Predictive Analytics

The sorts of models used to fuel the ensuing insights vary greatly, as do the potential uses for predictive analytics. Determining whether predictive analytics tools are ideal for our firm begins with a well-defined goal. Once we’ve decided what question we want to answer, we may select the model that best suits our needs. Predictive analytics models may be broadly classified into four types:

Models of regression

Regression models evaluate the strength of a variable-to-variable connection. The model monitors how actions (independent variables) affect results (dependent variables) and utilizes that data to forecast future effects. These statistical models might be basic, with one independent variable and one dependent variable, or complex, with two or more independent variables. There are several regression approaches that may be used based on the application and variables involved. Organizations may use scenario analysis, commonly known informally as ‘what-if’ analysis, to plug in new independent variables and evaluate how they impact the outcome by describing the connection between variables. Organizations may use a regression model to examine how the features of a product impact the chance of purchase. A company may notice a link between blue shirts and increased sales by studying the relationship between the color of the product and the chance of purchase. Because correlation does not imply causation, the organization may investigate how other characteristics, such as size, seasonality, or product positioning, influence purchase likelihood. They may utilize this information to improve their marketing efforts or product development to determine which products might perform well in the future. 

Models of classification

Based on past knowledge, classification models categorize data. Classification starts with a training dataset that has previously been labeled with each piece of data. The classification algorithm discovers the relationships between the data and the labels and categories of any new data. Decision trees, random forests, and text analytics are some common categorization model strategies. Many companies utilize categorization models because they can be quickly retrained with fresh data. Banks frequently employ categorization algorithms to detect fraudulent transactions. The system can evaluate millions of historical transactions to predict potential fraudulent transactions and inform clients when activity on their account appears suspect.

Models for clustering

Clustering models categorize data into categories based on similarities in properties. A data matrix is used in a clustering model to correlate each item with relevant attributes. Using this matrix, the algorithm will group objects with similar characteristics together, revealing patterns in the data that were previously concealed. Clustering models may be used by businesses to group consumers together and develop more tailored marketing tactics. For example, a restaurant may group its clients by location and only distribute fliers to people who reside within a specified driving distance of its newest location.

Models of time series

Time series models record data points as they change over time. Time is one of the most popular independent variables utilized in predictive analytics since so much of the world’s data can be described as a time series. A typical model would assess the previous year’s data and then forecast that statistic for the following weeks. Tableau’s powerful analytics solutions enable businesses to foresee and explore many scenarios with minimal time and effort. Organizations utilize time series analysis for a number of applications since time is a prevalent variable. This model may be used for seasonality analysis, which predicts how assets will react at different periods of the year, or trend analysis, which determines how assets will move over time. Some practical applications include forecasting sales for the upcoming quarter, predicting the number of visitors to a store, or even determining when people are most likely to get the flu.    

Other strategies for predictive analytics

To mine the data for insights and possibilities, a mix of these models is frequently employed. Neural networks, for example, are a set of algorithms meant to replicate the human brain and find patterns in data. Because neural networks include regression, classification, clustering, and time series models, they can handle large amounts of data and create exceedingly complicated connections. In truth, neural networks are capable of processing more than simply text data. They may also input photos, audio, video, and other types of data using deep learning techniques, and training on labeled datasets helps these networks to increase their accuracy. Deep learning algorithms are now employed in speech and face recognition software, and networks can scan facial motions to identify individuals’ disposition. With information like this, organizations can potentially predict the emotions customers will feel when using certain products or services.