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What Is Predictive Analytics?

What Is Predictive Analytics?

Learn more about what predictive analytics is, how it works, and how it can help your business succeed.

What if you could predict the future? For example, if you were able to predict the best offer to convince customers to open a marketing email and hand over their credit card details? Or which of your customers is most likely to take their business elsewhere? You can — with predictive analytics.

Predictive analytics allows businesses to predict what is likely to happen in the future, by looking for patterns in the information they already have. A subset of data analytics — the science of analysing raw information to answer specific business questions — it uses techniques including machine learning, statistics, data mining, and artificial intelligence (AI) to create predictive models. These models are used to examine datasets for underlying patterns or causes, and predict outcomes.

Organisations are already collecting vast quantities of data, ranging from customers’ personal details, browsing habits and purchasing histories, to sales figures, revenue, and profits. Predictive analytics allows them to turn that data into insights they can use to make better decisions and improve outcomes across their business.

How is predictive analytics used?

Predictive analytics used to be out of reach for most organisations. However, recent advances in the technologies that underpin it, including machine learning and AI, have made it more accessible. 

And although just 28% of U.S. businesses use predictive analytics, the majority surveyed consider it to be “critical” or “very critical.” In fact, the global market for predictive analytics is expected to triple to about $10.95 billion by 2022, from $3.49 billion in 2016.

A common entry point is to use predictive analytics tools in conjunction with a business’s customer relationship management (CRM) system. Using their CRM allows companies to make predictions about customer behaviour across sales, marketing, and service channels. This might include analysing customers’ past behaviours, including product usage and spending, to identify opportunities for cross-selling. Or to find ways to optimise the products, offers, or content shown to each customer. For example, the streaming service Netflix makes recommendations for programs a customer might enjoy based on their viewing history. 

Other uses might include using predictive analytics to determine which customers are at the highest risk of canceling their products or services or switching to a competitor. This gives sales reps the opportunity to engage with customers to try to change their mind. Customer service teams can use predictive analytics to determine the category and severity of a case as it is logged, so it can be routed appropriately.

Organisations also use predictive analytics to reduce risk. For example, banks use a mortgage applicant’s data — including their employment status, income, savings-to-debt ratio, and credit score — to predict whether they would be a low- or high-risk borrower. They also use this information to determine how much money and what interest rate they are willing to offer. In addition, banks and other financial institutions use machine learning to spot patterns that could indicate fraud.

Healthcare providers also use predictive analytics in a variety of ways. For example, Texas Children’s Hospital has developed a predictive model that uses information about social and psychological factors that affect patients to predict their risk of developing diabetic ketoacidosis, a dangerous complication of diabetes. This allows caregivers to identify high-risk patients and monitor them more closely. Using this model has resulted in a 30.9% reduction in repeat admissions for the complication annually.

Finally, predictive analytics can enable manufacturers to identify problems in advance and take steps to avoid or reduce their effect on production. For example, companies can use a predictive model for equipment performance and estimate when a service is needed.

Why is predictive analytics important?

The data that businesses and governments generate is a gold mine of information that can be used to improve customer experience, guide decision-making, and create competitive advantage. But just like gold ore, raw data needs to be processed before it can be used. It’s only after you dust off the dirt and extract the precious insights that the true value is revealed. Enter the field of data analytics.

There are several types of data analytics. These include descriptive analytics, which explains what happened in the past, and diagnostic analytics, which explains why it happened. They are used to make large quantities of information more manageable by condensing it into smaller, more easily understood chunks, as well as to identify the significance of past events in relation to business actions.

Businesses often use these forms of data analytics to generate reports on everything from company finances to inventory management and workforce productivity. Descriptive analytics can also be used to track things like social media performance, such as how many times a post is shared, liked, or retweeted.

Prescriptive analytics is a more abstract form of data analytics. It allows users to create “what if” scenarios, and extrapolate outcomes based on variables. This type of advanced analytics is often used in healthcare, where a doctor’s interpretation of facts is as important as hard evidence. Airlines also rely on prescriptive analytics to consider many potential factors when setting the price of airline tickets. 

Predictive analytics, on the other hand, answers the question: What could happen next? To do so, it uses predictive models to look at the variables likely to influence future results. Once data has been collected for each variable, a statistical model is formulated.

Types of predictive analytics models

There are three main types of predictive models — decision trees, regression, and neural networks. Decision trees use a tree-shaped diagram to chart the possible outcomes of different courses of action, including how one choice leads to others. Regression techniques use statistics to help users understand the relationships between different variables, such as commodities and stock prices. Meanwhile, neural networks are complex algorithms designed to mimic the way the human mind works, and by doing so, identify nonlinear relationships in data.

In practice, predictive analytics tools are usually predictive analytics software programs that enable users to mine large volumes of data to find valuable relationships between causes and consequences. They also allow users to make educated predictions, based on a better understanding of the available data. Organisations can then share these projections across departments and put them to use. 

And while predictive analytics can never produce conclusions that are 100% accurate, they are generally reliable forecasts that can improve business outcomes. In fact, a Forbes Insights report found that 86% of executives who used predictive marketing for at least two years reported an increased return on investment. The report found that one of the primary benefits of predictive marketing was that it enabled a much greater degree of focus, including “the ability to better identify market opportunities, better ad targeting, improved nurture programs, and more targeted accounts.”

How to get started with predictive analytics

While the full potential of predictive analytics is yet to be realised, it has two particularly exciting features for businesses. These are the ability to embed predictions in context during decision-making so that business users can act on them in real-time. And the ability to automate workflows and business processes, based on data-driven forecasts, freeing up workers for higher-value tasks such as customer service and problem-solving.

While the ability to fully capitalise on these features may be some way in the future for most businesses, it’s important to get started now. We recommend the following four-step process.

1. Collect data in one place. Predictive analytics requires a lot of data to work. However, for many organisations, this often lives in multiple, siloed systems.

2. Prepare the data. Once the data has been collected, it will need to be “cleaned” so that the predictive model can process it. This step can be time-consuming, but it’s important, as better data leads to better results.

3. Build the predictive model. Advances in technology mean this is easier than it sounds. Analytics software can help businesses to develop, test, and implement a predictive model without needing to have a team of data scientists on standby.

4. Use the results. This may involve sharing insights with employees on the frontline of dealing with customers such as sales reps and marketers. Or it may mean embedding the output of the model into a business context, such as a mobile app for customers.

By embracing predictive analytics organisation-wide, businesses will be able to unlock even more of the value hidden in their raw data. And, by doing so, turn it into nuggets of pure gold that will help them to predict the future.

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This post originally appeared on the U.S.-version of the Salesforce blog.


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