Predictive Analytics: 6 Examples of Predictive Analytics

Predictive analytics is a type of data analysis meant to gain predictive insight into future outcomes. It uses statistical techniques and forecasting to reduce risk and increase optimization across a host of metrics and industries. For example, professionals apply predictive analytics to the areas of weather forecasting, commercial advertising, and cybersecurity.

This form of advanced analytics operates via sophisticated software technologies. Here are three key components necessary to this branch of data science:

1. 1. Artificial intelligence: To do big data analysis and forecast statistical models as efficiently as possible, artificial neural networks will do most of the legwork for you. Artificial intelligence (AI) goes hand in hand with the predictive analytics process—it sifts through giant compilations of data to parse out trends in minutes and seconds that would take human beings significantly longer.
2. 2. Data analytics software: Specific programs and software designed for data collection, data mining, and data analysis are more available now than ever. They include built-in methodologies for common statistical modeling techniques like decision trees, linear regression, and so on. This sort of predictive analytics software automates and simplifies the hard, mathematical work of doing this sort of analysis.
3. 3. Deep-learning algorithms: Data scientists design machine learning algorithms to increase artificial intelligence. As software sifts through historical data sets and performs statistical analysis, it gradually gets better at finding correlations, making attributions, and predicting future events because of these deep-learning algorithms. This leads to more effective statistical analysis.

There are numerous ways to put these analytical tools to work in different fields, disciplines, and industries. Consider these six predictive analytics examples:

1. 1. Business improvement: Predictive analytics can solve business problems and increase business intelligence. It can forecast issues that might arise throughout the supply chain lifecycle, suggest ways to improve workflow, predict how pricing changes will affect profitability, and more. Startups to longtime, established businesses can both benefit from the insight this technology provides.
2. 2. Credit scoring: Many credit card companies and banks use predictive analytics techniques to assess credit risk and assign scores. They do so by using other financial services data to ascertain who is likely to be a reliable candidate for credit and who is likely to renege on their payments. Some critics say these ostensible analytics solutions actually end up harming those who would most stand to benefit from loans and credit lines, while proponents insist this helps streamline how the broader credit system functions.
3. 3. Cybersecurity: By using statistical techniques and predictive models, cybersecurity companies can detect high-risk issues within their programs. Predictive analytics programs will sift through large amounts of data to see where vulnerabilities exist and, as a result, forecast where attacks may likely occur.
4. 4. Insurance: Whether for the car, home, or healthcare industry, insurance providers make heavy use of predictive analytics for fraud detection and risk assessment. They can use this technology as much to assess pharmaceutical vendors as they would potential customers. In certain cases, predictive analytics can also inform prescriptive analytics—a type of data analysis that prescribes solutions to problems rather than just predicts their occurrence.
5. 5. Marketing campaigns: Predictive analytics serves as a multifaceted, multipurpose technology for a marketing strategy. It provides insights to help marketers better cross-sell, understand customer churn and retention, improve customer experience and messaging, and set new initiatives in accordance with forecasted gains and losses.
6. 6. Weather forecasting: As analytics programs sift through data about weather trends, they can collate disparate information to arrive at greater accuracy as to what people can expect from one day to the next weather-wise.

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