Predictive Analytics has many approaches and most depend on clean databases, relational data warehouse and the ability to mine data to look for patterns or to create classifications. It is important to understand the various approaches so you know when to use which one.
Regression models is a set of statistical processes for estimating the relationships among variables and it is the mainstay of predictive analytics. It includes many techniques for modeling and analyzing several variables. The linear regression model analyses the relationship between the response or dependent variable and a set of independent or predictor variables. That relationship is expressed as an equation that predicts the response variable as a linear function of the parameters.
- Logistic Regression
- Linear Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- It shows significant relationships between the dependent variable and independent variable.
- It shows the strength of impact of multiple independent variables on a dependent variable.
Choice model is used decision process of an individual or segment via preferences made in a particular context. It also helpful for making probabilistic predictions about decision-making via user behavior. It behooves every organization to target its marketing efforts at customers who have the highest probabilities of purchase.is is regarded as the most suitable method for estimating consumers’ willingness to pay for quality improvements in multi-dimensions.
It is used to identify the most important factors driving customer choices. This model enables a firm to find an individual’s likelihood of purchase and customer behavioral response, based on variables that the firm has in its database, such as geo-demographics, past purchase behavior for similar products, attitudes, or psychographics.
- Discrete Choice Modelling
- Volumetric Choice Modelling
- Conjoint Analysis
- R-Language Choice Modelling
- Analyse price sensitivity
- Bundle product and service features
- Optimize brand strategy
- Improve product-line planning
- Maximize media advertising effectiveness
- Improve promotional offers
- Optimize advertising messages
- Improve package designs
Rule induction involves developing formal rules that are extracted from a set of observations. The rules extracted may signify a scientific model of the data or local patterns in the data.
One major rule-induction paradigm is the association rule. Association rules are about discovering interesting relationships between variables in large databases. this technique applied to data mining and uses rules to discover regularities between products. Data mining uses techniques to sift through massive amounts of data to tell us something informative. In rule induction, if-then “rules” are generated based on patterns found.
For example, if someone buys peanut butter and jelly, he or she is likely to buy bread. The idea behind association rules is to understand when a customer does X, he or she will most likely do Y. Understanding those kinds of relationships can help business analysis
- Horn clause induction
- Association rule learning algorithms
- Decision rule algorithms
- Hypothesis testing algorithms
- Version spaces
- Rough set rules
- Inductive Logic Programming
- Boolean decomposition
- Sales forecasting
- Promotional pricing
- Product placements.
This is another technique for associating with records and used to evaluate relationships (connections) between nodes. Relationships may be identified among various types of nodes like organizations, people, and transactions. Link analysis is a subset of network analysis. It is commonly used for fraud detection and by law enforcement. You may be familiar with link analysis since several Web-search ranking algorithms use the technique.
- Find matches for known patterns of interests between linked objects.
- Find anomalies by detecting violated known patterns.
- Find new patterns of interest
Beyond data, predictive analytics can result in a positive impact on the entire organization. The immediate benefits of apply predictive analytics are usually realized first by marketers but eventually, it can transform the entire organization into data-driven and customer-centric culture.