Microsoft Leveraging AI Strength for Ad Business


The Daily Upside has featured perspectives from Arun Kumar, Hero Digital’s EVP of Data and Insight, in an article titled: Microsoft’s AI ad play. Arun Kumar said AI’s impact would be felt in three main areas: content creation, advertising delivery, and effectiveness measurement. Read the full article below.

Microsoft may be leveraging its AI strength to boost its ad business. 

The company is seeking to patent a machine learning model that offers “incrementality estimation” to predict how a piece of sponsored content will perform. The company defines incrementality as how the budget given to an ad impacts its performance. 

Microsoft’s machine learning model is trained to forecast how a piece of content is going to perform based on the budget applied to it, in order to “judge the effectiveness of the budget towards performance.” The outcome of this model could be based on a number of different performance metrics, including the number of clicks, impressions, and in the case of job postings, the number of applications received, the company said.

The company said in its filing that common machine learning models used to estimate ad performance often make the mistake of conflating higher budgets with better performance and, therefore, “suffer from inaccurate recommendations due to inaccuracies in the estimate of how much additional advancement will be accomplished by increasing the budget a certain amount.” 

“More particularly, these machine-learned models rely upon historical information in which biases can appear in the data,” Microsoft said in the filing.

Here’s where Microsoft’s model differs: The model is trained with an approach called “asymmetric budget split,” where it learns from two separate sets of training data: one for a high-budget set and another for a low-budget set. Microsoft claims this method “ensures unbiasedness in the machine-learned model,” as it is not pulling answers from one large data set, therefore not taking into account the performance of high-budget ads to judge low-budget ones (and vice versa). 

Advertising is far from Microsoft’s core business. While the company made $3 billion in ad and search revenue in the most recent quarter, up 3% year over year, its cloud and business productivity offerings make the bulk of its money. Compared to competitors like Meta and Google, which made $28 billion and $61 billion on ads in the latest quarter, respectively, Microsoft’s ad business looks like small potatoes. 

Ads may not necessarily be the company’s forte, but Microsoft subsidiary LinkedIn is showing signs of faster growth, reporting revenues of $3.6 billion, up 8% compared to last year. Taking this into account, it’s no wonder that the patent filing focuses on how its measurements can improve job posting performance, as well as ads.

If the company is seeking to grow its ad practice, implementing AI into its strategy is a no-brainer. Microsoft is a leader in the AI space, and with this tech, the company is essentially applying its strengths to its weaknesses. (Though it may have some competition in this area, too, as Google, Meta and Amazon are also toying with AI-generated ads.) 

Microsoft’s patent also has the potential to negate the need for advertising A/B testing, said Dan Ratner, CEO of Australian ad agency uberbrand. “If AI can help me determine which ads are going to be more effective before I actually publish, and if it could become super reliable, my advertising would be much more effective.” 

While this filing gives a peek into how Microsoft thinks about advertising, it also shows how AI has and will continue to impact the world of digital advertising. Arun Kumar, EVP of data and insight at Hero Digital, said AI’s impact will be felt in three main areas: content creation, advertising delivery, and effectiveness measurement.

“The burning question among people now is — will AI eliminate careers for humans or put creative organizations out of business,” Kumar said in an email. “No, but what it will do is provide better efficiencies by reevaluating and reallocating where humans are spending their time. Instead of spending time on baseline activities, they have now freed up time for other value-ad projects.” 

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