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Artificial Intelligence Addresses Ineffective Ad Targeting and Engagement

The advertising industry faces major challenges.

One of them is the industry’s widespread difficulty targeting ads effectively. Too many ads are seen or heard by people who are not interested in the products or services being advertised. Frequently, advertisers don’t know whom the correct people are to target or how to reach them.

A second challenge is how to deliver ads that engage consumers and convey to them the experience of a product or service. Many ads simply do not deliver personalized and enjoyable experiences often enough.

These problems result in ineffective advertising programs on TV and other video platforms that don’t generate sufficient returns on advertising investments and are difficult to quantify. Far too much ad spending is being wasted.

To overcome these difficulties, the industry has turned to artificial intelligence to better understand the consumer, enhance user advertising experiences, extend the reach and effectiveness of ads, and generate more advertising revenues.

Artificial intelligence and machine learning

Artificial intelligence enables machines to sense, comprehend, and act, as well as learn on their own. In the past few years the media industry has begun to leverage its benefits for advertising. By extending the capabilities of what humans or machines can do on their own, artificial intelligence may become the advertising industry’s most disruptive emerging technology. The technology can improve the efficiency and effectiveness of advertising.

An important type of artificial intelligence, known as machine learning, will be especially significant in improving ad targeting. Using analytics, machine learning extracts insights from data to execute different predictive tasks automatically, which can streamline processes and operations, increase revenues, and provide valuable insights that would otherwise remain undiscovered.

Marketers can leverage the latest machine learning technologies and the vast quantities of digital data the technologies can process to accurately predict what and how to sell and at what price. Marketers can also leverage the speed and accuracy of the machines’ abilities to uncover insights that lead to smarter advertising decisions. A marketing message to millions of consumers could be automatically personalized repeatedly in real-time. Machine learning makes it far more likely for a consumer to receive the right ad at the right time on the right device.

Machine learning can also forecast what content a TV broadcaster should invest in, to whom the company should show it and when, and what return that content investment will likely generate.

Early stages

The advertising industry is in the early stages in its use of machine learning. But because the technology is now ready for prime time, expect it to be more broadly used as companies recognize its potential to improve key aspects of digital advertising.

The M6 Group, which owns major TV channels and is one of the largest TV networks in France, is benefiting from machine learning. The company built a big data platform that includes machine learning, predictive modeling, and data visualization to find new ways to generate revenues from consumer data and extend the reach and effectiveness of ads. The technology helps M6 use data about consumers already targeted for a product ad and, based on that, make easier and faster predictions about which other consumers will most likely be interested in the same ad. These predictions help extend the reach and effectiveness of ads.

Machine learning can help media companies more precisely forecast a wide range of variables, including revenue, traffic, engagement, customer profiles, price, demand and inventory availability. These companies can create an appropriate data-driven price for every campaign based on the true value of inventories and customers. This innovative technology also can empower such companies to control pricing rules and discounts that the sales force can apply for each client during sales negotiations.

To leverage machine learning, media companies should consider focusing on analytics and data and take the following steps:

  • Define the value of the data and analytics. Media companies need to be clear on the reason they are using the analytics—the problem they are trying to solve and the desired outcome—such as to increase or forecast revenues, and determine returns on their advertising content investments.
  • Take an inventory of the data. Media companies need to manage their data and decide which is useful and clean, and whether it resides in one centralized place.
  • Have a plan for analytics. Experimentation and “failing fast” are encouraged, but media companies also need to determine what to implement first and then measure it.

Accenture has released a report about these advertising technologies that can be accessed at the following link: Advanced Advertising Technologies: Igniting Growth.

Matthew Gay leads Accenture’s advertising solutions business and can be reached at