What Is Predictive Analytics?

Knowing what your customer might do next is the superpower that every marketer dreams about. But artificial intelligence and machine learning is bringing predictive analytics out of the realm of fantasy and into the world of customer experience management.

In fact, according to the Digital Journal, the use of predictive analytics will spike among enterprises in 2018 in order to identify upcoming consumer trends and behaviors.

What is Predictive Analytics?

Predictive analytics, also known as predictive analysis, is a form of analytics that lets you forecast future events based on historical data. It combines statistical algorithms, AI and machine learning to analyze and learn from past customer actions. Like any prediction, it won’t be 100 percent accurate, but you can imagine the possibilities of being able to know what your customer’s future actions may be — statistically speaking, anyway.

With predictive modelling, you can discover and analyze patterns within your historical data to help uncover future opportunities and risks you’ll want to avoid. It sounds complex, but today’s evolving technologies are enabling businesses of all sizes to employ the use of software and their own data collection methods to forecast the future much easier.

Some examples of current predictive analytics include:

• Fraud detection: With historical fraud data, you can now be aware of the conditions, circumstances, and data sets that lead to fraud, vulnerabilities, and threats. You can then take proactive action to identify threats before they arise.
• Marketing optimization: Using past customer data, you can effectively recommend products, offer upsells, and create more targeted campaigns that your users will be responsive to.
• Streamline efficiency: Forecast modelling enables you to make future inventory, personnel, and resource predictions. Allowing you to maximize revenue and reduce costs that could arise from over-staffing or overstocking products.

How Does Predictive Analytics Work?

Predictive analytics utilizes models based upon historical data to predict values based upon new data entered into the model. Target variables are also utilized to yield a more accurate probability of an event to occur.  

There are two main types of predictive models:

1. Classification models: This model uses historical data to predict class membership of a new data point. To give you an example, this type of model can look at previous customer purchases to predict if a customer will buy a particular product.
2. Regression models: This model is used to calculate a projected figure. Regression models are able to determine the next time a customer will make a purchase and it can also predict how much revenue can be generated.

The predictive models above will be created through the three (most widely used) predictive modelling techniques below:

1. Linear and logistic regression: This technique is used to find patterns within large-scale datasets. It shows how a change in one variable will influence the end result, thus helping you to make better influence-based predictions.
2. Decision trees: This analysis looks at the overarching path of decisions an individual made to reach a specific end-result. This technique will help you to isolate logical variables and behaviour that played a key role in influencing a decision.
3. Neural networks: This type of analysis uses AI processes and patterns recognition to model complex relationships that are based on diverse datasets. This style is very powerful and flexible and is able to make sense out of seemingly unrelated data.

How do all these predictive analytics models come together, you ask? Leading predictive analytics solution MRP Prelytix combines these techniques into two separate packages; one for marketing teams and one for sales teams.

Between them, the two packages help marketers and salespeople identify marketing and sales opportunities, understand their target audiences, orchestrate content to be delivered at the right times and create strategies based on historical data. Moreover, the platform’s built-in analytics system enables users to review the data for themselves to gain deeper insights into their audience’s wants and needs.

Predictive Analytics and Hyper-Personalization

One of the most useful applications of predictive analytics is predictive personalization. Customers demand highly personalized brand experiences, right from the first contact to the final transaction. Customers also expect to receive this highly personalized experience each time they make a purchase.

With predictive analytics, we can finally achieve what Steve Jobs had set out to do: “Our job is to figure out what they're going to want before they do.”

You can implement predictive measures like:

• Predictive-scoring. This uses historical data and pattern recognition to prioritize leads based on their likelihood to take a specific action. Instead of relying on intuition and other “lead quality” scores you’ll be able to create lead engagement experiences that meets their expectation.  
• Market segmentation. In the past, lead segmentation was primarily done via generic attributes. But now you can use predictive algorithms to segment users, and in turn, provide appropriate marketing messages and timely conversation to the relevant customer.
• Attribute-based on-boarding. Users who take desired actions like making a purchase, buying an upsell product or other valuable behaviors can now be used to unearth new users and market segments. The data of these users can be modeled and then used to find similar users who are at the same point in the buying cycle and will be more receptive to your messaging.

Predictive Analytics vs. Machine Learning

It’s easy to assume that predictive analytics and machine learning are the same thing, but there are some key differences.

Machine learning is a field of computer science that gives computers the ability to “learn” things (like the likelihood of an event to happen based on historical data) without being explicitly pre-programmed by humans.  

Predictive analytics brings together a number of statistical strategies and algorithms, one of which is machine learning technology. Everything works together to understand the data being mined by the organization and figure out what the customer in question is most likely to do next.

In other words, machine learning is part of the predictive analytics equation, although it has a myriad of uses outside of the predictive analytics space.

Know Your Customer in a Whole New Way

Knowledge of customer behavior is one of the most critical pieces of providing your users with a hyper-personalized customer experience. Predictive analytics gives you an overarching view of customer behavior that includes not only their own past actions but the actions they’re most likely to take in the future. No longer is customer behavior limited to their own actions, but instead correlated with data of other users that have exhibited similar patterns.

And thanks to recent advances in machine learning, predictive analytics can be fully automated, leading to reduced cost. As brands begin to further experiment with predictive analytics, it will help to cement a shift towards creating a more relevant customer experience and establish a stronger brand loyalty.