Planning Video Ad Inventory: Why Frequency Capping is Crucial and What Tools to Use
Video is one of the most profitable mediums for ad monetization. It’s also one of the trickiest to utilize fully for a publisher.
First, the market for video inventory is still undergoing teething troubles of fragmentation and lack of standards. Second, there are some aspects of video being different from display ads that affect ad campaign delivery. Luckily, media planning can compensate for both issues.
Here are a few things worth paying attention to:
There’s no “video blindness.”
Unlike a static picture, video always catches the eye. Seems great for viewability, doesn’t it? Not when you watch the same ad several times a day, for weeks, even if it’s the most entertaining and engaging one.
In the fragmented market, especially in OTT, tech partners may struggle to scale the audience, and publishers often end up rotating the same five creatives to the same user over and over again. Don’t want to annoy the user? Make sure there are enough unique users and set frequency capping.
Video ads are all about the view-through rate.
This point stems from the previous one. Advertisers intend their video ads to be watched, not just glanced over. And overexposure is the first reason that pushes the user to skip the video if there’s an option.
The view-through rate number directly affects ad revenue that the publisher gets. So not only is frequency capping necessary to get on the right foot with the user; it also drastically affects the campaign’s effectiveness and whether the publisher can establish a good rapport with the buyer.
Managing cross-device video ad campaigns is painful.
Video ads can tell a story on a level unattainable for display ads. They also fit into all screens and channels, from mobile in-app to connected TV. This is why video is indispensable for cohesive and sequential marketing campaigns that follow the user between different devices to engage differently on each of them.
Such cross-device campaigns heavily depend on frequency capping, usually tied to unique users. However, CTV, the main driver of video ad spend growth, is measured in households. So, the new challenge arises to account for cross-device and link households and unique users.
Media planning tools aren’t equal.
Here’s the main pain point – publishers can set cappings and device types all they want, but no algorithms will deliver the campaign as intended if there’s not enough available inventory to begin with.
Video leaves few options to increase available inventory, e.g., by adding banner space to the webpage as one can do with display ads. It’s also not enough to serve a video ad X times to call a campaign successful. Not accounting for frequency capping, unique users, and device types, the publisher won’t cover enough audience, will frustrate users with repetitive ads, and won’t even meet view-through rate thresholds half the time. They need to assess availability before launching a campaign.
Overall, there are three means to plan inventory (we’ve outlined them in more detail in this article):
- manual calculations via Excel,
- free ad server forecasting tools,
- advanced forecasting by dedicated platforms.
Excel and ad server forecasting are easily available. However, neither frequency capping, unique users, or devices, let alone their overlapping, can be calculated manually. And the quality of ad server forecasts for these targeting parameters, if ever supported, is subpar.
On the contrary, it’s a piece of cake for dedicated media planning platforms.
To plan video inventory properly, you need advanced forecasting tools.
What do dedicated forecasting platforms do that ad ops and ad servers cannot? Let’s look at an example our team knows the best – Burt Forecasting.
Burt Forecasting is an AI-powered platform. It automatically aggregates a publisher’s 1st and 3rd party data and the log files of the ad servers they use, which already sets it apart from human managers with spreadsheets and ad servers’ simple reports.
Our algorithms get the fullest, safest, and most precise anonymized data to learn about two major things:
- ad server delivery logic – what ads end up being served and what affects the pacing of campaign delivery, and
- users’ behavior models – what pages they visit, what creatives they see, what devices they use, etc.
Burt Forecasting quickly and accurately simulates the behavior of the advertising network when launching a new campaign. The more overlapping parameters the campaign has, the starker the difference between the quality of the forecast done by an ad server and Burt Forecasting.
Given the nature of video, correctly planning available inventory – accounting for narrow segments of unique users and frequency capping – is an absolute must for a successful campaign. And with every successful campaign, the publisher gets more incremental revenue, builds credibility, and increases their video inventory value even further.
