Everyone Should be Using Machine Learning – Not Just Ad Tech Professionals


Kai HennigesIn many respects our industry is only just starting to come to terms with data-driven advertising and programmatic technologies, all of which seemed daunting to people at first. As we move into the ‘AI Age’, we’re seeing a similar amount of industry apprehension about machine learning and the impact it’s going to have on our industry. But Kai Henniges, CEO and founder at Video Intelligence, believes we should be embracing AI and viewing it as something we all use. 

Machine learning is often thought of as technology best left to the product team, or something that is beyond the grasp of small to medium sized businesses. But in the advertising world we’re at an interesting turning point where machine learning tech is becoming easy to access and apply in a marketing setting for all businesses.

Many of us will already see a remarkable amount of AI employed (as ML) in our daily tasks. Social sharing platforms which learn and optimize the best time to tweet for example, or email schedulers which can run A-B tests for you and optimize results. These are simple mechanised learning processes, and we take them for granted.

Machines are brilliant at building scale; they can test endless parameters in astonishing time and optimise tasks such as media buying, but these optimisations should be based on human set parameters, individual for every campaign.

Setting these parameters is an area where many ad tech companies have seen great results. By building algorithms that learn about user behaviour, buying ad space on behalf of clients can be automated and made even more profitable. Teams are then able to manage much more complex trading than ever before, testing more parameters and empowering ad-ops to become one of the fastest improving areas in our sector.

These improvements are great for the adtech industry’s consistent improvement, but smaller, less tech-savvy companies often perceive machine learning as a technology beyond their capabilities or resources.

But there are a number of brilliant free tools available that mean that digitally literate marketing teams of all sizes (and budgets) can use the advances in machine learning to improve their audience understanding, contextual accuracy and overall efficiency.

Machine Learning for the Masses

Google has a suite of API’s which are available for anyone to use. Their Vision API ‘image content analysis’ platform is particularly powerful – and a lot of fun to use. Drop any image onto it and it will run an analysis – based on billions of data points which it has been fed before.

Google’s Cloud Video intelligence API does much the same. It will tell you with a displayed degree of accuracy what is in the video, colour and content wise, and the likelihood that contains unsafe content.

We have built our own brand safety tool which uses this to check websites for brand safety. But the applications are limitless, and is sure to prove to be the basis for many new products.

Another useful AI tool is IBM’s Watson, perhaps the best-known AI technology on the market. Those looking to use machine learning to improve their targeting without enlisting the services of an adtech agency can also use this with relative ease. Watson’s Natural Language Understanding (NLP) function is a world-class tool that can analyse content, relationship and sentiment. Applying a tool like Watson to niche, specific ad topics can yield incredible results, and you’ll see analysis improve before your eyes.

It may seem like a daunting arena, but by using the three tools above, which involve a variable yet achievable level of technical knowledge, anyone can take advantage of this fast-developing technology to improve their advertising output.

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