Predictive Analytics

Predictive Analytics is the process of using data to predict future outcomes.This process uses data analytics, machine learning, artificial intelligence, and statistical models to look for patterns that may predict future behavior. Using historical and current data, organizations can predict trends and behaviors seconds, days, or years into the future with great accuracy.

How Predictive Analytics Works

Data scientists use predictive models to identify correlations between different elements in a selected data set. Once the data is collected, a statistical model is formulated, trained, and modified to generate predictions.

The workflow for building a predictive analytics framework consists of five basic steps:

1. Defining the problem: Forecasting starts with a good thesis and a set of requirements. For example, can a predictive analytics model detect fraud? Can it determine optimal inventory levels for the holiday shopping season? Can it determine flood levels that may result from severe weather? A clear question to be solved will help determine the predictive analytics approach that should be used.
2. Acquiring and organizing data: An organization may have decades of accumulated data or a constant flow of data generated by customer interactions. Before developing a predictive analytics model, it is necessary to identify the data streams and then organize the data sets into a repository.
3. Preprocessing Data: Raw data by itself is only nominally useful. To prepare data for a predictive analytics model, you should clean the data to remove abnormal values, missing data points, or extreme outliers, which may be the result of input or measurement errors.
4. Developing predictive models: Data scientists can use a variety of tools and techniques to develop predictive models depending on the problem being solved and the nature of the dataset. Machine learning, regression models, and decision trees are the most common types of predictive models.
5. Verify and deploy results: Check the accuracy of the model and make adjustments accordingly. Once acceptable results are obtained, make them available to stakeholders through an app, website or data dashboard.

Predictive analytics methods

Generally speaking, there are two types of predictive models: classification models and regression models. Classification models try to classify data objects (such as customers or potential outcomes) into a category. For example, if a retailer has a lot of data on different types of customers, they might try to predict which types of customers are willing to receive marketing emails. Regression models try to predict continuous data, such as how much revenue a customer will bring in over the course of their relationship with the company.

There are three main analytical approaches commonly used to perform predictive analytics:

• Regression analysis: Regression is a statistical analysis technique used to estimate the relationship between variables. Regression can be used to identify patterns in large data sets, thereby determining the correlation between inputs. This method works best with continuous data that follows a known distribution. Regression is often used to determine how one or more independent variables affect another independent variable (for example, how a price increase affects the sales of a product).
• Decision Tree: Decision trees are classification models that classify data into different categories based on different variables. This method is best used to understand individual decisions. This model looks like a tree, with each branch representing a possible choice and the leaves of the branches representing the decision outcomes. Decision trees are usually easy to understand and perform well when the dataset is missing a few variables.
• Neural Networks: Neural networks are machine learning methods that are very useful predictive analytics methods when modeling very complex relationships. Essentially, they are powerful pattern recognition engines. Neural networks are best used to determine nonlinear relationships in a data set, especially when there is no known mathematical formula for analyzing the data. Neural networks can be used to validate the results of decision trees and regression models.

Uses and Examples of Predictive Analytics

Predictive analytics can be used to streamline operations, increase revenue, and reduce risk for nearly any business or industry, including banking, retail, utilities, public sector, healthcare, and manufacturing. Sometimes, augmented analytics is also used, which leverages big data machine learning. Here are some other use cases:

• Fraud Detection:Predictive analytics examines all actions within a company’s network in real time to identify anomalies that could lead to fraud and other vulnerabilities.
• Conversion and purchase predictions:Companies can take actions such as retargeting visitors with online ads, using data to predict which visitors are more likely to convert and have a higher likelihood of purchasing intent.
• Reduce risk:Credit scoring, insurance claims, and debt collection can all use predictive analytics to assess and determine the likelihood of future default.
• Operational improvements:Companies use predictive analytics models to forecast inventory, manage resources, and improve operational efficiency.
• Customer Segmentation: By segmenting the customer base into specific groups, marketers can use predictive analytics to make forward-thinking decisions to tailor content to unique audience segments.
• Maintenance forecast:Organizations use data to predict when equipment will need routine maintenance and can then schedule maintenance in time to avoid issues or failures.

References

【1】https://cloud.google.com/learn/what-is-predictive-analytics?hl=zh-cn