How Machine Learning is Being Used in Different Areas of Marketing
By Data-Core Systems | 12/11/2018

The introduction of Artificial Intelligence (AI) tools has significantly changed the digital landscape. Marketers worldwide are now leveraging the power of data through machine learning to garner valuable customer insights, streamline marketing processes, and improve their overall marketing strategy.  
 
As per an Econsultancy and MediaMath survey of 400 digital advertising professionals worldwide, 47% of advertisers worldwide use AI for audience targeting. The other areas of digital ad tactics advertisers leverage with AI include audience segmentation, dynamic creative, campaign planning and modelling, media spend optimization, and personalized offers 
 
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Here, we have handpicked a list of examples of how machine learning/AI is being implemented in various areas of marketing: 
 
1) Google - Search:
 
One of the newest machine learning-backed ad formats offered by Google is responsive search ads. Although still in beta, responsive search ad is designed to automate ad creative optimization. It can automatically adjust to match the search terms of potential customers, making your ad campaigns more relevant and effective.  
 
2) Google - Advertising, programmatic advertising:
 
Maximize lift is another latest machine learning-powered offering by Google. It is a bidding strategy designed to automatically adjust ad bids of brands’ YouTube videos. It helps brands boost their KPIs (key performance indicators).
 
3) Bing - Analytics:
 
The new series of insights from Bing Ads, including competition tab, performance insights and location targeting recommendation are powered by machine learning. They are designed to help advertisers get better insights about ad campaign performances which can further help them make improvements. 
 
4) Facebook - Social media:
 
Facebook has introduced a new deep learning-based text understanding engine, DeepText which can understand textual content of posts. Unlike the traditional text-understanding models that use references such as dictionaries and grammar books, DeepText doesn’t need a human-compiled reference and instead uses character-based approach to extract meaning out of words. 
 
The text understanding engine can improve Facebook experience for users by better understanding posts, text messages, and search intent. 
 
5) Intercom - Chatbot:
 
AI-powered chatbots are one of the evolving tools marketers are heavily capitalizing on. US-based software company Intercom has made it easier for businesses to automate interaction with customers using their recently launched Custom Bots.
 
Custom Bots is designed to eliminate the hassles of coding when automating customer interactions. Businesses can also use the chatbot to handover conversations/complex issues to human customer service agents.  
 
6) Netflix - Content:
 
When it comes to personalizing content recommendations, Netflix wins the game. How? According to Netflix Machine Learning Director Tony Jebara, Netflix tracks user behaviors such as content preferences, content-watching behavior, and feedback for watched content to fetch more data and extract insights from them using machine learning. 
 
7) Wix - Web design/development:
 
AI can be implemented in website design/development as well, and Wix has proved this by launching a new service called ADI (artificial design intelligence). The idea behind Wix ADI is to help users easily create websites by learning about their preferences.  The service also includes an option for users to manually customize websites. Watch this video to learn more how Wix ADI works.   
 
Leverage machine learning to improve your business processes/marketing strategy with Data-Core Systems
 
Regardless of your business model, you can implement machine learning to enhance and streamline processes. We, at Data-Core Systems, can help you achieve your business goals by working with you to create the right machine learning model/system for you.   
 

 

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