Predictive and prescriptive analytics are two forms of advanced analytics. Predictive analytics helps us predict future outcomes, whereas prescriptive analytics takes those predictions to the next level by identifying the likely outcomes of these predictions and suggesting ways to alter them. Predictive analytics is understanding what will happen, and prescriptive analytics is understanding how to make it happen, prevent it, or change it. What is Predictive Analytics? Predictive analytics is a form of advanced analytics that can help a business predict future outcomes. Predictive analytics can predict customer behavior, future trends, or the likely outcome of current activities. The predictions are derived by analyzing historical data and using statistical methods to predict the outcomes we can expect in the future. Examples Churn prediction detects which customers a business is likely to lose, for example, through the cancelation of a subscription to services. Churn prediction can provide insight into possible future…
Predictive analytics is a form of advanced analytics that can help us predict future outcomes and identify risks and opportunities. It can consist of predicting customers' behavior, future market trends, potential risks for our business, profits, and opportunities. Predictive analytics uses machine learning and statistics to analyze historical data and make predictions. Predictive analytics has a number of useful applications in many industries and for a wide range of use cases. Let’s explore some examples of predictive analytics applications in the banking, manufacturing, retail, insurance, and telecom industries. Examples of Predictive Analytics Use Cases by Industry Banking Risk and Fraud Prevention Banks can leverage predictive analytics by implementing processes and procedures that inform them of potential risks and fraud. A straightforward example is that the bank will track the customers' spending habits together with the location; therefore, when an unusual activity that stands out from the pattern we have for…
Predictive analytics is an umbrella term for the use of data to predict future outcomes. The basic idea is that predictive analytics models can analyze historical data and find patterns. Patterns discovered by these models can predict future behavior. Some predictive analytics models are easier to understand than others, but they all have their place in business and data science. In this article, we'll outline the major types of predictive analytics models so you know what kind of model could help your organization achieve its goals. What is a Predictive Analytics Model? A predictive analytics model is essentially a set of algorithms that discovers patterns in data and uses those patterns to predict beneficial outcomes. Predictive Analytics Models can predict two main outputs: The probability of an individual case given known characteristics (i.e., how likely is this person who walked into the bank today to default on the loan they…
Predictive analytics, often also referred to as advanced analytics, has grown from a niche practice to an essential part of any company’s analytics arsenal. According to markets and research, the analytics industry will be worth $309 billion by 2026, a CAGR of almost 40%. As new predictive analytics techniques and algorithms become available, the value of predictive analytics becomes more evident and more far-reaching. As a recent Forbes article points out, the predictive power of AI allows companies to get companies back on track following the disruptions of the pandemic and the ongoing supply chain issues. Predictive analytics has powerful use-cases across many industries, and is proving especially useful for inventory management and delivery optimization, helping companies operate more leanly and with less waste. What is Predictive Analytics? From a business perspective, Predictive Analytics is the science of analyzing large volumes of historical data to identify scenarios where action can…
