5 Machine Learning Trends to Watch Out for in 2018 If You Are a Mobile Advertiser

By Diksha Sahni | February 1st, 2018

Over the last few years, machine learning has been a “force majeure,” especially for the ad tech industry. For mobile advertisers particularly, machine learning is changing the way ad tech is done by helping them to drive campaigns more efficiently and cost-effectively through Real-Time Bidding (RTB), lookalike targeting and user data enhancement.

Where do the next set of trends lie? Let’s take a look at the five trends in 2018 that will guide the application of machine learning in mobile advertising:

Machine Learning As a Service

Tech giants like IBM, Google, Amazon and Microsoft are increasingly focusing on innovations that bring machine learning technologies at the forefront. These tech giants are already offering machine learning models as a service with solutions that reduce the time for marketers. Cloud Vision API, Cloud Natural Language, and Azure Machine Learning Studio are few of the popular cloud MLaaS models that allow advertisers to tailor their offerings to reach the customers with greater efficiency. Other areas of application are enhancements in employing deep learning in CPA and fraud prevention algorithms. Offerings in this space are deep learning platforms such as TensorFlow, Apache MXNet, and Microsoft Cognitive Toolkit. With the field of machine learning picking up, the industry is likely to see the competition rise up, and with that, the innovations available to the marketers.

Chatbots and Conversational Commerce

The advancements in the field of machine learning have made chatbot interfaces more natural and convenient. Increasingly, we are seeing the adoption of a chat interface, away from the traditional tap interface of the apps. App users are now able to hail a cab and make hotel bookings and payments using chatbots. The digital assistants can now not only understand our commands in our natural conversational languages, but also make smart choices for us and complete orders. This field of conversational commerce is full of potential for mCommerce players. Conversational marketing will not just be limited to a voice command, but also studies suggest that it will enable people and machines to use multiple modalities such as sight and sound or sensors, to create a comprehensive conversational experience.

Blockchain and the Promise of Transparency

Blockchain, until now a favorite child of the financial sector has arrived in ad tech and how! The newest buzzword around the block, Blockchain is being hailed as the messiah to solve advertising’s two big problems of fraud and intransparency. By adopting a secure digital ledger of blockchain for transactions, it might be able to finally force stakeholders in the ad tech ecosystem into being transparent about the ad-buying and delivery process, ensuring low costs and ultimately improving the consumer experience of advertising.

Boost in Reinforcement Learning

Reinforcement learning is an area of machine learning that works on allowing machines to automatically determine the ideal behavior within a specific context to maximize the end goal or performance. While this field has been around for many years, recently there has been a big boost due to the advances in deep learning. Advertisers can already begin to see the multifarious advantages of reinforcement learning via the multi-armed bandit approach for creative optimization. When running any campaign, it is important for advertisers to gauge early on which creative variations are outperforming others in order to optimize and shift budget towards the better-performing variations to increase ROI. A more efficient A/B testing process can thereby guide advertisers to maximize their performance.

Robots Mimicking Human Behavior

With the recent advances in computer vision and deep learning it has become economically feasible to train machines to solve tasks which until then have been conceived to be possible to solve only by human interaction. Examples are recognizing objects such as street signs or handwritten digits in images. Posing such tasks to the user is an important way for developers to distinguish between machines and human interactions with their websites’ content. The problem extends well into online advertisement and is coined under the term non-human traffic. Ensuring human viewability is one of the advertisers main concerns since they are spending a lot of money on non-human traffic otherwise. The weakening of these barriers to robots has lead to an arms race where industry players and service providers such as MOAT analytics are trying to come up with new ways to authenticate human behavior such as tracking the mouse movement. However it stands to reason that a machine can be trained to mimic that kind of behavior too. Here, new ad formats such as playables could be one solution to the problem. The machine learning community has demonstrated that machines can be trained and learn to play such games. However it is likely to be less feasible from an economic point of view compared to other creative formats such as banners and easier to detect unusual patterns during the game.

AppLift's Uzi Blum and Jan Ove Erichsen contributed to this article.

Diksha Sahni
Diksha is a Content Marketing Manager at AppLift and is based out of our Bangalore office. When she is not behind her computer writing, you can find her binge watching her favorite movies, finding her happy place at a dance studio, and checking off places on her bucket list.

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