The 5 Key Predictive Analytics Models You Need To Know

Today’s businesses work with endless amounts of data and need to make informed decisions based on it. In doing so, employees often face barriers in collecting and evaluating data as they have to get used to the new complexity. In this article, we’ll provide an overview of the most popular types of predictive analytics models and algorithms currently used to solve business problems.

What Are Predictive Analytics Tools?

Predictive analytics tools are based on different models and algorithms that can be used for different applications. Identifying the benefits of predictive analytics tools for your business is key to getting the most out of your solution and using the data to make informed decisions.

The problem is that many companies want to achieve great results but don’t know where to start. Implementing advanced analytics initiatives can be a daunting task, but the following five algorithms can make it easier.

But how does predictive analytics help your business? Most often, they start with a use case. It often involves new ways of transforming and analyzing data to uncover previously unknown patterns and trends in the data. Applying new insights to business processes and practices can lead to positive changes in a company.

What Are The Top Five Types Of Predictive Analytics?

Type 1. Classification Model

The classification model is considered the simplest among the different types of predictive analytics model that classifies data and provides clear and easy to understand answers to the questions in the questionnaire. It groups data into categories based on inferences drawn from historical data. It is the model that best answers the “yes” or “no” questions and provides a comprehensive analysis that can be used to guide action. The versatility of the classification model means that it can be applied across a wide range of industries.

Type 2: Cluster Model

The cluster model organizes data according to common characteristics. It is a mechanism that bundles data into discrete, nested and intelligence based on similar behaviors. For example, if an online shoe company wants to launch targeted marketing campaigns for its customers, it can filter hundreds of thousands of records and create a personalized strategy for each user. With this model, a company can easily determine the credit risk of a borrower based on the past performance of other borrowers in the same or similar circumstances.

Type 3: Time Series Model

The time series model consists of a series of data points collected over time that serve as input data. Based on the previous year’s data, a numerical index is calculated and used to forecast data for the next three to six weeks.

This is an effective way to understand how each piece of data changes over time and is more accurate than simple averaging. It also takes into account seasons or events that may affect the index.

The number of stroke patients hospitalized in the last six months can be used to predict how many patients are expected to be admitted next week, next month or at the end of the year.

Type 4: Forecast Model

The forecast model is one of the most widely used predictive models and is used to predict metrics. This very popular model applied to anything that is numerically significant and based on learning from past data. It estimates the numerical value of new data based on past data. This model can be used wherever historical data are available.

The model also includes a number of input parameters. If a restaurant owner wants to predict how many customers he will have in the coming week, the model takes into account that affect him, such as: how many pizzas a restaurant will order next week or how many customer service calls a customer service department will handle in a day or a week.

Type 5: Outliers Model

The outliers model is based on the metrics records in the database. It works by analyzing anomalies and unusual data points. You can define outliers on their own or in combination with other outliers and categories. For example, a bank might use the outliers model to detect fraud by checking whether certain transactions deviate from the usual pattern of customer spending, or whether certain types of spending are normal.

How To Find The Best Predictive Model For Your Business?

First, decide what predictive questions you want answered and at what quality level. And above all, what you want to do with the data. Weigh up the benefits of each model, optimize the use of different predictive analytics algorithms and decide how to apply them to your business.

It is therefore not easy to decide which of these models is best for you and your business. It has to be a carefully considered decision. If you still need help or have questions, please contact Existbi’s Predictive Analytics Consulting Team and see how we can make it work right for you!


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