Decision Science in Action

It is desirable for a retailer to have information of the life cycle stage of  a customer. This helps greatly to a retailer in offer optimizations. Targeting right customer with rights offers is greatest challenge for a retailer. They run hundreds of promotions and optimizing it saves huge cost and helps in retaining customers.  In the era of super markets, large retailers issue loyalty cards and maintain customer purchase data. But most of the demographic data is not available with them as customers normally do not fill these fields and leave them blank. Although customer demographic data are not available using purchase data certain life stages of customer can be predicted using rule base techniques or predictive modeling. Life stages bring changes in purchasing pattern. 1.  A single male will mostly buy his needs and daily eatables.  Eating habits gets changed if he is married etc.  A man can occasionally buy woman's product but it will be regular if ...

Product propensity models are developed to identify   the  customers or observations those  have high likelihood of responding to any cross-sell campaign or any event of interest.  Model d evelopment which includes has multiple steps requires extensive efforts. 1. Identification of business objective or business problem. 2. Identifying analytical objective  3. Identifying data scope and time window.  4. Data preparation and exploration 5. Data treatment and variable reduction 6. Model training and validation 7. Model selection and interpretation  8. Business approval  Steps to automate the model scoring for its periodical usage. 9. Model deployment  10. Periodical Model Scoring  Once the models are scored and used in the campaign for targeting customers to cross sell the product of interest, effectiveness of model must be measured. I am going to discuss the different ways to ev...

Multicollinearity is defined as the linear relationship between two or more independent variables while performing regression analysis between a dependent variables and set of independent variables. Multicollinearity presents a severe problem during regression modelling. Inclusion of independent variables having linear relationship with each other leads to parameter estimation with higher standard error. This in turn leads to inaccurate parameter estimation. Furthermore, due to inaccurate parameters regression model becomes unstable. The unstable models performs badly on the validation and test samples. When model is unstable, its performance deteriorate very fast compared to stable model over the period, though model is scored on the data of sample of same population. In such a situation an analyst must investigate for the multicollinearity, before finalizing the model. Next question is how to investigate and which variable should be kept if some variables are found to ...

In the statistics, we use data to derive the information. It helps in business, research and governance.  We also develop to predict the value or behaviour of any dependent variable based on the historical data. We have following categories of the model based on the type of the variable When the dependent variable is continuous variable: 1.OLS  Linear Regression Model :  When the dependent variable is continuous variable and independent variables is/are continuous variable(s) 2.ANOVA:  When the dependent variable is continuous variable and independent variables is/are categorical variable(s) 3.ANCOVA :  When the dependent variable is continuous variable and as independent variables, we have   continuous  as well as categorical variables as independent variable. When the dependent variable is categorical variable: 1. Maximum Likelihood Logistic Regression Model :  When the dependent variable is categorical variable and independent ...