Ad-driven media organizations are under extreme pressure to increase revenues and maximize the value of their airtime inventory to enable new business opportunities. Now more than ever, it’s important to provide measurable methods that allow our customers to be more intelligent with their multiple types of inventory. This white paper provides some inside-industry information on the evolution of ad management, the history of linear vs. nonlinear delivery methods, and why inventory optimization is the next logical step towards increasing ad revenue. Put your ad caps on. We guarantee you’ll learn at least one new thing in here.

The Bridge Between Linear & Nonlinear

First, a linear/nonlinear primer: In traditional linear viewing, content and advertising is delivered real-time to a region with no option for the viewer to skip ads, receive targeted ads, or choose to not watch them (without leaving the linear channel). With nonlinear viewing, content and advertising can be delivered in either real-time or time-shifted methods (OTT, DVR, etc.) and can allow the viewer to skip the ad, click through to view it, and receive highly targeted ads specific to the viewer’s preferences and habits.


Traditionally, linear TV sells ad space using a forecasting system called, “Gross Rating Point” (GRP), which is a ratings-based formula that projects what kind of an audience a particular piece of content (such as a TV show or sporting event) will draw. Many of the new nonlinear advertising business models use a different system, “Cost Per 1,000 Impressions” (CPM), which is an impressions-based method that measures how many users were actually exposed to the ad. As traditional media companies expand their services to nonlinear viewing, a new “currency” problem emerges: how can a media seller offer an omniplatform campaign with radically different currency models like GRP and CPM? This lack of a standard currency makes it incredibly challenging to sell and manage inventory across all media platforms. Advertisers, like media sellers, want to unify their campaigns and objectives, but without significant reconciliation for these currencies, it is near impossible to make full use of all available seller inventory. In effect, millions of dollars—and billions of impressions—get flushed away every year as a result of not optimizing inventory.

The Next Step in Ad Evolution: Manual > Automated > Optimized

Before the ad industry embraced automated methods of buying and selling, the idea of automation (using software to buy, sell, manage, and optimize ad placements and opportunities) was just as foreign to advertising managers as personal computers were to the average household in the ‘80s. And just like personal computers, automation is something no one can imagine living without these days.


The next step in the industry’s evolution is optimization: the ability to leverage sophisticated algorithms and machine learning to determine the optimal placement of an ad based on campaign objectives. By using optimization automation, a media seller can more accurately deliver the right audience for the right ad at the right time. Optimization tools leverage an efficiency model in which the algorithm looks at past performance of programming, placements, and consumer trends to determine where the most productive yield will result from a placement. In essence, optimization uses a computer to compare and contrast business rules that the media seller sets up to ensure an ad runs in the avail that is best positioned to deliver against campaign objectives.

A scale showing the balance between quality and efficiency

Consider a scenario where ad space is traded against a low-cost, general demographic of adults 25-64 years old versus the higher cost, targeted demographic of left-handed golfers. Without an optimization engine, the media seller may consider the general adults’ category as more financially advantageous, and choose to run an ad appropriate to the wider demographic. But a good optimization engine might determine that this particular ad avail would be better served by running a targeted ad to the left-handed golfers, while saving the more general demographic for another time.

Accordingly, a good optimization engine should:

  • Review all inventory and manage against campaign objectives automatically—not just against availability and/or price;
  • Accurately analyze real-time and historical data to ensure the right inventory opportunity is used against the campaign objectives;
  • Remove the guess-work of an ad’s audience, reach/ frequency capabilities through data-driven forecasting;
  • Provide accurate metrics on demographics using highly advanced CPM statistics;
  • Find every second of underutilized inventory in order to squeeze every ounce of revenue from it;
  • Further automate the process of scheduling commercial and promotional content without adding additional resources or back-office infrastructure.

Most importantly, a good optimization engine should process both currencies by converting GRP / spot objectives to audience / CPM metrics. This enables traditional linear programming to behave like nonlinear— and vice versa; one that is cross-platform functional and capable of optimizing ad inventory for multiple methods of media delivery.

Advertising Utopia – A Tale of Two Business Models

Linear advertising is still a major revenue stream that funds the industry. According to the media research firm, Magna, global ad spend for linear TV in 2017 reached $180 billion. Only an 11 percent difference between the $204 billion spent on digital (nonlinear) advertising. The time for these two platforms to come together with a common method for generating ad revenue is now. But how do we get a traditionally based GRP system of trading to work with a CPM system? With a story of course…

Once upon a time there were two cities—OldTown and NewTown—both of which shipped passengers to the idyllic island of Utopia, where the government paid for successful passenger delivery to specific ports on its island.

OldTown was an established city, with a proven track record for delivering groups of passengers to Utopian ports. But due to historical waterway constraints, OldTown never had a way to truly track how many passengers actually made it to Utopia, let alone specific ports. So OldTown had to get creative with valuing its pricing of passage to Utopia. They based their valuation method on potential delivery instead of actual delivery. Naturally, this meant more focus on a ships’ capacity to carry a set of passengers rather than the actual number of passengers delivered.

Despite these constraints, OldTown’s methods were quite sophisticated at the time—they gave a representative group of people on the ship a method to contact them in order to prove they had arrived, and to notify them of the port they’d arrived at. Based on the responses from the “test group,” OldTown would set or reset its shipping prices for the Utopian government. But as sophisticated as the method was, it was not without its issues. Passengers were not always accurate in their responses, and some never even sent their replies.

Due to the complexity of finding the right kind of “test group” in order to determine a valuation for a given ship’s cargo capacity, Oldtown employed a massive staff of “wizards.” And because of the inherent inaccuracy of human accounting, sometimes the Utopian government would object to the bill Oldtown charged, which meant the wizards had to enter into lengthy debates with the Utopian government in order to prove their numbers were accurate. As a result, more and more wizards had to be hired to accommodate Utopia’s increasing demand for passenger delivery accuracy.

Along came NewTown, a city that radically changed the system of Utopian passenger delivery in two significant ways: method of delivery and accuracy of passengers delivered

For method, NewTown still used ships to deliver their people, but they didn’t sail the ships in just water anymore. Sometimes the ships sailed in the air, or space. NewTown even found a way to construct virtual tunnels on top of the water, making passage easier and less susceptible to the water’s current.

Most importantly, NewTown found a way to accurately assess how many passengers not only made it to Utopia, but also which ports they entered. Rather than asking a test group of passengers to send word of delivery, NewTown simply gave each passenger a ticket, which included all the relevant details Utopia requested. Then NewTown gave Utopia a ticket reader at its various ports, so that when a passenger would pass through the port to Utopia, his or her ticket was automatically read and its passage information returned to NewTown.

NewTown had its own wizards, too. But rather than spending their time debating over whether and where passengers were delivered to Utopia as requested, all the wizards had to do was point to the ticket readers for the facts. Thus, no longer did the wizards need to focus their energies on arguing over the past, they were free to focus on predicting the future. And now that they were getting accurate results from the Utopian ticket readers, the wizards’ predictions became even more accurate and valuable.

When NewTown approached OldTown to forge a partnership, OldTown was at first hesitant of this new way of passenger trading. So OldTown set its best wizards on the project to research all the ways this new method of automation would impact their trade. And indeed, OldTown not only realized that NewTown’s methods were sound, but also they discovered an even better way to make use of extra space on their ships. Using NewTown’s method of delivering and measuring passenger arrival, OldTown devised an algorithm to determine when extra space was available on a given ship, which they then offered as passenger standbys to various ports in Utopia. NewTown was thrilled at OldTown’s findings and the two of them joined hands lovingly. Utopia was happy that they got more passengers on the ship, and OldTown and NewTown were happy that their overall passenger delivery revenue increased.

And they all lived richer ever after.

OldTown………………………………Traditional broadcaster sellers

NewTown…………………………….Multiplatform video service providers

Utopia…………………………………..Successful audience delivery of an ad campaign

The Ship……………………………….Video content (TV show, sports event, etc.)

Passengers…………………………..Target market consumers

The Water/Currents……………Traditional linear broadcast delivery method

Wizards……………………………… Ad sales representatives

By air, by space, over the water……..New nonlinear delivery (Packaging, multi-device, OTT, VoD)

OldTown’s method of valuation……..GRP (Gross Rating Point)

NewTown’s method valuation………..CPM (Cost per 1,000 ad impressions)

Test Group………………………………………….A traditional ratings mechanism, like Nielson

OldTown’s extra space algorithm…..Ad inventory optimization

The Ad Management Tower of Babel... Can’t We All Just Get an Ecosystem?

Video delivery people say tomato; ad management people say orange. Why is there such a disconnect in terminology? Shouldn’t we all just standardize on the same language?

Not necessarily. Language is contextual. The language of delivery focuses on how content is delivered; the language of ad management focuses on how content is monetized. Telling an ad person that her linear TV includes digital delivery might start a fight; telling a delivery person that his front end is really the back end and, well, maybe something more than a fight.

Now that delivery and ad management systems are joining together to create next-generation, cross-platform media ecosystems, knowing some Ad Speak might help.

We Challenge You to GamePlan™

If you’re looking for the only cloud-based application on the market today that uses unparalleled, battletested inventory optimization tools, look no further. GamePlan™ allows the highest level of inventory optimization for the least amount of back-office traffic and sales solution replacement. The only downside of it is… wait, there is no downside. You have literally nothing to lose and millions to gain.

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Interested in learning more?

Read Lessons Learned From The Frontlines of Global Content Monetization to learn why audience-based buying is gaining traction in key global markets and why North America may be making the shift as well. 

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portatif of Steve Reynolds


Steve Reynolds

Steve Reynolds is President of Imagine Communications, a global leader in multiscreen video and ad management solutions that broadcasters, networks, video service providers and enterprises around the world rely on to support their mission-critical operations.

Steve brings 25 years of technology leadership in the video industry to Imagine Communications. He has served as the CTO at Imagine Communications and Harris Broadcast, Senior Vice President of Premises Technology at Comcast, Senior Vice President of Technology at OpenTV, and CTO at Intellocity USA.

Steve earned a MS in Computer Engineering from Widener University and BS in Computer Science from West Chester University. As the Chairman of the AIMS Alliance and a member of SMPTE and SCTE, he has participated in numerous standards-making bodies in the cable and digital video industries. Steve also holds over 40 patents relating to digital video, content security, interactive television and digital devices.