The era of ‘sitting back and printing money’ is over for video advertising

The voracious appetite for video and increasing penetration of smartphones and tablets has fueled growth over the last 10 years for the medium, but the ‘sit back and print money’ days of video media and advertising are about to come to a screeching halt.

Literally, there is no more time that we can spend with media. The average person in the US spends just less than 12 hours consuming media today. Add to that sleep, time for personal-hygiene, and finding a few hours to actually work, you are left with little time left for growth. This makes for a new and unique set of business challenges for media sellers.

The problems during a time of growth and expansion in a market are about how to capture and reach net-new; that is what adtech has done for media over the last 10 years. It enables us to chase new audiences, on new devices, consuming new forms of content and advertising.

With no more net-new to be had, the industry faces a new set of problems. In particular, the challenges to be tackled today are business problems, not advertising problems. How do you maximize the value of a portfolio of products assets and increase them over time? This is very different from chasing eyeballs.

Other industries have coined this business function as yield optimization.

Yield optimization is starting to get more airtime across the industry, but often not until the wheels are about to come off at a media company because pricing and planning has simply become too hard due to the complexity caused by the number of ad products, platforms, channels, customers and data used for targeting.

In other words it is by force that yield optimization gets airtime, as an afterthought, and I would suggest that it needs to become a core competence at all media companies in the very near future given the unchartered waters ahead.

Yield optimization is a mindset, not an afterthought or analytics tool. It is about ensuring the right tools, data and team are in place to maximize revenue (and profit!) across every deal, and across the entirety of a portfolio.

And it is an ongoing process, because often, a deal may appear to be good on its own, but may undermine the ability to achieve overall revenue goes, in lieu of the goals for a particular customer, sales person, product or region.

In order to have confidence in every business decision, inventory and revenue management functions need to begin to use the same tools their advertising clients are using to do their jobs with greater precision and efficiency. Data, software and Artificial Intelligence (AI) will power the math and data-inclined teams that run pricing and planning functions at media companies in the future.

The problem in media is that yield optimization has gotten a bad rap thus far. It has been a value added ‘capability’ (note, not core function) of many adtech providers, but is often a derivative of display ad optimization logic and, done in a black box, myopically focusing on impression-by-impression delivery optimization.

This is important, but not going to suffice in helping sellers of video optimize yield across all products and platforms, as well as multiple currencies. Yield optimization and TV are only recently two things used in the same sentence, and that is because of the increasing demand for addressable and audience indexed targeted TV inventory.

There are a few things that make yield optimization in TV and online video a unique and new set of challenges, which must be tackled with a fresh approach. First, all video is supply constrained, or there is a shortage – very much unlike display. This greatly affects price elasticity, which is an important variable in yield optimization.

Second, the business cycle is unique for TV with most dollars being spent in the Upfronts (in the US) and more than 50% of online video being sold direct. There is very little ‘real-time’ in the sales cycle of TV and premium online video, so the math that powers the management of revenue (pricing) and inventory must account for that

And let’s call a spade a spade. Although buyers are demanding targeted TV, they are still mostly buying using Nielsen guarantees. The key is that more products (targeted, non-targeted, etc.), more currencies (GRP versus impressions, versus CPC, etc.), more platforms (mobile CPMS are different from OTT for example), the harder the math required to optimize yield and the greater need for tools like AI and machine learning.

As a business person, with experience in other industries with increasingly complex supply chains, selling more products, to more customers, in more places, it seems pretty clear to me that we need new tools for new problems. However, the big gorilla in the room of media is that we continue to try to solve Calculus III problems with an abacus.

That’s right, the unspoken truth is that the majority of the TV and online video media planning is still performed on Microsoft Excel, which was released in 1990. And as any media strategist/planner or ad operations manager knows, extracting information from advertising sales and delivery systems — for both digital video and TV— is a laborious, time-consuming process, and pricing and inventory allocation is often as much an art as it is a science. 

I’ve spoken to planning VPs at major media houses who have told me how this process makes it difficult to even know what inventory is at their disposal (especially across channels), never mind how to best track and optimize it. 

One industry source I spoke to put it as such: “I don’t know if a campaign is getting me closer or further away from our target. “ 

The only constant in media is change. With the simultaneous trends of time spent flattening and device fragmentation, the business of selling ad space will only get more challenging and complex. And what sellers require (and deserve) are more advanced tools and data science to power yield optimization in a dynamic, flexible, and as a result, sustainable way. 

Although my tone may sound a bit fatalistic in this article, I am so excited about the future of our industry. Other industries have experienced the same types of business challenges and use technology and data to not just survive, but to thrive. As an athlete (well, math-lete in this context), I don’t think it is worth getting out of bed to do anything other than go big, or go home. And for me, yield optimization is a big, hairy problem worth getting up and putting my big girl panties on to solve.

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