Machine Learning In Advertising

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The hype about machine learning in the advertising and media industry exists in heaps. However, once you move past the buzz, there are a lot of practical applications for this technology. Machine learning is earning momentum over traditional analytics models, which are no longer sufficient. They will continue to lag as disruptive pushes the industry to evolve.

Machine Learning is a broad area with a number of approaches, such as, supervised and unsupervised algorithms. Supervised learning algorithms create a mathematical model of a set of data that comprises both the inputs as well as the desired outputs. In other words, we tell a machine what we want to happen and the machine gradually learns to produce that outcome.

Unsupervised learning algorithms take a set of data that contains only inputs, and discover structure in the data, like grouping or clustering of data points. This is beneficial for finding developing behaviors that would be unlikely to predict ahead of time.

So far, machine learning has been functional at enhancing ad delivery without the requirement for human surveying. It would be inefficient for digital advertising platforms to accept ads on the basis of who bid the most. This is especially true because of two reasons; advertisers are not getting the results they want. And secondly, users still hate the ads they have to see.

Machine learning allows such companies to swiftly optimize their campaigns in the context of inventory demand, bidding, user response, and hundreds of other variables. As a result, there will be more relevant ads for users as well as turnkey campaigns for advertisers.

However, what we can’t know from this is why – why did a video result in 10x sales as compared to other videos? What worked? What didn’t?

Driving Results Through Video Content

Supervised machine learning tools offer the expertise to dissect and analyze every frame of a video for content. This includes – specific objects, action, humor, locations, etc. When we blend this with performance and retention data, it becomes easier to understand not only what content resonates, but what aspect of that content resonates. This allows optimizing assets and preemptive creation of videos that drive results.

Large tech companies that have to process millions of hours of video, already analyze videos frame by frame to determine the content and what is resonating with the users. However, this technology is not a popular choice among content creators. Mainly because of the following reasons:

It is a relatively new technology.
Most off-the-shelf technology does not target advertisers.
A healthy solution requires many machine learning libraries and tools.
Generally, creative is delivered to media teams as a one-way street, with optimization serving different variants to different audiences.
Most advertisers don’t realize it’s possible in the first place.

Employing Machine Learning Tools

TONIK+, the first machine learning video software, is solving these problems by creating a number of machine learning tools. These tools prepare videos within individual scenes and label all of those scenes with appropriate labels. Once this is done, the performance data can be overlayed and brands can be given their first intra-video intelligence report. This report will get answers to:

Which scenes resonate best across all users.
Details of scene resonance by the audience along with their demographics (age, location, gender, etc).
Performance by interest, such as fans of different film genres or sports.
Efficiency across platforms, devices, as well as video duration.

Even though this information is amazingly valuable on its own, there are tools that can automatically create an audience & platform-specific creative on the basis of these insights. Videos fit the appropriate durations as well as aspect ratios. They are re-published, which usually results in average view lengths being 20-50% greater than the original videos. As a result, clients get nearly double the seconds of video viewed per advertising dollar spent.

Personalized Video Content

The applications of machine learning technology are boundless. Unsupervised machine learnings will be crucial in optimizing platforms, creating video variants, as well as classifying audiences on a massive scale. It will also serve to preemptively optimize and align content to audiences before running a single impression.

Media plans will also be developed in real-time, with rising behaviors pointing to real-time changes in the content we distribute, our budget allocations, as well as the ad products we leverage.

If executed perfectly, machine learning will essentially drive revenues, boost growth for businesses, enhance your brand, as well as improve customer satisfaction. In fact, whatever we are predicting for machine learning today, might be only picking at the surface for its potential to transform the advertising business altogether.

FAQs:

1. What is Machine Learning?

Machine Learning is a broad area with a number of approaches, such as, supervised and unsupervised algorithms. Supervised learning algorithms create a mathematical model of a set of data that comprises both the inputs as well as the desired outputs. Unsupervised learning algorithms take a set of data that contains only inputs, and discover structure in the data, like grouping or clustering of data points.

2. What is the role of Machine Learning in advertising?

3. How applicable is Machine Learning in creating video content?

4. Why is Machine Learning not preferred by content creators?

It is a relatively new technology.
Most off-the-shelf technology does not target advertisers.
A healthy solution requires many machine learning libraries and tools.
Generally, creative is delivered to media teams as a one-way street, with optimization serving different variants to different audiences.
Most advertisers don’t realize it’s possible in the first place.

Machine Learning In Advertising | Transforming The Video Ad Space