Predictive Modeling applies decision trees, regressions, and artificial intelligence (AI) deep learning methods to historical data to forecast future values to enable more informed decision-making.
Why is Predictive Modeling Important?
Predictive modeling empowers businesses to make informed decisions by analyzing historical data to identify patterns. This makes it easier to anticipate market trends, customer preferences, and potential risks.
These predictive models minimize the trade-offs between precision and control versus time to insight, allowing more people within an organization to get fast and accurate answers to business-critical questions.
Predictive Analytic models
Several predictive modeling methods are used by data scientists, depending on the type of data available to them and the nature of the question they are working to answer.
The following section describes a selection of predictive analytic model types.
Forecast Model
The forecast model uses historical data as an input to make informed estimates that are predictive of future trends. If past predictions are inaccurate, variances can be applied.
Time Series
A time series consists of multiple events ordered by time-stamped data. Past values are used to predict future values.
Regression
Regression models estimate the strength of a relationship between variables. A predictive model will predict how actions, known as independent variables, will impact future relationships.
Decision Trees
Decision trees are more simplistic than most models but are useful for classification tasks like recommendations and anomaly detection. They use a tree-like structure to represent decisions and their possible outputs, and branches are created until they reach a final outcome.
Neural Network Models
A neural network mimics how humans think using nodes in a layered structure. The network consists of connected units or nodes, which loosely model neurons in the brain. A neural network can be trained on historical data and tuned by iteratively adjusting weights to minimize the difference between predictive and actual outcomes.
Logistic Regression Model
The logistic regression model aims to arrive at a yes or no answer, which helps predict customer behavior or anticipate customer churn.
Outliers Model
The outliers model detects anomalous data entries within a dataset. It is used to identify unusual values when identifying fraudulent transactions; the model can assess not only the amount but also the location, time, purchase history and the nature of a purchase to flag potentially fraudulent transactions.
Naive Bayes Model
Naive Bayes is useful for tasks that involve detecting and analyzing customer feedback and categorization of emails.
K-Nearest Neighbor Model
Predictive modeling is beneficial for uncovering hidden relationships. The K-nearest neighbor model operates on the assumption that similar information will cluster. It is a supervised machine learning (ML) model.
Applications of Predictive Models
Sales
Deciding the right approach to a sales relationship can be a tough call. Depending on what actions the prospective customer has taken to get to the stage of being ready for a call, choosing the correct email, voicemail, or sales tactic is crucial. Predictive analytics can use all the data points gathered about this individual and compare them to the success rates of the various tactics that have worked with similar individuals at this stage. It advises the sales team on the approach most likely to work.
Marketing
Marketing automation systems allow an organization to map out a desired buyer’s journey for a lead. Predictive analytics techniques that have studied past interactions can provide advice on what sequence or specific digital assets to map into future campaigns.
Gaming
Sports betting games use predictive analytics to dynamically set bet prices based on changing odds to maintain their profit margins. Individual player behavior can be analyzed to present the optimal in-game offers.
Online Retail
Predictive analytics can increase customer engagement, resulting in more product purchases. Past buying patterns can be analyzed to find matches, and current promotions can be offered to buyers in real-time as they shop.
Stock Trading
Trading information systems can study stock or commodity pricing to suggest potential buying or selling opportunities. Although past performance is often said to be no indicator of future success, hints can be handy.
Risk and Fraud Management
Predictive modeling is used to assess credit card, insurance, and banking transactions to monitor for fraud.
Actian Data Management for Predictive Analytics
AI Models depend on good data to deliver accurate predictive analytics. The Actian Data Platform perfectly complements analytics projects by providing a unified experience for ingesting, transforming, analyzing, and storing data. The Actian Data Platform provides ultra-fast query performance, even for complex workloads, without tuning due to the use of parallel queries boosted by vector processing at the CPU core level. DataConnect, the built-in data integration technology, prepares data for analysis and training by removing outliers, structuring data, and transforming values to improve query performance.
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