What is predictive analytics?
Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. 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 future trends and behaviors.
Predictive analytics has captured the support of wide range of organizations, with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022, according to a 2017 report issued by Zion Market Research.
How does predictive analytics work?
Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning 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.
What are the benefits of predictive analytics?
Over the centuries, peering into the future has always had three fundamental goals: money, fame and power. Predictive analytics doesn't really change the reasons why people want to know what's going to happen next week, next month or next year, it just makes looking into the future more accurate and reliable than previous tools.
Money: Predictive analytics 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.
Fame: No business ever succeeded by fading into obscurity. By optimizing marketing campaigns with predictive analytics, organizations can 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.
Power: Predictive analytics can 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.
How does predictive analytics differ from traditional analytics?
The difference between conventional analytics and predictive analytics is simple and straightforward. Whereas traditional analytics generally focuses on insights impacting the here and now, predictive analytics aims to allow users to gaze into the near- and long-term future to pinpoint likely trends and upcoming behaviors.
How should an organization begin with predictive analytics?
While getting started in predictive analytics isn't exactly 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.
Predictive analytics examples
Organizations today use predictive analytics in a virtually endless number of ways. The technology helps adopters in fields as diverse as finance, healthcare, retailing, hospitality, pharmaceuticals, automotive, aerospace and manufacturing.
Here are a few examples of how organizations are making 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.
- Manufacturing: Predict the location and rate of machine failures. Optimize raw material deliveries based on projected future demands.
- Law enforcement: Use crime trend data to define neighborhoods that may need additional protection at certain times of the year.
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 the best moment to launch a marketing campaign, basing predictions on an analysis of data collected over a period of time.
Virtually all predictive analytics adopters use tools provided by one or more external developers. Many such tools are tailored to meet the needs of specific enterprises and departments. Major predictive analytics software and service providers include:
- Information Builders
- SAS Institute
- Tableau Software
- TIBCO Software
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 who are 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 range of predictive analytics platforms.
Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that's 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 machine-learning 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 to 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 is helping them save money.
Healthcare organizations use predictive analytics in several different 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.
A 2017 Society of Actuaries report on healthcare industry trends in predictive analytics, discovered that over half of healthcare executives (57 percent) at organizations already using predictive analytics believe that the technology will allow them to save 15 percent or more of their total budget over the next five years. An additional 26 percent predicted savings of 25 percent or more.
The study also revealed that most healthcare executives (89 percent) belong to organizations that are either now using predictive analytics or planning to do so within the next five years. An impressive 93 percent of healthcare executives stated that predictive analytics is important to their business’ future.