What is predictive analytics?

Predictive analytics helps businesses look into the future and peer around corners with a reasonable degree of accuracy. This capability has always been important – but it has never been as critical as it is right now. Companies have had to navigate major trade and supply chain disruptions, sudden spikes (or nosedives) in demand, brand new risks and challenges, and overall unchartered waters. That’s why predictive analytics has shot to the top of priority lists for organizations around the world.

Predictive analytics definition

Predictive analytics is a branch of advanced analytics that makes predictions about future events, behaviors, and outcomes. It uses statistical techniques – including machine learning algorithms and sophisticated predictive modeling – to analyze current and historical data and assess the likelihood that something will take place, even if that something isn’t on a business’ radar.

Predictive analytics is relevant to most industries and has myriad uses, including:

Reducing employee and customer churn
Identifying customers who are most likely to default on payments
Supporting data-based sales forecasting
Setting optimal pricing
Tracking when machines will need maintenance or replacement

Actionable, accurate predictions are essential in helping decision-makers navigate a world where rapid change and market volatility are constants. And while that was true before COVID-19, the ability to pivot and forecast and plan for multiple possible scenarios is now more critical than ever.

Predictive analytics has also played a key role in the fight against COVID-19. Hospitals and health systems use predictive models to gauge risk, predict disease outcomes, and manage supply chains for medical equipment and PPE. In turn, researchers are using models to map the spread of the virus, predict case numbers, and manage contact tracing, all with the goal of reducing infection numbers and deaths.

Predictive analytics, as shown above, can help businesses anticipate cash flow.

Predictive vs. prescriptive analytics

After building and deploying predictive models that generate accurate, timely predictions – what’s next? Many businesses see prescriptive analytics as the next logical step.

Predictive analytics helps you determine what’s likely to happen next, whereas prescriptive analytics can tell you what to do about it – or how you could achieve a better result if you did X, Y, or Z. This type of advanced analytics builds on predictive analytics and takes many, many different factors into account to prescribe the best possible course of action or decision.

Prescriptive analytics is often described as the “last phase of business analytics.” It’s also the most complex and relatively new – currently sitting at the peak of Gartner’s Hype Cycle for Analytics and Business Intelligence 2020.

Predictive analytics today

According to a study from Allied Market Research, the global predictive analytics market is projected to reach US$35.45 billion by 2027, growing at a compound annual growth rate (CAGR) of 21.9%. Predictive analytics has truly come into its own in today’s world, where massive amounts of data are being generated, computers have exponentially faster processing power, and software has become more interactive and easier to use.

Companies not only collect huge volumes of data, they collect many different types – from traditional structured data to unstructured data like Internet of Things (IoT), text, video, and dark data. The ability for predictive analytics to combine and analyze Big Data from different sources produces more accurate forecasts and surfaces insights that are deeper and more powerful. The cloud is key for connecting all these different data sources – plus, storing data in cloud-based data warehouses and lakes is more cost-effective and more scalable than storing it on premise.

Today’s predictive analytics is also “augmented” with artificial intelligence (AI) technologies like machine learning, deep learning, and neural networks. These augmented analytics can analyze large volumes of data quickly, reveal insights that humans might miss, and make predicting the likelihood of future events more nuanced and more accurate. They also automate complicated steps in the predictive analytics process, such as building and testing predictive models. And natural language processing (NLP), a type of AI that lets users ask questions and get answers in conversational language, makes interpreting and understanding these answers easier than ever.

Historically, the tools and techniques behind predictive analytics have been so sophisticated – and so complicated – that only data scientists and professional analysts have been able to use them effectively. But with augmented analytics, business users with minimal training are now able to generate accurate predictions and make smart, forward-looking decisions without help from IT – an advantage that can’t be ignored in a fiercely competitive market.

Examples of predictive analytics

Predictive analytics is applicable and valuable to nearly every industry – from financial services to aerospace. Predictive models are used for forecasting inventory, managing resources, setting ticket prices, managing equipment maintenance, developing credit risk models, and much more. They help companies reduce risks, optimize operations, and increase revenue.

Predictive analytics in HR

HR is a field that naturally tracks a large quantity of people data. With predictive analytics, that data can be analyzed to determine if a potential employee is likely to be a cultural fit, which employees are at risk of leaving an organization (shown below), whether a company needs to upskill an employee or hire to fill skills gaps, and if employees are productively contributing to business outcomes. These abilities mean that HR can contribute to overall business outcomes rather than act as an isolated function.

Predictive analytics in HR can be used to predict employee churn.

Predictive analytics in healthcare

In today’s world, hospitals and healthcare organizations are under immense pressure to maximize resources – and predictive analytics makes that possible. Using predictive analytics, healthcare officials can improve financial and operational decision-making, optimize inventory and staffing levels, manage their supply chains more efficiently, and predict maintenance needs for medical equipment. Predictive analytics also makes it possible to improve clinical outcomes by detecting early signs of patient deterioration, identifying patients at risk for readmission, and improving the accuracy of patient diagnosis and treatment.

Predictive analytics in retail

Retailers gather vast amounts of customer information both online, such as tracking online activity via cookies, and in the real world, such as monitoring how customers navigate their way through a store. Other information tracked includes customers’ contact details at the point of sale, their social media activity, what they’ve purchased, and how often they purchase specific items or visit a store. Using predictive analytics, retailers can leverage that data for everything from inventory optimization and revenue forecasting to behavior analytics, shopper targeting, and fraud detection.

Predictive analytics in marketing

The models generated by predictive analytics are extremely valuable for marketers in making their campaigns more targeted and effective in a world where customers can order what they want, when they want, from almost anywhere online. Predictive marketing analytics drives data-driven customer and audience segmentation, new customer acquisition, lead scoring, content and ad recommendations, and hyper-personalization. Marketers can use a customer’s data to feed them promotions, ad campaigns, and suggestions for other products they may like at just the right time, improving customer experience and retention.

Predictive analytics in supply chain

Predictive analytics has become essential for running an agile, resilient supply chain and avoiding disruption. It analyzes massive data sets from many different sources to generate accurate supply and demand forecasts, determine optimal inventory levels, improve logistics and on-time deliveries, predict equipment maintenance issues, detect and adapt to unexpected conditions – and much more.

Companies using predictive analytics

Motor Oil Group is an industry leader in crude oil refining and the sales of petroleum products across Greece and the Eastern Mediterranean region. With the support of predicitive analytics capabilities, they harnessed sensor data to continuously monitor equipment health and predict potential malfunctions days before they happen. The results? They achieved greater than 77% accuracy in explaining abnormal events from 120 to 20 hours in advance using root-cause analysis of historical data.

Ottogi Corporation is one of the biggest food and beverage companies in Korea and a globally renowned brand of curry powder, instant noodles, and many other products. Forecasting demand with predictive analytics is an essential part of the business, informing strategic decisions for the sales, marketing, manufacturing, and financial departments allowing for deep insights on market share and the business.

Basic steps in the predictive analytics process

The predictive analytics process involves defining a goal or objective, collecting and cleaning massive amounts of data, and then building predictive models using sophisticated predictive algorithms and techniques. This traditionally complex process is becoming more automated and more accessible to the average business user thanks to new AI technologies, but companies may still need IT to help in certain steps or to build certain models.

In very simple terms, the steps in the predictive analytics process are as follows:

The steps in the predictive analytics process.

Define your project’s objectives. What is the desired outcome? What problem are you trying to solve? The first step is to define your project’s objectives, deliverables, scope, and data required.
Collect your data. Gather all the data you need in one place. Include different types of current and historical data from a variety of sources – from transactional systems and sensors to call center logs – for more in-depth results.
Clean and prepare your data. Clean, prepare, and integrate your data to get it ready for analysis. Remove outliers and identifying missing information to improve the quality of your predictive data set.
Build and test your model. Build your predictive model, train it on your data set, and test it to ensure its accuracy. It may take multiple iterations to generate an error-free model.
Deploy your model. Deploy your predictive model and put it to work on new data. Get results and reports – and automate decision-making based on the output.
Monitor and refine your model. Regularly monitor your model to review its performance and ensure it’s providing the expected results. Refine and optimize your model as needed.

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