Predictive Analytics

What is Predictive Analytics?

Predictive analytics is a form of advanced analytics. It uses statistical modelling, data mining techniques, artificial intelligence (AI) and machine learning (ML) to make predictions about future outcomes. You can use predictive analytics to identify business risks and opportunities.

What Is Meant by Predictive Analytics?

Traditional analytics programs are useful for transforming raw data into actionable intelligence. This helps turn data into a story, giving you an overview of what’s happened and making it easier to draw conclusions about why it happened.

Predictive analytics is the evolution of this process. It uses advanced technologies, artificial intelligence, and statistical modelling to turn trends and patterns from data analytics into anticipations of the future. You can analyze single decisions or tasks to see their future outcome and impact. This makes it easier to see where you should deploy resources for the best business outcomes.

How Predictive Analytics Works

Predictive analytics uses variables that can be measured and analyzed. These variables are combined into a predictive model that can assess future possibilities with an acceptable margin of error. ML algorithms process large amounts of “training data”, which helps them predict outcomes from the input data. Once the data has been analyzed and processed, a predictive model can then anticipate future events. AI-supported predictive analytics can flag likely events proactively. Through AI and predictability, you can have even more informed decision-making.

Data can also be sorted into different models. Classification models sort data into classes based on the relationships between data categories. Clustering models sort similar customer groups using demographic or other information. Time series models can also anticipate time-based data groups, such as daily fluctuations in the volume of customer service calls.

Types of Predictive Analytical Models

The most common models used in predictive analysis programs are decision trees, regression and neural networks.

Decision Trees

Decision trees are classification models. They make use of branching methodology to demonstrate all possible outcomes relevant to certain conditions. Decision trees can be used to visually represent decisions. Each branch depicts a different decision or event, and the leaf depicts the outcome.


The regression model is used to predict numbers by observing relationships between an outcome (a dependent variable) and the action (an independent variable). For example, a company might use regression analysis to determine how many additional sales can be made when shipping is free.

Neural Networks

The most complex predictive analysis model is a neural network, which is fashioned on the workings of the human brain. A neural network uses ML to identify abstract and non-linear patterns from data sets and makes predictions based on those patterns. With the ability to “learn” from experience, a neural network can continually improve without any extra programming or training.

Benefits of Predictive Analytics for Businesses

Predictive analytics can scrutinize past trends and customer behaviors, and determine future expectations. Predictive analytics can help streamline your workforce management procedures, inventory purchasing and transport requirements, sales and marketing approaches and more. Its ‘future-telling’ abilities can be applied to many processes. Some practical applications include:

  •   Risk reduction. Assess and interpret customers’ credit scores, gauge the risk of a customer defaulting and evaluate insurance coverage and claims.
  •   Fraud detection. Improve pattern detection and abnormalities, trigger security procedures based on unusual end-user behavior and identify and expose vulnerabilities and persistent threats.
  •   Marketing campaign enhancement. Predict customer purchasing behavior, target specific markets to attract the most profitable customer base and improve customer segmentation.
  •   Operational improvement. Forecast inventory needs and manage resources, set appropriate prices, schedule staff, deliveries and equipment maintenance, enhance marketing strategies according to anticipated surges, improve contact/data center service and customer satisfaction and stock inventory.
  •   Decision making. Assess the costs and benefits of expanding or enhancing products or services, provide competitive intelligence and evaluate conversion data from new and at-risk customers.

Industries Using Predictive Analytics

Here are some of the ways different industries can use predictive analytics to reduce risk, improve operations and enhance decision-making.


The manufacturing industry uses predictive analytics to optimize processes and improve efficiency:

  •   Identify quality control and production issues.
  •   Optimize distribution procedures.
  •   Apply appropriate service resources.
  •   Plan adequate product inventory and maintenance.
  •   Determine pricing strategies.
  •   Evaluate the cost and return of products over time.

Banking and Financial Services

Banking and financial services use predictive analytics to:

  •   Detect, prevent and reduce fraud.
  •   Evaluate credit risks.
  •   Leverage cross-selling opportunities.
  •   Support market changes in real-time.


In retail, predictive analytics helps enhance business processes and the customer experience:

  •   Plan inventory/merchandise purchasing and shipping.
  •   Optimize prices.
  •   Assess the effectiveness of promotional events and branding efforts.
  •   Assess the effectiveness of promotional events and branding efforts.


Predictive analytics has been used in healthcare to:

  •   Evaluate and manage patient care.
  •   Track infections and diseases.
  •   Identify and record effective diagnoses and treatments.
  •   Assess patient risk for specific infections and diseases.


Insurance companies leverage predictive analytics to:

  •   Detect, prevent and reduce claims fraud.
  •   Identify high-risk patients.
  •   Determine appropriate patient interventions and treatment protocols.

Getting Started with Predictive Analytics

Here are some steps you can take to begin to bring predictive analytics to your operational and decision-making processes:

Evaluate your business needs. What problems are you looking to solve? What trends and patterns would you like to understand and predict? What do you hope to gain by implementing these changes? Answering these key questions paints a clearer picture of how predictive analytics can help.

Identify your data sources. Predictive analytics outcomes are only as good as the data they use. It is important to clean your data, which includes combining data from disparate sources. That way, no single data point is able to influence the analysis unfairly.

Build your predictive model. A unified system that can prepare all data from across your company for analysis is essential. A data analyst can help you connect your collected data to the business problem you wish to solve and deploy the appropriate predictive models.

Ensure buy-in across the organization. Everyone can be on board for implementing a predictive analysis program, from assessing business needs to implementing granular changes. Choosing the right platform and people to prepare the data, build predictive models, review the forecasts and implement changes are matters for careful consideration.

The Future of Predictive Analytics

Traditional analytics programs have helped corporations examine what happened at their company in the past and why. This has helped business leaders draw conclusions about their biggest revenue drivers and where improvements might be needed.

Building on the successes of these programs, predictive analytics adds ML and AI business intelligence to these processes. It allows algorithms to dissect, assess and evaluate data for patterns and trends and forecast likely future risks and opportunities. This helps businesses improve their operational processes, workforce decisions, corporate strategies and, ultimately, their bottom lines.

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