Predictive analytics
What is predictive analytics?
Predictive analytics refers to the use of historical data, statistical modeling, data mining and machine learning to make predictions about future outcomes and performance.
There are three main types of predictive analytical models:
- Decision trees: A model that places data into sections based on predefined variables. A decision tree has "branches" that indicate choices available, and "leaves" that represent decisions.
- Regression: A model that helps identify patterns in large sets of data where a linear relationship between inputs exists.
- Neural networks: A model inspired by the structure of the human brain that consists of various layers of nodes: an input node, one or more hidden layers of nodes and an output node. Based on which nodes are activated, neural networks can make predictions based on the input data. This predictive analytical model is used in situations where there are complex data relationships.
Almost any business in any industry can benefit from predictive analytics. Some common use cases include:
- Finance: Predicting project sales, revenue and expenses.
- Marketing: Forecasting sales trends or predicting the likelihood a lead will move down the marketing funnel.
- Manufacturing: Predicting when machinery is likely to require maintenance.
Benefits of predictive analytics
Predictive analytics can be used to benefit organizations in many different ways, including, but not limited to:
- Reduce fraud: Using the data and experience gained through fraud detection practices, organizations can build fraud prevention strategies that can help stop fraud before it occurs.
- Make informed business decisions: Predictive analytics can give businesses insight into the likelihood a product or service will fail or be successful before it launches.
- Reduce customer churn: Algorithms can be trained to gauge the probability of customer churn and customize strategies for preventing it by supporting chatbots, natural language processing systems and sentiment and behavioral analysis.
- Improve customer experience: Historical data can be used to understand what common questions are asked by customers, make predictions about their wants and needs and prepare for those scenarios before they occur.
- Improve operations: Predictive models can be used to forecast inventory, manage resources and even predict employee retention rates.