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Envisioning The Era of Data-driven Publishers

If you don't know how your ad stack is doing, how can you know if adding a partner or tweaking a floor helps or hurts you?
To maximize their revenue, publishers have tons of puzzles to solve on a daily basis. Besides big picture goals like maximizing CPMs and increasing competition for their impressions, a lot of time and work goes into clearing ad stacks from low-quality bidders, reducing latency, and preventing malicious popup ads. It's essential work: Daily pursuit for quality improves the user experience, increasing page views and preventing money from slipping through the cracks. However, ad stack optimization is much more daunting than it sounds. Its success directly depends on ad auction data (its recency, quality, and availability). If you don't know how your ad stack is doing, how can you know if adding a partner or tweaking a floor helps or hurts you? Collecting data, processing, and analyzing it are big challenges that most publishers haven't solved yet. It's not easy; the data's fragmentation, industry giant's walled gardens, and middlemen's data black boxes are all huge obstacles.
Although header bidding provides more transparency into ad auctions, struggles remain. First of all, as you increase the number of bidders for your inventory, you multiply ad server vs demand discrepancies, directly influencing your revenue. When your ad server reports 40 - 60% more impressions than each of your five demand partners, it's crucial to understand where your potential revenue is slipping through, which source has the most reliable data, and how to repair the cracks. Also, publishers struggle to monitor bidder's loading times for real users despite the fact that latency is a well-known issue of client-side header bidding.
These struggles bog down AdOps. Instead of reacting to the data, publishers are - more often than not - simply guessing as to what the data actually is. But when publishers have access to unified ad stack data with advanced segmentation, the sell-side ad operations function is transformed into pure strategic analytics. More simply, when the process of data collection, processing, and reporting is automated, the data is clean, and presented in an easy-to-understand way, the work of a publisher changes from operational to purely analytical.
In the era of data-driven publishers, clean and comprehensive data about a publisher's ad stack performance would be the main driver for building and maintaining a successful monetization strategy. With header bidding, comparing demand partners based on a combination of revenue, win rate, timeout rate, and payment terms - instead of relying solely on revenue - would turn around a publisher's approach to optimizing the setup and demand partner choice.
The consequences of this transformation are huge. Gone are the problems of poor data quality and accessibility. The ability to make strategic decisions based on raw data will be the core competency for the sell-side AdOps. Those who understand the data will be able to develop the optimal ad stack for their business model, track demand changes, and stabilize the market. While the buy-side is planning trips to Mars, sell-side is still building its first rail road. While publishers waste their resources on collecting data from different data sources, manually formatting and merging excel files, and using a dev tools console for real-time analytics, data-driven decisions are just a dream. Just how far off is this new era of data-driven publishers?
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