Looking for ways to optimise the management of your YouTube Ad campaigns? Sick of wasted ad spend on toxic YouTube channels that slip through your filters? Feel like you’re sinking excessive hours into the curation of your Ad impressions? If that’s the case, then read on! N-gram analysis may be the solution you are looking for.
YouTube Ad campaigns and the toxic channel problem
Youtube is more than just a video-sharing website. Considered separately from the Google ecosystem to which it belongs, it is the world’s second largest search engine
(or, depending on how you crunch the numbers, the third largest
). It won’t come as news to any marketing professional that this represents a huge opportunity for ad campaigns.
One of the big problems encountered in YouTube campaigns above a certain size is the presence of toxic channels that are entirely irrelevant to you or your client’s goals. Google Ads allows you to follow in detail the channels where your ads have appeared, but how to efficiently manage an Ads account with multiple campaigns running ads that simultaneously appear on thousands of channels?
Viewed individually, such toxic YouTube channels are easy to shrug off as part of your campaign’s error margin. After all, how bad can it be to have a few misdirected impressions here and there? You have bigger fish to fry—namely, the YouTube channels getting the bulk of the impressions which are the real objective of your campaigns. However, if you sum up all of these irrelevant channels that you have been ignoring due to prioritization of your resources, you may find it adds up to more wasted spend than you imagine.
Solving the toxic channel problem at scale: n-grams to the rescue
Even if you identify the toxic YouTube channel problem, the prospect of fixing it can be daunting. Exclusion of irrelevant and undesired channels can only be done at the campaign level. This means that, unless you can figure out a better approach, you would need to manually remove each and every toxic channel independently.
Let’s put this problem in context at a relatively modest scale. Imagine, for example, running five YouTube campaigns across ten different Google Ads accounts. This means repeating the same tedious and costly operation fifty times. The time wasted on such measures quickly gets out of hand. Unchecked, the spending waste is likely much higher than you would like.
We have a solution: use an n-gram analysis!
An n-gram is an aggregation or collection of symbols or words
often used in natural language processing, it enables machine learning algorithms to understand, and/or predict the probability of, words (or grams), based on the context they are used in. Replace the n value with the number of words in a given grouping that you want to identify, and voilà
In the context of a YouTube ad campaign, performing an n-gram analysis will allow you to reverse-engineer your data to further optimise your impressions, use of keywords, and ad spending. In the next and final part of this article, we will take a look at a practical example of how to run an n-gram analysis on a fictitious ad campaign.