“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Scott Siegler, vice president of engineering and West Coast operations at Gamut.
Publishers face an uphill battle in today’s evolving media landscape. The emergence of new channels for media consumption, combined with the transition to automated media buying and selling, has significantly impacted the way advertisers conduct business. Shifting revenue streams have forced publishers to adjust their sales strategies to capitalize on both new opportunities and the growing demands from the buy side.
Perhaps the most significant hurdle facing publishers is overcoming a reliance on rate-card thinking and outdated systems. Too many publishers use legacy practices for their yield optimization strategies. They find it challenging to allocate resources to develop and transition to new processes that leverage big data.
This availability of information has influenced the way advertisers buy media and can help publishers address their inventory’s multifaceted appeal. And yet many publishers still price inventory by using historical data of sales from the previous month. To thrive in today’s marketplace, publishers must supplement this historical data with other richer, often real-time data sets.
Richer data sets allow publishers to respond to the growing demands of the buy side. As advertisers rely more on granular data to make media buys, publishers will need to use data to define their available inventory and differentiate impressions within the marketplace.
There will always be great impressions that advertisers want to purchase, and some that are not so great due to factors beyond the publisher’s control, such as a lack of cookie data. Leveraging pre-bid knowledge around what influences an individual piece of inventory is key to navigating the changing needs of advertisers.
This kind of real-time data also enables publishers to cohesively address oscillating demand across all inventories, from the most desired impressions to the least. Today’s sales cycles are measured in milliseconds, as opposed to months. Via programmatic platforms, demand can rise and fall as quickly as an impression is purchased. As a result, publishers must focus beyond historical data to support this new media buying and selling environment.
There are other dimensions beyond coarse-grained CPM history that display strong signals on the exchanges, including user segmentation, geodata, time of day, page-level context, page-level sentiment and viewability, to name a few. Relying simply on historical price data dilutes the impact of these other components, ultimately negatively impacting overall yield.
Lastly, this data allows publishers to focus less on their inventory and more on what they do best: publishing content. In the end, the single most strategic action a publisher can take to drive yield is to create compelling content that attracts the audiences advertisers are interested in buying. And while not all impressions are created equal, publishers that leverage the automated intelligence of relevant data sets for a multifaceted pricing strategy can spend less time fretting over individual impressions and more time on the content that drives their audience.
A Tiny Slice
In the end, historical data is generally just that: historical. For a given publisher, it often captures only a small slice of what’s really going on.
Today’s demand channels react quickly to new price points, and thus a single recommendation based on backward-looking price data is obsolete in minutes.
In the end, a rate-card-like perspective ends up leaving significant opportunity to maximize yield on the table. But modern technology can enable exchanges to index a publisher’s demand against broader categorical or full-market trends. And price, as a result, can become a dynamic, multifaceted strategy that empowers publishers with as much real-time intelligence as the advertisers.