How Artificial Intelligence is Empowering Predictive Analytics

How Artificial Intelligence is Empowering Predictive Analytics


If there was a time when the problem of business enterprises was a lack of data, ours is a time where businesses have so much data they don’t know what to do with it.

We are like the child who was sad and furious that no one bought him a gift on his 5th birthday only to wake up to a litany of gifts on his 6th birthday. Now, his problem is not the absence of gift but the presence of too many gifts – much more than he can handle.

Today we have a litany of web analytics tools, CMS (content management) platforms, CRM (customer relationship management) systems, and customer data platform. Put together, the average organization have a broad range of customer and business data – so vast they do not know how to deal with it. We have come up with a new name for this new reality – big data.

However, businesses know that multiplying the data is not a business strategy; it is not the data that matters but the insights from the data. As Carly Fiorina, former CEO of HP, puts it, “The goal is to turn data into information, and information into insight.”

How do we make this move from data to information to insights?  Welcome Predictive Analytics.

In what follows, we will look at:

  • Predictive Analytics: what it is, what it does
  • The Impacts of AI on Predictive Analytics
  • Some use cases and applications of Predictive Analytics, and
  • The Benefits of Predictive Analytics.


Courtesy: Edupristine.com

What is Predictive Analytics?

Predictive Analytics is the use of data mining techniques, statistics, and modelling to make predictions based on the collection and analysis of a set of historical data. It seeks to use patterns in historical data to predict the future and identify risks and opportunities.

These forecasts serve as a decision-making tool for organizations. They can pursue, avoid, or change a course of action based on the forecasts. That is, predictive analytics can tell you what strategies you need to adapt, drop, or modify to achieve a certain business objective.

The aim is to develop insights that are easy to use and relevant to the organization. In the words of Peter Drucker, “What gets measured gets improved.” The improvement is the goal.

AI and Predictive Analytics

To succeed, predictive analytics tools need to analyze large sets of data quickly to identify patterns and gain insights from them. The use of machine learning, a subset of artificial intelligence, has enhanced the ability of predictive analytics tools and systems to do this more effectively.

With machine learning, algorithms are becoming smarter. They can ‘grow’ with the data, so to speak. As data increase in volume and complexity, machine learning empowers predictive analytics systems to grow in their understanding of the data and the insights they generate.

AI is improving the computational power of predictive analytics systems, allowing them to process more data and generate better insights.

Today, machine learning is at the heart of predictive modelling, which is at the heart of predictive analytics.

Some Use Cases

Let’s consider some of the top ways businesses are deploying AI-empowered predictive analytics.

Data-Driven Content

A company can analyze their past content and use predictive analytics systems to predict the performance of a content they are about to publish.

The company can go on to tweak and modify the content if the predicted performance is undesirable. Predictive analytics will help you understand the core factors that differentiate poor performing content and top-performing content so you can modify every content and improve performance.

Data-Driven Campaigns

A company can also analyze previous marketing campaigns and use predictive analytics to predict the performance of a marketing campaign they are about to deploy.

The results of this analytics may lead them to press the publish button or modify some elements of the marketing campaign. Companies can also gain insights from successful marketing campaigns and adjust future ones accordingly.

Lead Scoring

According to Marketo, “Lead scoring is a shared sales and marketing methodology for ranking leads in order to determine their sales-readiness. You score leads based on the interest they show in your business, their current place in the buying cycle and their fit in regards to your business.”

Lead scoring seeks to help marketing and sales team allocate their resources more efficiently by focusing on leads that are most likely to convert.

With predictive analytics, you can predict whether a customer will buy or not. These systems use data from your current customer base to predict if a lead (given his behaviour, demographics, etc.) will convert into a customer.

Product Recommendations

Predictive analytics can help businesses upsell and cross-sell. You can use data from your current customer base to predict if a current lead will make an extra purchase after buying a particular product. Based on that prediction, you can recommend the product to him after he makes the purchase.

Competitive Intelligence

You can gain insights and intelligence from your competitors’ data. By identifying what is working for your competitors, you can predict if a content piece or campaign you are creating will succeed. That insight can help you improve your content and campaign to match or exceed your competitors’.

Predictive analytics can also help you understand what your competitors are not doing well so you can identify opportunities to increase your competitive advantage.

Some Applications of Predictive Analytics

However, predictive analytics is not only a marketing tool. So before you accuse me of showing love to my favourite child, let me show some love to the other children as well.

Weather Forecasting

Predictive analytics is the technology behind weather forecasting. Today, we can now forecast the weather for the next five days, even up to nine and ten days.


Insurance companies use predictive analytics to determine the probability that a policyholder will make a claim in the future. They analyze large sets of data like past events and the risk pool of other similar policyholders. They use this information to determine the premium a policyholder will pay.


Income investors use predictive analytics to predict the price movements of a stock, currency, or commodity. Banks also use it to identify potential fraud and define market risk.


Retail stores are using predictive analytics to understand customer behaviour. They can use historical data to predict who will buy what at different periods in the year. This helps them to optimize inventory management and sales.


Google Flu, though not very successful, increased the adaptation of predictive analytics in healthcare. Predictive analytics can help public health professionals predict the possibility of a disease outbreak in a location.

Benefits of Predictive Analytics

With predictive analytics, businesses can:

  • Increase their revenue: Predictive analytics help companies to maximize returns from their content marketing and other marketing campaigns. It also leads to increased revenue from upselling and cross-selling.
  • Reduce their cost: By making data-driven decisions, companies will reduce marketing cost as well as operational cost.
  • Improve customer satisfaction: When you understand your customers better, you will deliver a better experience, which improves their satisfaction. Recommending the right products for them, publishing the content they want to read, and creating campaigns that show you understand them will deepen their commitment to your brand.
  • Increase their competitive advantage: With the competitive intelligence it provides, you will always find opportunities to gain a competitive advantage.


AI-empowered predictive analytics tools and systems can help you derive data-driven insights that will improve your business’s performance across the board. They will help turn your data to information and information into insights – the kind of insights that will grow your business beyond bounds.

Remember, “Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, Senior VP, Gartner Research.

Are you ready to explore predictive analytics? Here are forty predictive analytics tools you can consider.

This is the end of our series on artificial intelligence and digital marketing. If you are new, you can get started here.

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