Advertising sales, especially programmatic, generate an overwhelming amount of data. In the days of direct deals, a contract typically involved just a buyer and a seller. Today, even a standard PMP introduces additional layers such as agencies and intermediaries. Moving into header bidding, the number of participants inflates quickly, along with the data they generate.
That complexity is both a blessing and a curse. On one hand, it creates a level of transparency and insight that didn’t exist before. On the other hand, it produces more data than most teams can realistically process.
Luckily, there are solutions to data collection and storage. Between ad servers, SSPs, and analytics platforms, publishers are not short on data. The real bottleneck now is access and speed. Getting the right data, in the right format, to the right people, fast enough to act on it, is where things break down.
This is where AI assistants start to make a real difference.
Challenges Publishers Face in Utilising Ad Sales Data
Volume is not our problem nowadays; it’s usability. Based on what we see across publishers and sales houses, a few challenges consistently arise.
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Data Fragmentation – Ad servers, SSPs, exchanges, and direct deals all produce separate data streams. Without proper integration, it is difficult to get a reliable, unified view. We recommend daily API-based data collection to automate the process, like our Ad Revenue Insights module.
- Slow Reporting – At many organisations, access to data is limited to BI teams or even just a handful of super users. Bottlenecks! This means that Sales, RevOps, and AdOps teams, the people who actually act on the data, often do not have direct access. The result is slower decision-making and missed revenue opportunities.
- Missed optimisation opportunities – Data alone does not drive performance. If pricing inefficiencies, underperforming placements, or demand partner issues are not surfaced quickly, revenue is left on the table. This is especially true in environments where the people analysing the data are not the same people responsible for optimisation.
The practical solution for this is more data accessibility. Technical complexity can stay under the hood, but the insights should not. Teams need a single, reliable view of revenue data that is accessible across the organisation, with the right level of control and governance.
How to Make Ad Sales Data Easier to Understand?
Okay, say that you’ve got all the data you need in a neat and accessible system. How do you make sense of this large chunk of data if you are not an Excel magician or analyst? Traditionally, ad sales professionals have had to manually combine and analyse data from multiple systems, which is time-consuming and prone to human error. AI streamlines this by providing direct answers, but AI is only as good as the data it can access.
With the right setup, AI can answer questions like:
- Compare revenue and viewability of deals between Q2 and Q3, and highlight changes
- Identify advertiser churn and quantify revenue impact from lost and new buyers
- Flag underperforming placements compared to historical benchmarks
- Rank bidder performance based on revenue, win rate, or bid density
This is not, and will never be, about replacing analysts. It is about reducing the time it takes to get from question to insight, especially for people who are not professional data engineers. AI helps surface patterns and anomalies faster, so sales & yield experts can focus on decisions rather than data wrangling.
How to Leverage AI Safely?
AI is a powerful support tool, so treat it as such. Its effectiveness depends entirely on data quality, structure, and completeness. Poor inputs will lead to unreliable outputs. Even with good data, AI can still produce incorrect or misleading interpretations.
A few practical rules that we recommend:
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Treat AI as a support layer, not a source of truth
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Validate outputs, especially when they inform revenue decisions
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Ensure your underlying data is accurate and regularly updated
AI works best when paired with human judgment. The goal is not automation for its own sake, but better and faster decision-making.
How Do Relevant Yield and Relevant AI Help in Making Better Decisions?
Most publishers already have analytics tools. If you don’t, run to our HB Analytics & Ad Revenue Insights. With a great analytics system in place, you can start tackling the bigger issue - connecting fragmented data and making it actionable in real time.
Relevant Yield solves this by unifying programmatic and direct revenue data into a single view, so teams can monitor performance and spot issues without jumping between platforms.
Relevant AI builds on top by adding an analysis layer. Users can query data in natural language and get instant insights, from trend analysis to performance comparisons across demand sources, placements, and deals.
The real value is speed. Faster access to insights means faster optimisation and better revenue outcomes. If you are already using Relevant Yield, activating Relevant AI is a straightforward way to reduce manual analysis and improve decision-making.