Why Are AI and Predictive Analytics Needed?

While actually gathering and storing massive amounts of information is hardly new, over the past decade organisations have realised Big Data has value. The very act of analysing your information lets you discover new insights that can be helpful when making decisions.

Over the past decade, organisations have realised Big Data has value.

The big question is how do you gain insights from massive amounts of information? That's where predictive analytics come into play. Predictive analytics refers to the use of data mining, statistics, and modeling to make predictions about what might happen in the future.

Predictive analytics technology has existed for over a decade but have only become more effective with the development of other technologies - specifically, a branch of AI called “machine learning”.

Machine learning enhances predictive analytics.

Machine learning is when a machine becomes more accurate in predicting outcomes without being programmed to do so by assimilating information given previously and drawing conclusions from it.

The idea of machine learning isn’t new – in fact, the math behind it dates from the 18th century – but recent applications have enabled this technology to scale up so that it can handle greater amounts of information. Some AI software on the market can process as much as one petabyte of information and provide predictions. 

How Can AI and Predictive Analytics Provide a Competitive Advantage? 

What if you were able to have more than just a guess about who your next customer is? We're not talking about fortune-telling or having psychic abilities; that's actually the power of AI and predictive analytics. 

Both have a variety of applications that could benefit your organisation. For instance, here's an example from marketing. 

Having a very good sense of who your next customer will be comes from leveraging AI and predictive analytics.

As marketers know, your product or solution isn’t the right fit for everyone. You need to find the right lead - someone who’s interested in what you have to offer, and more importantly, is ready to purchase. AI, coupled with predictive analytics, helps you determine your current customers’ attributes so you can find leads that have similar traits. 

Suppose you’ve identified more potential customers. How do you know which ones to pursue? AI and predictive analytics prioritise prospects, leads, and accounts based on the likelihood of them taking action. When you don’t waste time on the wrong leads, you have a greater chance of converting customers. 

AI and predictive analytics prioritise prospects, leads, and accounts based on the likelihood of them taking action.

Another excellent example of how predictive analytics and AI help you gain a competitive edge is in the field of e-commerce. While humans don’t always act logically, we do exhibit identifiable patterns of behaviour. We tend to buy the same things time and again; AI and predictive analytics make sense of these patterns, and can suggest products to customers which they are more likely to purchase. Personalised suggestions make customers feel as though they’re valued.

Microsoft: Enabling Companies to Make the Most of AI and Predictive Analytics

Microsoft offers Machine Learning Studio through Azure. Machine Learning Studio is a browser-based solution built on drag-and-drop authoring. With no coding needed, you can go from idea to deployment in a series of clicks. 

One of the benefits of Machine Learning Studio is that you can deploy your model in minutes. Moreover, you can access that model through any device, anywhere. The model can also utilise any data source, enabling you to get more out of your corporate stores of information. 

With Machine Learning Studio, you can deploy your model in minutes and use it on any device, anywhere.

Once you’ve created your model, you can share it in Microsoft’s Gallery or in the Azure Marketplace. This allows you to monetise it. One example of such a model is the Telco Customer Churn. Built with Machine Learning Studio, Telco Customer Churn helps predict which telco customers will churn (stop using a product or service) based upon real-world data.

The value in such a model is that you can tell which customers you’ll lose. When you know that, you can figure out if they're worth keeping, and if so, what methods would be best to do so. Additionally, you now know who your loyal customers are and can focus energies on retaining them. 

The value in predictive analytics is that you know which customers you’ll lose, and assess whether they’re worth keeping.

Don't worry if you’re not familiar with machine learning, as Microsoft offers useful resources to help you get started. There’s an introduction to machine learning concepts, tutorials, and sample experiments in the Gallery. If you have questions, you can turn to Microsoft engineers in the online forum.  

For current R or Python users, Microsoft offers some excellent options. Machine Learning Studio includes hundreds of built-in packages and support for custom code. 

Enlighten: Helping You Gain a Competitive Advantage 

Enlighten has two decades of experience in helping its customers gain a competitive advantage in the marketplace. We are proud partners with Microsoft, and can assist you in implementing AI and predictive analytics solutions at your organisation. To learn more, contact us today.