Regression Scoring – Meaning, its Process and Types of Scoring

Regression scoring is one of the difficult but more precise and faithful marketing technique as compared to profiling and modeling. For targeting new and esteemed customers all the organization substantially pursue regression scoring techniques. Following is the process involved in regression scoring:

1. Identify the prospect or probable customers from the population of all customers and draw random samples from them.

2. Collect individual characteristics from the information and data available from these samples.

3. Record which all prospects are converted into customers after performing the marketing campaign on individual prospects.

4. Using this information and trend, produce a regression scoring model which is a series of important variables which are used to predict and which prospects can be easily converted into customers in accordance to their individual characteristics.

5. After the estimated model is ready the researchers engage themselves in following process.
   1. According to the model, create regression equations to implement information of future group of prospect customer.
   2. Plug the information and individual characteristics of prospects that are not in the sample to calculate scores.
   3. Rank the prospects according to the regression score according to highest and lowest values.
   4. Perform marketing campaigns at the prospects that have scores above the cutoff value. This cutoff score depend on most of the important marketing and financial factors.

Following are the general types of regression scoring which are implemented according to specific need by the organization:

1. Linear Regression Scoring: This type of scoring is performed by implementing linear regression algorithm on the random sample of data. The process includes scoring techniques on variables that have linear dependencies. For example if scoring has to be done on 2 distinct data values and each these values is associated with 5 distinct characteristics, then 25 linear regression analysis are performed.

2. Non-Linear Regression Scoring: This is an extension linear regression scoring process which includes performing scoring by implementing nonlinear regression algorithm on the random sample of data. This means that the algorithm does not perform analysis on direct linear relationships of sample value, and hence the more and specific nonlinear analysis techniques are performed according to the conditions expected.

3. Weighted score tables: This type of scoring does not need any sampling of data before associating scores to prospects. Weighted and important variables are directly associated with sample of prospect to determine individual scores for them without creating historic regression model. This type of scoring is not as accurate as linear or nonlinear regression scoring but it takes less time to perform.

Regression scoring has many advantages over other marketing methods. The primary advantage is that it measures the usefulness of variables that helps in determining which prospect to target and when. Secondly it provides a sophisticated and scientific process to determine the cutoff values or scores for a particular marketing campaign. The outcome of regression scoring helps in enhancing marketing efficiency. The primary and the only disadvantage of regression scoring is that the whole process is very complex and costlier as compared to profiling which is easy to perform.

Even if regression scoring is considered as complex and costly process but it is hardly recommended for an organization to implement regression scoring as it usually targets high marketing efficacy and effectiveness. In acquiring new customers regression scoring is the most capable and important marketing process that yields great results, hence organization should usually compromise on complexity and cost.