Regression Modeling

Regression models are widely used in analytics, in general being among the most easy to understand and interpret type of analytics techniques. Regression techniques allow the identification and estimation of possible relationships between a pattern or variable of interest, and factors that influence that pattern.  For example, a company may be interested in understanding the effectiveness of its marketing strategies. It may deploy a variety of marketing activities in a given time period, perhaps TV advertising, and print advertising, social media campaigns, radio advertising and so on. A regression model can be used to understand and quantify which of its marketing activities actually drive sales, and to what extent.  The advantage of regression over simple correlations is that it allows you to control for the simultaneous impact of multiple other factors that influence your variable of interest, or the “target” variable. That is, in this example, things like pricing changes or competitive activities also influence sales of the brand of interest, and the regession model allows you to account for the impacts of these factors when you estimate the true impact of say each type of marketing activity on sales.

Types of Regression Analysis

There are several different types of regression techniques, including :

1. Linear regressions, which assumes that there is a linear relationship  between the predictors (or the factors) and the target variable,

2. Non-linear regression, which allows modelling of non linear relationships,

3. Logistic regressions, which is useful when your target variable is binomial (1,0 – Accept or Reject)

4. Time Series Regressions: used to forecast future behaviour of variables based on historical time ordered data

Business Applications

Regression techniques are widely used in for a variety of business needs.  Regression models are built to understand historical data and relationships to assess effectiveness, as in the marketing effectiveness models described above, or for example to assess impact of price changes on sales, to ranking people on propensity to respond to a direct mailing campaign, to flag potentially fraudulent applications, to assess cross-sell and up-sell opportunities across an existing customer base, to predict attrition or churn, and so on,

Regression techniques are used across a range of industries, including financial services, retail, telecom, pharmaceuticals,  and medicine.