7 Approaches for Programmatic Optimization

There is no single way to optimize programmatic inventory, mainly because not all publishers are working with the same demand sources. Each demand source partner may be using different platforms.
Publishers that use multiple-demand source platforms have many challenges, and among them is data aggregation. There is no single way to optimize programmatic inventory, mainly because not all publishers are working with the same demand sources. Each demand source partner may be using different platforms. Yet Publishers still need a clear view of the data and what it means for their business.
Understanding the most popular approaches for programmatic inventory optimization provides additional insight, as it offers a better understanding of which approach may fit your situation. Here are the seven common approaches for non-direct inventory optimization.

Finding the Right Approach
Data aggregation tools only come into play once a publisher understands what needs to be analyzed, but in some cases, publishers aren’t sure of what the best methods for optimization are. In fact, some aren’t aware that dimensions that work extremely well on one platform may be entirely unavailable on another. Yet getting this right is the key to success.
The optimizations listed below are helpful for increasing revenue by reducing inefficiencies across platforms and uncovering critical issues and discrepancies.
In addition, these methods provide additional insight into the programmatic business and how it can drive more indirect programmatic buys and provide an avenue for more direct-sold business. Here’s a quick guide to the variety of methods that publishers are successfully using for non-direct inventory optimization.

1. Partner by Deal. Most often used for troubleshooting and understanding whether a deal is working properly. This approach breaks down each “deal ID,” which allows publishers to view which partners are offering the most in programmatic deals.

2. Buyer by X (Ad Format or Device Type). Locking down the preferences of buyers into specific ad formats offers a better understanding of what audiences look like on open exchanges. However, not all programmatic platforms have detailed breakdowns of Device Type.

3. Advertiser by Buyer. Compiling this information can increase the understanding of which brands work with which advertisers, and how much money an advertiser is spending on programmatic. This could lead to an avenue of generating more direct sale revenue.

4. Ad Unit by Country. Ad units that get sold to open exchanges in other countries may have some additional optimizations especially if certain ad units have low eCPM or fill rates. This also allows a view into whether there are international revenue opportunities, especially for sites that have global demographics.

5. Device by Country. Device type dimensions help programmatic traffickers understand the audience that is viewing the ads in different countries.

6. Advertiser by Ad Format. Provides a view of what ad formats are highly sought after, which may open the door to direct-sold businesses.

7. Buyer by Ad Unit by Site. Offers a view of what ad units and what sites are in high demand. This may affect the way a trafficker would target these ad units to the buyers that compete with one another.

When optimizing non-direct inventory, there isn’t a one-size-fits-all approach. Every digital publisher optimizes non-direct inventory its own way. However, with the above approaches in mind, Tercept has built its dashboard to be as flexible and agile as possible to meet publishers’ changing needs.

Author
Gourav Chindlur
CEO, Tercept

  • Share:

Leave a Comment

Your email address will not be published. Required fields are marked *