How is artificial intelligence used in analytics?

Analytics powers your marketing program and business intelligence efforts, but how much value are you really getting from your data? Artificial intelligence can help. AI is a collection of technologies that excel at extracting information and patterns from large data sets and then making predictions based on that information. This includes your analytics data from places like Google Analytics, automation platforms, content management systems, CRM and more. In fact, AI exists today and it can help you get much more value from the data you already have, unify that data, and actually use that data to make predictions about customer behavior. That sounds good, but how do you start? This article is intended to get you started. At the Marketing AI Institute, we spent years researching and applying AI. Since 2016 we have published more than 400 articles on this topic. And we’ve posted stories of 50+ AI providers totaling over $ 1 billion. We also track more than 1,500 artificial intelligence marketing and sales companies with total funding of more than $ 6.2 billion. This article builds on that experience to demystify AI. And it will give you ideas on how to use AI in analysis and give you some tools to explore further.

What is artificial intelligence?

Ask 10 different experts what artificial intelligence is and you will get 10 different answers. A good definition comes from Demis Hassabis, CEO of DeepMind, an artificial intelligence company bought by Google. Hassabis calls artificial intelligence the “science of making machines intelligent”. Today we can teach machines to be like people, we can give them the ability to see, hear, speak, write and move. Your smartphone has tons of AI-powered features, including facial recognition that uses your face to unlock your phone (AI that sees) They also include voice assistants (AI that listens and speaks in natural language). predictive text (AI writing in natural language). Other types of artificial intelligence systems even give machines the ability to move using computer vision (AI that visually interprets the world) as seen in autonomous cars. Your favorite services like Amazon and Netflix use AI to provide product recommendations. And email clients like Gmail even use artificial intelligence to automatically compose parts of email for you. In fact, you probably use artificial intelligence every day, no matter where you work or what you do. “Machine Learning” offers the most impressive capabilities of an artificial intelligence platform.Machine learning is a type of AI that identifies patterns based on large structured and unstructured data sets. The machine uses these patterns to make predictions and then uses more and more data to improve those predictions over time. The result? Technology powered by machine learning models improves over time, often without human involvement. And “deep learning” is the most advanced type of machine learning in which neural networks are structured to mimic the human brain. This is all very different from conventional software. A typical non-AI system, like your accounting software, relies on human input. The system is coded with rules made by humans. Then follow these rules carefully to take care of your taxes. The system only gets better when human programmers get better. The. But tools powered by a machine learning algorithm (or many of them) can improve on their own. That improvement comes from a machine evaluating its own performance and new data. For example, there is an artificial intelligence tool that writes email subject lines using Natural Language Generation (NLG) and Natural Language Processing (NLP). The tool’s artificial intelligence model uses training from people (examples of a company’s marketing copy) to learn and then the tool writes its own email subject lines. The split test is carried out, then the machine learns independently what needs to be improved based on the results.Over time, the machine gets better and better with little human intervention and possibly opens up unlimited potential for performance. Now imagine that this power is applied to any marketing technology that uses data analysis. In fact, AI can make everything from ads to analytics to content smarter.

How is AI used in analytics?

Before we dive into AI use cases in analytics, let’s briefly talk about the confusion you can get when talking about various terms related to AI and advanced analytics solutions. First, you will often talk about predictive analytics, or analysis where a machine uses historical data to predict the future. AI supports many sophisticated predictive analytics solutions. So when you see the term you might think of AI. The key here is in the nature of the solution’s predictive model. To be a real AI, the predictive model really needs to learn from its predictions and improve.

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