AI News, The Future of Advertising and Machine Learning – Audience Targeting, Reach, and More

The Future of Advertising and Machine Learning – Audience Targeting, Reach, and More

Episode Summary: Facebook and Google’s advertising complex is founded on machine learning, allowing people to self-serve their data needs across a broad audience.

Patchava explains how machine learning plays a role in appropriately matching advertising requests to the right audience at scale,  whether on mobile, desktop or different devices and media.

Patchava paints a robust picture of what this technology will look like moving forward and how it will change the game for marketers and advertisers, especially with the emphasis on data and machine learning.

His project experience spans automotive, financial services, manufacturing, IT services and technology, energy and materials, pharmaceuticals and health, public infrastructure and development, and consumer goods.

research article.) (3:00) Can you give an idea in nontechnical terms of how machine learning plays a role in the bulk of operations of your business?

 To help advertisers and publishers alike achieve their objectives in a high-frequency marketplace, advertising technology (adtech) companies such as InMobi have to match the right advertiser with the right sites for both to achieve their KPIs (key performance indicators).

Because ad requests often total more than 10 billion daily, quick decision making is imperative, a skill brought by a machine learning system.

Adtech companies are committed to finding the best equation and bringing them together in an effective way to ensure that both advertiser and publisher are successful.

(6:42) To paint a clearer picture of how the process goes, let’s take an example of an ad with a call to action (CTA) that targets a particular audience in a specific location.

Firstly, the ad network must determine what it already knows about this profile and the likelihood that the audience will complete the CTA based on the historical data gathered from the billions of requests every day.

For new categories or new types of applications that do not have the data, adtech companies use a machine learning system that allows them to learn, take feedback, and further invest or divest, all happening at a high level of data and speed for decisions to happen.

In a world where that value chain—from marketing to the last mile of your advertising dollar—is integrated and the data and feedback is flowing well with the help of speed and scale of automation, you have a world where these two functions become one.

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