Impact of Machine Learning on Digital Advertising

What is Machine Learning?

Machine learning is an Artificial Intelligence (AI) application that helps the systems to learn and automatically improve the experience through previous experiences without being explicitly programmed. Machine learning focuses on developing the computer programs that can use the data and learn from themselves.

In simple words it can be explained as machine learning is a science that getting the computers act without explicitly getting programmed. It is pervasive today and you may probably use it multiple times a day without knowing it. The primary aim of machine learning is to allow the computers learn automatically without any human assistance.


How is Machine Learning impacting Digital Advertising?

Machine learning has become the buzz word these days. And it is gaining momentum from the traditional analytic models, which are no longer been effective. Advertising industry is growing and becoming more complex day by day. And the world is accessing everything through internet with few clicks on the web. To understand the user experiences earlier, marketers use to analyze the data through traditional analytic models. With the evolution of Machine learning data analyzing is simplified and large sets of data can be analyzed by the algorithms in a simple way.

For example, Human brain recognizes the food, tastes, colors and textures through nourishing the previous experiences. It analyzes the previous experiences and applies the same on the future circumstances. In the same way, Machine learning is an automated process obtained through the reasoning of huge sets of data that is collected before. Due to the rapid advancements in the artificial intelligence, machine learning is playing a vital role in unlocking the true potential of the systems. With the continuous progression of the automation in the marketing and advertising world, machine learning algorithms are increasing the efficiency, performance and ultimately success through large sets of data utilization.

In digital advertising the advertiser should analyze the user behaviour or preferences to reach the target audiences. Machine learning helps in increasing the ROI for the advertisers and revenue for the publishers. Machine learning analyzes the huge sets of data of publishers, advertisers, buyers and ad impressions. It identifies the trends in real time and allows the advertisers to act on the opportunities or issues that are raised by optimizing the click through rates (CTR).

Machine Learning – Advertiser Side:

Advertising can be done to reach the target audiences and improve the sales of the products/services offered by the advertiser. Earlier, advertises used to reach to the people but not the right people (targeted audiences).

Now, the changes in the programmatic advertising is allowing the advertiser to speak directly with the targeted audiences or future customers. With the help of machine learning, programmatic advertising could actually turned very much useful to the advertisers by analyzing and identifying the customers by:

  • Demographics
  • Interests
  • Geography
  • Time of the day
  • Device
  • Weather
  • Sex
  • Age and more

In programmatic advertising buying and selling the ad inventory is an automated process connecting with exchanges, advertisers and publishers. With the help of machine learning it has become easy for real-time bidding (RTB) for the inventory across mobile, video and social channels and now it is even extending to the television.

Artificial Intelligence analyzes the visitor’s behaviour allowing the real time campaign optimizations towards the audiences that are more likely to convert. With the help of machine learning, programmatic advertising platforms are using the real time and third-party data to identify the right audiences for your campaign. Then you can buy the the inventory to show your advertisements reaching right places and audiences who cares about.

Machine Learning – Publisher’s Side:

The old and traditional way of publisher advertising system used to set the prioritization rules to show the ad based on the auctions. Now, with the help of machine learning, the advertisers can predict the value for their every ad impression. The publishers price is dynamically set in the auction using machine learning based on the buyer’s interest and bid price.

With the help of Machine learning, publishers got more control over the ad efficiency and optimizing the publisher revenue. Here are some of the ways discussed below:

Bid Throttling:

The header bidding has become the game changer for the publishers, which led to several challenges on the buying side. SSP’s and DSP’s used to receive much traffic arising in the problem of keeping the infrastructure costs down.

Bid Throttling leverages on Machine Learning ensuring that SSP’s is sending only the relevant traffic that DSP’s is interested. Therefore, increasing in the efficiency of the performance by working together.

In order to keep a tap on the costs incurred on the technology, Machine learning is very much useful to predict the DSP that is likely to win the bid. It can be achieved by analyzing the algorithms on a historical basis identifying the attributes affecting DSP’s win rate. If you can predict that who is more likely to win the bid on an impression, then we can send the impression to those DSP’s with fewer bid requests. The bid throttling results in the decrease of queries per second (QPS) and increases the win rate in addition to increase in the spend per QPS for DSP’s.

AD Quality:

Machine learning is helping the publishers to identify the harmful ads before shown on the publisher’s website. It can scan the ads that comes through our platforms and the machine learning algorithms identify the harmful content or mal-ware, offensive, high band with ad. Then block the ad before it is shown on the publisher side.

With the help of machine learning algorithms, we can discover the ad sent is something that publisher wont like, which goes a long way in protecting the brand safety of the advertiser.

Dynamic Floors:

Platform data and machine learning is used to predict how advertisers will value the ad impression. Also, working with publisher partners to set the price floor in the auction that is submitted to the bidders.

The publisher is able to reclaim the control over the price by protecting the ad inventory that can accumulate more information about the buyer’s willingness to pay. The noticeable increase is seen in the monetization for the publishers when compared with the old traditional auction systems.

The increased efficiency of the Digital advertising by adopting Artificial Intelligence / Machine Learning is not only helping us to protect the advertiser dollars but also to understand the customers. Machine learning is ultimately bringing a big change in the field of Digital Advertising by analyzing, optimizing and interacting with the customers.