9 Ways Machine Learning Will Aid Marketers in 2017

Machine Learning

60 years ago, the world’s greatest scientists and mathematicians converged on the Dartmouth Summer Research Project on Artificial Intelligence to take part in a massive brainstorming session around artificial intelligence.

Today, businesses are scrambling to pick up where they left off and embrace the promise that artificial intelligence and machine learning offer the human race. The opportunity to mine repetitive human behaviors and predict the outcome has never been such a hot topic.

Luckily, existing and new tools will assist all sizes of companies to meet their goals in 2017. Here’s a list of the best.

Google’s tools such as AdSense, Search and Maps. Assists marketers in reaching active and passive prospects from their desktop, moment of truth or commute.

Facebook’s ‘Add Friend’ and LinkedIn’s “People You May Know”, continuing to facilitate the connections that make both the largest social networks.

Tagxit applies deep learning to improve image classification and categorization to associate names and sentiments with specific images.

Snapchat’s app and goggles will know when you smile and take pictures for you with face detection. Devices that can learn a user’s preferred side, stance and pose can do the work for them.

Facebook’s face recognition auto-tag’s pictures for users, and so can the auto-detect feature in phone’s unlocking system. This will goes a step further with the Internet of Things with startups developing systems that can recognize the faces of vehicle or home owners. The system will only allow recognized faces to enter the vehicle and take countermeasures against any unknown attempts. An additional security layer like this, given its after-sales nature, goes a long way in building trust with customers.

C2’s anti-spam software ensures marketers are only emailing prospects that are interested in a specific category or offer based on previous purchasing behavior within or around a segment. For instance, a baby car seat being offered to segments identified as new moms.

Weather forecast: Machine learning is applied in weather forecasting software to improve the quality of the forecast. Not only does doing so help the tangible supply chain but also real time fixes for online and offline shopping experiences.

iflix: Predicting taste in movies and TV shows. This is reminiscent of Netflix’s CineMatch, through which is has developed entertainment content that met the untapped needs of cross genre enthusiasts that may never have seen their preferences played out – like Abraham Lincoln Vampire Hunter. The same goes for Patari, predicting taste in not just music and genres, but also preferred vocal styles and cross genre’s for curated suggestions.

Digital Publishing: Recommending articles to you based on the type of news consumed on the website. Facebook already does a variation of this based on what users engage with on their timelines. Understanding the cues and triggers that evoke actions allows marketers to curate messages accordingly.

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Usman Khalid

About the Author Usman Khalid

Usman Khalid is the founder & CEO of Centric DXB. He excels in taking the point of view of both our clients and our internal teams - expressing those perspectives, concerns and requirements to the other side. Aspiring clients & partners can reach him on [email protected]