Programmatic advertising has been augmented by the depth, scale and accessibility of data. It is used to enrich advertising by enabling granular targeting and critical post analysis of campaign performance. Data and the competitive nature of the advertising industry, has mobilized the practice of yield optimization allowing it to become a standard skillset throughout the programmatic landscape.
What is yield optimization?
Yield optimization can be loosely defined as using data analysis and optimization techniques to maximize performance and revenue. There is always a necessity for efficiency, especially when each party involved wants to attain optimum value and return on investment for their business. With the growing amount of data points that are being made available within the industry, yield optimization can be a daunting task. This often encapsulates dissecting huge amounts of data and applying strict process-driven tasks to realize successful, tangible outcomes.
For publishers of online media, yield optimization can involve the analysis of existing data to determine which areas of inventory are performing well, and which are not. The key part of yield optimization is understanding the precise elements or circumstances of these areas and exploiting them to maximize efficiency.
One of the simplest forms of inventory optimization from a publisher's perspective is correctly valuing inventory. By effectively managing the value that they sell ad space for and applying floor prices against specific inventory segments, they can ensure a healthy balance between fill rate and eCPM value, easily enabling overall revenue to be increased. Many SSPs now build out their own yield optimization tools within their platforms to highlight simulated opportunities based on different valuations of inventory. Due to the nature of programmatic video specifically, the flexibility of revaluing inventory depends on perspective and how many parties are involved in handling the inventory in question.
What are the opportunities and challenges for yield optimization?
With the escalating amount of different data points available, yield optimization is becoming more complex and data heavy. Some of the analytical skills used to extrapolate data and understand patterns and trends are bordering on data-science. In some circumstances, an advanced knowledge of mathematics and engineering can be a requisite to the skillset and achieving success. Presently, yield optimization can be viewed as a skillset applicable to an ad-operations, trafficker or account manager role. However, as accessible data, complex ad systems and technologies grow, so does yield optimization into it’s own field.
Vendors and partners on the supply side specifically, seem to be struggling to scale yield optimization operations for their platforms. Collating and breaking down vast amounts of data and relaying the essential information to clients in an understandable fashion can be a difficult process, particularly when the skills required are subset to an existing role. This evokes a large opportunity for specific role-driven yield optimization teams to be built out within their own businesses, to purely focus on increasing programmatic efficiency and thus turning over more revenue.
Looking forward, as more resources and investment are applied to yield optimization, the tasks involved should become more data-science driven where more mathematical algorithms and automated processes come into play, always with the aim to achieve greater efficiency and overall revenue for the programmatic industry.