
- Record approvals and implemented changes
- Monitor publisher RPM, fill rate and revenue against baseline performance
What changed
| Measure | Before | After |
|---|---|---|
| Blended publisher RPM | $1.74 | $2.13 |
| Fill rate | 68.4% | 77.5% |
| Monthly advertising revenue | $84,600 | $99,913 |
| Average investigation turnaround | 11 hours | 1 hour |
| Monthly reconciliation discrepancies | 43 | 8 |
The result
Blended publisher RPM increased from $1.74 to $2.13, representing a 22.4% improvement. Fill rate increased by 9.1 percentage points, while monthly advertising revenue rose from $84,600 to $99,913. Performance investigations that previously required approximately 11 hours were reduced to one hour. The monthly revenue difference was $15,313, equivalent to $183,756 on an annualised run-rate basis.
Advertising revenue figures represent gross revenue rather than net profit. Annualised revenue assumes the monthly improvement is sustained.