The workflow for building predictive analytics frameworks follows five basic steps:

1. Define the problem: A prediction starts with a good thesis and set of requirements. For instance, can a predictive analytics model detect fraud? Determine optimal inventory levels for the holiday shopping season? Identify potential flood levels from severe weather? A distinct problem to solve will help determine what method of predictive analytics should be used.
2. Acquire and organize data: An organization may have decades of data to draw upon, or a continual flood of data from customer interactions. Before predictive analytics models can be developed, data flows must be identified, and then datasets can be organized in a repository such as a data warehouse like BigQuery.
3. Pre-process data: Raw data is only nominally useful by itself. To prepare the data for the predictive analytics models, it should be cleaned to remove anomalies, missing data points, or extreme outliers, any of which might be the result of input or measurement errors. 
4. Develop predictive models: Data scientists have a variety of tools and techniques to develop predictive models depending on the problem to be solved and nature of the dataset. Machine learning, regression models, and decision trees are some of the most common types of predictive models.
5. Validate and deploy results: Check on the accuracy of the model and adjust accordingly. Once acceptable results have been achieved, make them available to stakeholders via an app, website, or data dashboard.

In general, there are two types of predictive analytics models: classification and regression models. Classification models attempt to put data objects (such as customers or potential outcomes) into one category or another. For instance, if a retailer has a lot of data on different types of customers, they may try to predict what types of customers will be receptive to market emails. Regression models try to predict continuous data, such as how much revenue that customer will generate during their relationship with the company. 

Predictive analytics tends to be performed with three main types of techniques:

Regression analysis

Regression is a statistical analysis technique that estimates relationships between variables. Regression is useful to determine patterns in large datasets to determine the correlation between inputs. It is best employed on continuous data that follows a known distribution. Regression is often used to determine how one or more independent variables affects another, such as how a price increase will affect the sale of a product.

Decision trees

Decision trees are classification models that place data into different categories based on distinct variables. The method is best used when trying to understand an individual's decisions. The model looks like a tree, with each branch representing a potential choice, with the leaf of the branch representing the result of the decision. Decision trees are typically easy to understand and work well when a dataset has several missing variables.

Neural networks

Neural networks are machine learning methods that are useful in predictive analytics when modeling very complex relationships. Essentially, they are powerhouse pattern recognition engines. Neural networks are best used to determine nonlinear relationships in datasets, especially when no known mathematical formula exists to analyze the data. Neural networks can be used to validate the results of decision trees and regression models.