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SUB Version SalesAna
| Question | Answer |
|---|---|
| Sales Analytics is an approach to find solutions for major sales management and selling decisions based on decision makers’ experience and intuition. | False |
| The direct purpose of using Sales Analytics is to generate insights that customers can use when they make purchase decisions. | False |
| When and how to use a linear regression to investigate a sales decision? | when dependent variable is continuous |
| Variables have specific roles | - Dependent variable - Independent variables (also called predictors) |
| Use SPSS to run a regression (Data preparation) | categorical variables and interpretation |
| Use SPSS to run a regression (Log-transforming variables and interpretation) | elasticity |
| Recommend best month of a year to release a movie after controlling for two variables: production budget & number of competing movies. Linear regression used. Movie revenue follows a normal distribution. Y variable (dependent variable)? | dollar amount movie revenue |
| Please recommend what data preparation for the Month variable that the student may want to do before running a regression. | turn the months into dummy variables |
| Plotted data and regression line A (dots are closer to line) | has a higher R square |
| Plotted data and regression line B (dots are farther from line) | the relationship between the dependent and independent variable is stronger |
| If we find that an independent variable X has a positive and statistically significant effect on the outcome variable Y in a regression, we can conclude that X causes Y | False |
| An analyst uses all the available data to do a regression analysis without thinking through relationship between the variables is an example of fisher expedition fallacy | True |
| A sales analyst should consider log-transforming an independent variable if the variable has a right-skewed distribution | True |
| When and how to use a logistic regression to investigate a sales decision? | When the dependent variable is binary |
| What is Sales Analytics | A technology-enabled and model-supported approach to harness customer and market data to support sales force needs, diagnose problems and opportunities, and design solutions for major sales management and selling decisions. |
| Four Ways That Sales Analytics Add Value | 1. support sales machine operation 2. diagnose concerns/discover new opportunities 3. design sales force effectiveness drivers 4. partner with sales leaders |
| If a dependent variable is continuous | it is a linear regression model |
| If a dependent variable is binary | it is a logit model |
| Dependent variable Sales Revenue = B0 + B1 Churn+ E0 | Sales revenue=outcome variable= dependent variables |
| Independent variable Sales Revenue = B0 + B1 Churn+ E0 | B1 Churn..B2....= independent variables - As Churn increases by 1 unit, sales revenue goes up by x units |
| R-Square | ability of the independent variables to explain the variance in the dependent variable - It lies between (0-1) or (0%-1%) - Higher the value of R^2, the better the model is |
| F-Statistics | If its >0.05, then the model doesn’t make sense and can be disregarded - if it is less that 0.05 then look at R squared for more info |
| P-Values | explains if makes sense or not for each variable |
| How to evaluate P-Values | A p-value less than 0.05 (typically ≤ 0.05) is statistically significant |
| How salespeople spend their time: sales generated=B0+B1 meetings+B2 Calls+B3 Training +E0 - what are the sales when salespeople spend time on meeting alone? | B0+B1 meetings |
| How salespeople spend their time: sales generated=B0+B1 meetings+B2 Calls+B3 Training +E0 - what is the sales performance when salespeople spend time on calls and training but not meetings? | B0+B2 calls+ B3 training |
| In a regression model: the variable having the highest standardized score | will be the most impactful |
| Regression Analysis | an approach to identify the magnitude and direction of effects of a set of variables on an outcome variable |
| The biggest issue in sales | -Churn (turnover) -Resource allocation (salesperson at performance level) |
| Correlation: | tells us the relationship between two variables or positions -Cor(Sales Rev, Churn)= -96 -p>0.05 (if it makes sense) |
| Sales Revenue = B0 + B1 Churn+ E0 what is B0? | the estimate/slope - if you don't have churn in the company, what will be your sales revenue? |
| Sales Revenue = B0 + B1 Churn+ E0 what is E0? | accounts for errors in the model |
| Regression on excel | Input Y range=dependent variables P value explains if makes sense or not for each variable Significance f shows if whole model makes sense1 |
| Logistic Regression Model (logit model) | - DV is binary or dummy or dichotomous meaning (0/1)(yes or no) - A method that you use to predict a customer decision |
Created by:
kennedyspang