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What is predictive analytics? Transforming data into future insights

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Predictive analytics definition

Predictive analytics is a category of advanced data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling, data mining, and machine learning (ML). Many organizations are beginning to leverage predictive AI to speed up and automate statistical data analysis. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future.

Predictive analytics has captured the support of a wide range of organizations, with a global market size of over $18 billion in 2024, according to Fortune Business Insights. The report projects the market will reach over $95 billion by 2032, growing at a compound annual growth rate (CAGR) of about 23% from 2024 to 2032.

Predictive analytics vs predictive AI

Organizations are increasingly turning to predictive AI over predictive analytics to anticipate, for example, future outcomes, causation, and risk exposure. While predictive analytics frequently makes use of AI tools, it’s driven by humans querying data, identifying trends, and testing assumptions. Predictive AI is autonomous, analyzing thousands of factors and years of data, and can be used to predict customer churn, supply chain disruptions, and mechanical failures, among other things. ML enables predictive AI to improve its forecasting accuracy over time.

Predictive AI vs gen AI

Predictive AI forecasts future events and outcomes while gen AI creates novel content. Predictive AI uses a combination of statistical analysis and ML algorithms to uncover insights in historical data and make predictions about upcoming events, results, or trends and often makes use of smaller, more targeted datasets than gen AI.

Gen AI, on the other hand, responds to prompts or requests by creating content — audio, images, software code, text, video — based on patterns learned from existing content. Gen AI can be used to hold conversations, answer questions, write stories, produce source code, and create images and video. Most start with a deep learning model called a foundation model — often a large language model (LLM) or small language model (SLM) to learn how to generate statistically probable outputs.

Organizations leverage predictive AI for things like financial forecasting, fraud detection, and supply chain management. Gen AI use cases include chatbots and virtual agents for customer service, creating targeted ad and sales copy for marketing and advertising, and generating code for software development.

Predictive analytics in business

Predictive analytics draws its power from many methods and technologies, including big data, data mining, statistical modeling, ML, and assorted mathematical processes. Organizations use predictive analytics to sift through current and historical data to detect trends, and forecast events and conditions that should occur at a specific time, based on supplied parameters.

With predictive analytics, organizations can find and exploit patterns contained within data in order to detect risks and opportunities. Models can be designed, for instance, to discover relationships between various behavior factors. Such models enable the assessment of either the promise or risk presented by a particular set of conditions, guiding informed decision making across various categories of supply chain and procurement events. For tips on how to effectively harness the power of predictive analytics, click here.

Benefits of predictive analytics

Predictive analytics makes looking into the future more accurate and reliable than previous tools. As such it can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules, and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants, and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.

By optimizing marketing campaigns with predictive analytics, organizations can also generate new customer responses or purchases, as well as promote cross-sell opportunities. Predictive models can help businesses attract, retain, and nurture their most valued customers.

Predictive analytics can also be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.

Challenges of predictive analytics

Initiating and maintaining a predictive analytics practice or predictive AI is no easy task. Here are some of the challenges organizations must account for, according to AI knowledge management specialist, Shelf:

  • Data quality and quantity: The quality and quantity of your data are key to the accuracy of predictions. Inadequate or poor quality data will lead to poor results.
  • Model complexity and interpretability: Many AI models are complex and opaque. Unless they’re built with transparency in mind, it can be extremely difficult to understand how they arrive at certain predictions.
  • Ethical, privacy, and regulatory concerns: Predictive models can have ethical implications, especially around privacy and bias. They must also comply with all relevant regulations. A misstep can lead to a loss of trust.
  • Integration and implementation: Integrating predictive analytics into existing systems and workflows requires care and thought, and integrating predictive AI with an IT infrastructure reliant on legacy systems can be difficult.
  • Skills gap: Professionals with skills in predictive analytics and predictive AI are in high demand, but they can be difficult to attract and retain.

Importance of explainability and transparency

Explainable AI (XAI), which is a set of methods and techniques that allows users to understand how and why an AI model reached a particular decision, is often considered an important element of predictive AI. The complexity of AI algorithms means it can be challenging or even impossible to determine how or why an algorithm reached a conclusion unless that algorithm was designed for explainability. The benefits of XAI include:

  • Transparency: Users can understand the factors influencing predictions.
  • Trust: Understanding how an algorithm makes a decision increases the trust of users and stakeholders.
  • Bias mitigation: Understanding potential biases in models means they can be adjusted to account for those biases.
  • Regulatory compliance: Highly regulated industries like finance require automated decisions to be explainable and auditable.

Predictive analytics use cases

Organizations today use predictive analytics and predictive AI in a virtually endless number of ways. The technology helps adopters in fields as diverse as finance, healthcare, retail, hospitality, pharmaceuticals, automotive, aerospace, and manufacturing.

Here are a few ways organizations make use of predictive analytics:

  • Aerospace: Predict the impact of specific maintenance operations on aircraft reliability, fuel use, availability, and uptime.
  • Automotive: Incorporate records of component sturdiness and failure into upcoming vehicle manufacturing plans. Study driver behavior to develop better driver assistance technologies and, eventually, autonomous vehicles.
  • Energy: Forecast long-term price and demand ratios. Determine the impact of weather events, equipment failure, regulations, and other variables on service costs.
  • Financial services: Develop credit risk models. Forecast financial market trends. Predict the impact of new policies, laws, and regulations on businesses and markets. Implement autonomous fraud detection.
  • Manufacturing: Predict the location and rate of machine failures. Optimize raw material deliveries based on projected future demands. Use supply chain management to optimize logistics and operations, production plans, resource allocation, and workload scheduling.
  • Law enforcement: Use crime trend data to define neighborhoods that may need additional protection at certain times of the year.
  • Retail: Follow an online customer in real-time to determine whether providing additional product information or incentives will increase the likelihood of a completed transaction. Project sales and demand for inventory management. Analyze customer behavior data to create personalized recommendations.

Predictive analytics examples

Organizations across all industries leverage predictive analytics to make their services more efficient, optimize maintenance, find potential threats, and even save lives. Here are three examples:

Rolls-Royce optimizes maintenance schedules and reduces carbon footprint

Rolls-Royce, one of the world’s largest manufacturers of aircraft engines, has deployed predictive analytics to help dramatically reduce the amount of carbon its engines product while optimizing maintenance to help customers keep their planes in the air longer.

DC Water drives down water loss

The District of Columbia Water and Sewer Authority (DC Water) is using predictive analytics to drive down water loss in its system. Its flagship tool, Pipe Sleuth, uses an advanced, deep learning neural network model to do image analysis of small diameter sewer pipes, classify them, and then create a condition assessment report.

PepsiCo tackles supply chain with predictive analytics

PepsiCo is transforming its ecommerce sales and field sales teams with predictive analytics to help it know when a retailer is about to be out of stock. The company has created the Sales Intelligence Platform, which combines retailer data with PepsiCo’s supply chain data to predict out-of-stocks and alert users to reorder.

Predictive analytics tools

Predictive analytics tools give users deep, real-time insights into an almost endless array of business activities. Tools can be used to predict various types of behavior and patterns, such as how to allocate resources at particular times, when to replenish stock or finding the best moment to launch a marketing campaign, basing predictions on an analysis of data collected over a period of time.

Some of the top predictive analytics software platforms and solutions include:

For more on the tools that drive predictive analysis, go here.

Predictive analytics models

Models are the foundation of predictive analytics — the templates that allow users to turn past and current data into actionable insights, creating positive long-term results. Some typical types of predictive models include:

  • Customer Lifetime Value Model: Pinpoint customers most likely to invest more in products and services.
  • Customer Segmentation Model: Group customers based on similar characteristics and purchasing behaviors.
  • Predictive Maintenance Model: Forecast the chances of essential equipment breaking down.
  • Quality Assurance Model: Spot and prevent defects to avoid disappointments and extra costs when providing products or services to customers.

Predictive modeling techniques

Model users have access to an almost endless range of predictive modeling techniques. Many methods are unique to specific products and services, but a core of generic techniques, such as decision trees, regression, and even neural networks, are now widely supported across a wide selection of predictive analytics platforms.

Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram used to determine a course of action or to show a statistical probability. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next.

Regression techniques are often used in banking, investing, and other finance-oriented models. Regression helps users forecast asset values and comprehend the relationships between variables, such as commodities and stock prices.

On the cutting edge of predictive analytics techniques are neural networks — algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind functions.

Predictive analytics algorithms

Predictive analytics adopters have easy access to a wide range of statistical, data-mining and ML algorithms designed for use in predictive analysis models. Algorithms are generally designed to solve a specific business problem or series of problems, enhance an existing algorithm, or supply some type of unique capability.

Clustering algorithms, for example, are well suited for customer segmentation, community detection, and other social-related tasks. To improve customer retention, or to develop a recommendation system, classification algorithms are typically used. A regression algorithm is typically selected to create a credit scoring system or predict the outcome of many time-driven events.

Predictive analytics in healthcare

Healthcare organizations have become some of the most enthusiastic predictive analytics adopters for a very simple reason: The technology helps them save money.

Healthcare organizations use predictive analytics in several ways, including intelligently allocating facility resources based on past trends, optimizing staff schedules, identifying patients at risk for a costly near-term readmission, and adding intelligence to pharmaceutical and supply acquisition and management.

Healthcare consortium Kaiser Permanente has used predictive analytics to create a hospital workflow tool it uses to identify non-intensive care unit (ICU) patients who are likely to rapidly deteriorate within the next 12 hours. NorthShore University HealthSystem has embedded a predictive analytics tool in patients’ electronic medical records (EMRs) that helps it identify which chest pain patients should be admitted for observation, and which can be sent home. For a deeper look, read more here.

How should an organization begin with predictive analytics?

While getting started in predictive analytics isn’t a snap, it’s a task that virtually any business can handle as long as one remains committed to the approach and is willing to invest the time and funds necessary to get the project moving. Beginning with a limited-scale pilot project in a critical business area is an excellent way to cap start-up costs while minimizing the time before financial rewards begin rolling in. Once a model is put into action, it generally requires little upkeep as it continues to grind out actionable insights for many years. For a deeper look, see How to get started with predictive analytics.

Predictive analytics best practices

Here are some best practices to get started with predictive analytics and predictive AI:

  1. Define your goals and objectives: To properly structure and scope your predictive analytics and predictive AI efforts, you first need to establish what you aim to solve or achieve and then define specific, measurable outcomes. Align your predictive analytics strategy with your business objectives. Establish your business objective and define a specific prediction objective to achieve it.
  2. Assemble the right team: Before you consider software or vendors, you’ll need a team with a mix of skills to achieve results, including data scientists, data engineers, business analysts, and IT specialists. Manage predictive analytics as an enterprise effort.
  3. Identify, collect, and prepare data: Identify the data that’s relevant to the goals you’ve established. Some of that data may be internal to your organization, but you may need to seek external sources. You must ensure data quality through data cleansing to make it suitable for modeling.
  4. Select tools, platforms, and modeling techniques: Based on your team’s expertise and the needs of your objective, select appropriate tools, platforms, and predictive modeling techniques.
  5. Deploy and maintain: Predictive modeling isn’t fire-and-forget. You’ll need to continually retrain your model with fresh data and fine tune it through experimentation. You’ll also need to establish a feedback mechanism on real-world performance.

Predictive analytics salaries

Here are some of the most popular job titles related to predictive analytics, and the average salary for each position, according to data from PayScale.

  • Analytics manager: $75,000 to $138,000
  • Director of analytics: $90,000 to $189,000
  • Business analyst, IT: $55,000 to $108,000
  • Chief data scientist: $131,000 to $306,000
  • Data analyst: $49,000 to $93,000
  • Data scientist: $72,000 to $141,000
  • Senior business analyst: $69,000 to $123,000

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