Machine Learning and AI

While machine learning and AI has been around for a while, recent improvements in the speed of computing has made it feasible for the general industry to us. Machine learning is a form of “deep learning,” where machines possess the ability to enhance their performance and decision-making skills without human intervention.

AI and machine learning are gaining increasing popularity due to their innovative functionalities such as predictive analysis, trend analysis, marketing automation, voice and facial recognition. In 2017, 38% of enterprises were using AI, and according to Narrative Science’s survey, the number is set to rise to 62% in 2018.

Why use machine learning and AI?

The digital era has led to inundation of data generated from social channels, devices, sensors, apps, etc. However, the sheer size of the data can make it impossible to mine to acquire useful knowledge.

AI and machine learning can help improve and drive overall customer service by not only automating tasks but, also analyzing data and helping teams make relevant decisions based on collected data and human interaction. Here are some ways that it could help you.

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Product recommendation

Machine learning is routinely used to provide users with relevant information about their pursuits in e-commerce apps, video streaming channels, social media platforms, and so on. Some examples of this might be EPI server or Sitecore. The platform is then able to make automatic recommendations based on the patterns that visitors fall into, making their experience more relevant. Similarly, Netflix analyzes data generated by three primary sources; your preference list, what you watch over time, and the most trending videos. The recommendation engine then predicts what you are most likely to view and prompts you about it. Similar features are seen on Amazon, for example.

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Content optimization

Machine learning and social media algorithms analyze each user’s engagement and general activity towards your content to determine what is most attractive to them. Social apps filter and optimize the user news feeds with contents most likely to evoke a reaction and generate a response. Facebook and Twitter are examples of such use of machine learning.

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Marketing campaigns

Machine learning can assess data and gather additional insights on the users’ behavior and engagement preferences. Marketers can use machine learning for better customer categorization. It works by dividing customers into smaller segments according to their similar preferences and behaviors. This is especially useful in large content management systems.

Due to this, 80% of marketing executives believe that AI will have a revolutionary impact on marketing in 2020. In addition, a survey revealed that 60% of CMOs expect AI to have a more significant influence on marketing as compared to what social media ever had.

At High Score Labs, we like to help our partners globally automate their business operations, improve performance, increase the value of their brand online and deliver an outstanding user experience by building AI-based enterprise software, ML web applications, mobile apps and more.

Learn more about how one tool can help you take advantage of such technology now!

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