Ways that Artificial Intelligence Has Sparked Marketing and Sales Transformation

What is programmatic advertising?

Currently, people are experiencing a situation wherein websites, applications and digital properties are seen across channels every day. Separate platforms for advertising in addition to selecting the best place to put up ads are necessary. Additionally, selecting different mediums to advertise and the best places to put up ads, which is ideally tough can be done by programmatic advertising.

It has been growing its inventory and database such that any format and any channel can be evaluated programmatically today, including mobile, desktop, tablets and digital outdoor plus TV. In this form of ad.vertising, targeting tactics are used, wherein segmenting the TG with data, ensuring advertisers only pay for ads delivered to the right people, place and time. Here algorithms ensure ad buying, placement and optimization procedure becomes efficient, thereby eliminating routine tasks and time. 

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At the core of every programmatic ad, the platform is a proprietary machine-learning algorithm that analyzes campaign inputs and user behavior so that real-time campaign optimization can be done and the TG that’s most likely to convert is identified. Various types of data are used to enhance the probability of the success of the campaign.

Therefore it helps buyers and sellers make data-driven decisions and eliminates guesswork from the ad purchasing process. It ensures efficiency by using machine learning to optimize campaign success. 

Thirdly, it's budget-friendly and uses machine learning to maximize campaign success. It also puts together campaign management in one system to cross devices allowing singular success. Lastly, it enables easy access to ad inventories, wherein advertisers can access optimal inventory across exchanges simultaneously and algorithms do the bidding.

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Therefore summarizing the above, marketers can efficiently funnel messages across channels and optimize the share of attention, to reach desired markets with programmatic advertising.

The future of marketing: Predicting customer behavior with AI

In the current day and age, marketers are increasingly resorting to understanding customer behavior even before themselves. This is an ongoing activity by marketers and AI makes this possible through deep learning. So what’s deep learning all about? It’s a machine learning methodology that uses “layered” or “deep” neural networks, just like biological brains, to learn skills and solve complex problems quicker than people themselves can. It aids robots to manage “human tasks” like perceiving objects, recognizing voices and translating languages.

It needs a large data set and lots of computational power to function effectively. It provides a method of training AI to predict outputs. If deep learning is empowered with these elements, it can predict human behavior precisely. Researchers in MIT’s Computer Science and AI lab, trained deep learning systems to predict if characters in scenes from shows like “The Office” would hug, kiss and shake hands. After watching more than 600 hours of youtube videos, the system could predict actions 43% of the time. But existing algorithms could only do that 36% of the time.

Scientists at Cornell University and Stanford University conceptualized a “Brains 4 cars” system, which includes cameras, sensors, and wearable devices, that monitor a driver's boy language and traffic around the vehicle. The system sounds and alert wh the driven appears to be driving fast and have an accident. The algorithm can predict driver behavior in 3.5 seconds in advance.

Uncovering New Customers — and Predicting Consumer Behavior Months in Advance

Finding patterns insides patterns is something that deep learning is capable of doing, to enable a business to understand customers' needs. It enables the hyper-personalization of marketing messages. Researchers at Renmin University of China found that information on the consumer's hobbies and work situations, when used as inputs for deep learning methods, enables the prediction of the purchase intentions and preferences of various groups. 

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It’s a known fact that predicting a customer's needs is a jackpot for any marketer. If armed with well trained AI, marketers can rely more on data-driven, accurate results and less on guesswork. Marketers must also understand value creation. Reports from renowned consulting firms reveal that its a nascent concept among companies. And with a majority of companies fast-tracking AI applications, it’s expected to have gains, productivity, revenue, and shareholding value in 2 years. Research also states that a majority of businesses will now apply advanced AI to every function, including marketing and customer service.  

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 Deep learning r technology is all set to grow

Companies today are using advanced AI to deliver personalized experiences to clients and in turn predicting their needs. For eg., Netflix has an AI-driven focus on personalization, which influences about 80% of the content that subscribers watch. These algorithms are a lifeline and save the company a whopping $1 billion annually retention. Facebook is at the forefront of deep learning and has conceptualized powerful applications, including face verification app called DeepFace. It recognizes people in photographs with precision. Now it's working to develop a unified deep learning platform framework that’s accessible to the developer community. Therefore deep learning is still a nascent concept but undoubtedly picks up momentum.