This post is an excerpt from our newest guide to data analytics for publishers. Download the whole thing here!
Ad servers are great at serving ads, but they’re notoriously bad at providing good analytics or actionable revenue insights. This is because the technology required to provide a response time of mere milliseconds for selecting a targeted ad with 24/7 reliability is very different from the database crunching and visualization capabilities required for powerful data analytics.
In the early days of the internet, the publisher business model simply relied on basic business intelligence gathered from high-level reports. You really could run a multi-million dollar business on a spreadsheet because targeting was simple, products were limited, and you were only managing one sales channel. Those days are gone, and today’s publishers need more than Microsoft Excel to understand the risks and opportunities for their businesses.
The stakes could not be higher for publishers as they push into this ever-more complex world. A publisher’s business today sits across multiple direct and programmatic sales channels, supports a sophisticated set of contextual and audience-based targets, and has to monetize across desktop, mobile, native, and video platforms. Within each sales channel as well, there’s a further stratification of inventory into super-premium, premium, and secondary tiers. The need for good data analytics has become paramount to remain competitive now and in the foreseeable future.
Download the full guide here to learn how today’s top publishers are using data analytics to maximize yield.