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.
Predictive analytics use cases
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 ways 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. 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.
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 worldâs largest manufacturers of aircraft engines, has deployed predictive analytics to help dramatically reduce the amount of carbon its engines product while also 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 PepsiCoâs 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 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:
Alteryx Analytics Automation Platform Amazon SageMaker H20 AI Cloud IBM SPSS RapidMiner SAP Analytics Cloud SAS Viya TIBCO
For more on the tools that drive predictive analysis, see âTop 8 predictive analytics tools.â
Predictive analytics models
Models are the foundation of predictive analytics â 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 â and even neural networks â 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 thatâs 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 â 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 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 that it uses to identify non-intensive care unit (ICU) patients that are likely to rapidly deteriorate within the next 12 hours. NorthShore University HealthSystem has embedded a predictive analytics tool in patientsâ electronic medical records (EMRs) that helps it identify which chest pain patients should be admitted for observation and which patients can be sent home.
For a deeper look, see âHealthcare analytics: 4 success stories.â
How should an organization begin with predictive analytics?
While getting started in predictive analytics isnât a snap, itâs 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 âHow to get started with predictive analytics.â
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: $72K-$134K Director of analytics: $84K-$179K Business analyst: $49K-$87K Chief data scientist: $133K-$290K Data analyst: $46K-$89K Data scientist: $70K-$137K
More on predictive analytics:
7 projects primed for predictive analytics 7 ways predictive analytics can improve customer experience Top 8 predictive analytics tools compared Identifying high-risk patients with predictive analytics Predictive analytics: 4 success stories
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