Publishers want to know: What’s my inventory worth?

The traditional valuation principle of matching users and associated inventory to interested demand has varying degrees of efficiency ranging from direct sold rate cards to individual bid prices. The advantage of programmatic is that there are more pricing signals available beyond just looking at average CPM and fill rate. With the right analytical approach, publishers and app developers can glean valuable data-driven insights to more confidently adjust their programmatic strategies to improve business results.

In particular, publishers have never had more opportunity to leverage analytical insights within programmatic advertising due to the greater transparency offered through newer technology, such as header bidding, and increased self-service from demand-side agencies and large marketers. The best ad tech partners help publishers and mobile app developers “connect the dots” by using data to more easily identify performance improvements themselves, and in the future, leverage automation to drive greater scale of optimization with less work.

Divide and Conquer

The beauty of programmatic is that the user associated with a given ad request has a range of value depending on the individual marketer’s targeting. The corresponding challenge is how to manage the packaging and pricing of inventory given each marketer places a different value on user characteristics, such as geographic location, device category, etc., and additionally may have their own audience segment data. Below are some tips that can help identify potential gains in a publisher’s programmatic business through inventory segmentation and demand analysis.

∙ Go beyond tracking based solely on ad tag, ad unit, or ad placement.  Marketers target on multiple criteria and evaluating performance with other data such as user’s country, user’s device, ad size, creative format, domain, content, etc. will help identify pockets of opportunity.  This level of granular segmentation can help influence content or application decisions to increase the amount of ‘valuable’ inventory.

∙ Understand the pricing controls available within the ad tech stack.  While having lots of granularity for data analysis can be alluring, the level of segmentation should ideally correspond with the ability to set up discrete pricing rules to maximize performance. Additionally, the level of complexity in managing settings is worth considering since the management of pricing rules is typically a recurring exercise in a dynamic programmatic marketplace.

∙ Be laser-focused on the specific demand opportunity. The ability to focus on the type of incremental demand that can be unlocked is insightful to guide the right analytical approach. For example, is the focus on increasing win rate for specific demand that are bidding at a high rate but not winning? Is it on increasing revenue from existing demand through greater price discrimination? Is it better management of ad quality blocking to allow some demand to flow through that may have been inadvertently excluded?  Asking the relevant question can help define the analysis and also make it easier to apply a change and monitor the impact.

Making It Real

Let’s look at how data-driven analysis helped a publisher improve their performance through more fine-grained price management. This particular publisher was interested in looking at the bid performance of different demand partners relative to their desired inventory breakdown using ad size, mobile vs. web, creative format, country, device category, etc. The focus of the analysis was to compare the expressed value from the demand partner in the winning bid amount, versus the realized clearing price after second price auction to inform whether pricing rule changes could increase revenue.

Here’s what the analysis highlighted:

∙ There were over 1,500 combinations of inventory segments purchased by >100 demand partners.

∙ When applying the average winning bid amount to the impressions that were served, the potential incremental revenue gain was 49 percent versus the realized clearing price. This gain assumes perfect price discrimination across all combinations and represents a theoretical upper bound.

∙ In practice, ranking the inventory-demand combinations by potential revenue gain revealed that the Top 15 combinations represented 80 percent of the incremental revenue. The publisher was able to experiment with specific pricing rules and floors for these high-value combinations to achieve an incremental 10-12 percent revenue lift.

∙ Additionally, the publisher was able to use the average winning bid amount by the advertiser as another input to help price direct campaigns and programmatic direct deals with confidence.

Bottom line: With increased data insights and greater transparency from leading programmatic partners, publishers and mobile app developers are more able to apply structured analysis to improve business results.