MBA Question Bank - Analytics For Marketing Decisions PDF

**Question Bank -- Analytics for Marketing Decisions** **Module 1** 1. Elaborate why are organizations moving towards use of Analytics in business decisions? 2. How are organizations using analytics to develop competitive advantage? 3. Explain the factors driving massive adoption of a...

**Question Bank -- Analytics for Marketing Decisions** **Module 1** 1. Elaborate why are organizations moving towards use of Analytics in business decisions? 2. How are organizations using analytics to develop competitive advantage? 3. Explain the factors driving massive adoption of analytics. / Discuss the reasons for the explosion in the use of Analytics for business decisions? 4. Examine the different decision models used for decision making process? 5. Discuss the steps involved in data driven decision making. 6. Examine the decision Models for decision Making in the following cases a. Monty Hall Problem b. Russian Roulette 7. **Differentiate between traditional and smarter approach to Analytics.** 8. **Define Business Analytics with a Venn diagram.** 9. **Discuss the role and skills of Data Engineers, Data Scientists software engineers and data storytellers.** 10. **Discuss the specific analytics used by companies in any one of the following industries** c. **Airlines** d. **Consumer Packaged Goods** e. **Banking and Financial Services** 11. **Compare and contrast data and information** 12. Differentiate with illustration a dataset and database. 13. Classify Business Analytics into 3 categories of Descriptive, Predictive and Prescriptive and discuss each one of them OR Compare and contrast different types of Analytics. **Module 2** 1. Why are aggregate forecast more accurate? Discuss with suitable example using Mean and Standard Deviation (SD). 2. Explain in brief the subjective and objective approaches to forecasting 3. Discuss the tradeoff between bias and accuracy. 4. Explain the three different types of measuring forecasting error. 5. Discuss the different components of time series with summative, multiplicative and mixed models 6. Discuss the following Models in forecasting demand for a Product with their merit and demerit a. Naïve b. Moving Average c. Exponential Smoothing 7. Monthly Demand at an electronics retailer for LED Televisions is as follows: Month Demand (Units) ------------- ---------------- Jan 2023 1000 Feb 2023 1113 March 2023 1271 April 2023 1445 May 2023 1558 June 2023 1648 July 2023 1724 August 2023 1850 Sept 2023 1864 Oct 2023 2076 Nov 2023 2167 Dec 2016 2191 8. Explain seasonality with graph and suitable examples of annual, monthly, weekly and daily seasons. 9. You need to develop monthly forecasts (in pallets) for item \#VM11984 that seems to have an upward trend. Suppose we are in time 101 and looking at past year's data, you have determined that alpha=0.3 and beta=0.1 ----- ----------------------------- ---------------------------------------------------- ---------------------------------------------------- --------------------------------- t [*X̂*]{.math.inline}**~t~** [\$\\widehat{\\mathbf{a}}\$]{.math.inline}**~t~** [\$\\widehat{\\mathbf{b}}\$]{.math.inline}**~t~** [*X̂*]{.math.inline}**~t,t+1~** 100 92 90 8.5 98.5 101 95 ----- ----------------------------- ---------------------------------------------------- ---------------------------------------------------- --------------------------------- 10. We observe that demand is level with seasonality by day of week. It is now Friday (t=104) and we have estimated the level ([*â*]{.math.inline}~t~) to be 121 burgers. The current daily seasonality factors are shown to the right and smoothing factors α = 0.01 and ϒ =0.05. What is your forecast for Monday ([*X̂*]{.math.inline} ~104,105~) ----------- ----------- ----------------------------- ---------------------------------------------------- ---------------------------------------------------- --------------------------------- **Alpha** 0.1 **Gamma** 0.05 **t** **DOW** [*X̂*]{.math.inline}**~t~** [\$\\widehat{\\mathbf{a}}\$]{.math.inline}**~t~** [\$\\widehat{\\mathbf{F}}\$]{.math.inline}**~t~** [*X̂*]{.math.inline}**~t,t+1~** 100 Monday 0.50 101 Tuesday 0.75 102 Wednesday 1.25 103 Thursday 1.00 104 Friday 121 1.50 105 Monday 76 ----------- ----------- ----------------------------- ---------------------------------------------------- ---------------------------------------------------- --------------------------------- **Module 3** 1. Appraise the Predictive Analytics and modelling with a suitable example. 2. Explain in brief why is regression analysis necessary in business. 3. Discuss the steps involved in multiple linear regression. 4. Explain the assumptions of multiple linear regression model. 5. How do you check the validity of the assumptions both visually and through statistical testing? 6. Are the regression assumptions of linearity and homoscedasticity satisfied in the below figures? Explain 7. Explain the following terms used in marketing mix modelling a. Decay Effects b. Saturation Effects 8. Discuss the use of regression in analysis of a Marketing Experiment. 9. The marketing manager of DataCom Inc. wants to predict the annual revenues generated by its customers given certain characteristics of them. The manager runs a regression model on Years of Loyalty, Years Employed, Years of Marriage, Gender and Average Number of Products Purchased. The output of regression model is given below: ***Regression Statistics*** \* * ----------------------------- ---------- Multiple R 0.874778 R Square 0.765237 Adjusted R Square 0.735892 Standard Error 5652.67 Observations 46 **ANOVA**           ------------ ------ --------------- ------------- ------------- ------------------ \* * *df* *SS* *MS* *F* *Significance F* Regression 5 4166144430.17 833228886 26.07696863 0.00 Residual 40 1278106972.78 31952674.32     Total 45 5444251402.96       \* * *Coefficients* *Standard Error* *t Stat* *P-value* *Lower 95%* *Upper 95%* -------------------------------------- ---------------- ------------------ -------------- ----------- -------------- ------------- Intercept 25801.11 2488.444433 10.36836776 0.00 20771.77322 30830.44083 Years of Loyalty -208.634 237.4500522 -0.878644716 0.38 -688.5386908 271.2702232 Years Employed 638.4924 141.0373752 4.527114617 0.00 353.4451946 923.5395309 Years of Marriage 1604.962 408.1861998 3.931935175 0.00 779.9865937 2429.93676 Gender (Female) -1627.26 1753.634545 -0.927935442 0.36 -5171.487268 1916.967976 Average Number of Products Purchased 183.2034 97.86866728 1.871931586 0.07 -14.59650537 381.0034045 a. Build the model to predict the annual revenues of the company. b. Interpret the model output in terms of R^2^. c. According to the model, is there a difference between the mean revenues earned by males & female customers at DataCom? Justify your answer. d. Predict a 95% confidence interval of the annual revenue of a female customer who has been purchasing for last 10 years; is married for 8 Years and is employed for 12 years.