Course Title: Business Analytics and Intelligence Term: Term IV, PGP Course Instructor: U Dinesh Kumar, QMIS Group Introduction: Successful companies have better understanding of their customer, have better insights about the business and as a result create innovative business strategies. Business analytics is a powerful toolbox that helps organizations to get meaningful insights across various activities of the organization. Business Analytics consists of several analytical techniques that can be used to solve business problem by improving the underlying business processes in different business and functional areas. Business analytics tools provide the ability to acquire Business Intelligence and create capability for companies to compete in the market. Business Analytics uses mathematical, statistical, operations research and management tools to drive business performance. This course is designed to provide in depth knowledge of business analytic techniques and their applications in improving business processes and decision making. The tools are grouped under different functional areas with applications in different business domains. Course objectives: The primary course objectives are: 1. Understand the role of business analytics within an organization. 2. Analyse data using statistical techniques and understand relationships between the underlying business processes of an organization. 3. Understand the analytics tools used in different functional areas such as Decision Making, Finance, New Product Development, Marketing and Operations. 4. Manage business processes using analytical and management tools. 5. To develop ability to use analytics in various functional areas of management. Course Contents: Analytics in Decision Making: Analysis of transactional data using logistic and multinomial regression models and their applications in different functional areas; Multi-criteria decision making: Analytic Hierarchy Process; Sequential Decision Making: deterministic and stochastic dynamic programming; Nonlinear programming models and their applications in Operations, Marketing and Finance. Analytics in Marketing: Markov Chain Models in Marketing: Modelling customer relationship as a Markov chain; Brand Switching; Market Share Estimation; Markov model for customer retention; Customer lifetime value estimation; Markov decision process and their application in optimizing customer lifetime value and cross-sell. Analytics in New Product Development: Idea to product; idea selection and concept screening using Pugh matrix; Quality function deployment (QFD) and its application in new product development; life cycle cost and total cost of ownership models for evaluation of new products. Analytics in Operations: Six Sigma methodology for problem solving: DMAIC methodology for problem solving and process improvement; DMADV methodology for design and development of new process and products. Analytics in Finance: Markov chain models in finance: Markov decision process for portfolio allocation; credit rating; Markov chain with absorbing states and applications in bad debt modeling; Random walk: Stock price as a random walk; Brownian motion process: logarithmic and geometric Brownian motion process; Analysis of financial derivatives using Brownian motion process; Black-Scholes model for European call option. Pedagogy: Using interactive lectures and case discussions the students will be introduced to advanced techniques in Business Analytics. The pedagogy is designed to provide real applications of analytical tools using case studies. Evaluation: Mid-term exam: 30% End-term exam: 40% * Group Project : 30% (Each group can have maximum of 4 students. The students are required to show how analytics can be used to solve emerging business problems) Course Cap: Maximum of number of student is 75 Session-wise Plan: Session Session plan 1 Analytics in Decision Making Introduction to Business Analytics and Intelligence Reading material: Chapter 1 from Davenport, T H., and Harris, J G., “Competing on Analytics: The new Science of winning”, Harvard Business School Press, 2007. 2 Logistic regression and its application Case study: German credit applications – Classification of credit applicants 3 Reading material: Chapter 4 (Logistic Regression) from D T Larose, “Data Mining Methods and Models”, John Wiley, 2007 Multinomial Regression Model – Marketing strategy under promotions 4 Case: Indian FMCG Product Introduction to Multi-criteria Decision Making: Analytical Hierarchy Process Case: Marketing applications of AHP Reading material: Saaty, T L., “How to make decisions: the analytic hierarchy process”, Interfaces, Vol. 24, 19-43, 1994. 5 6 Wind, Y and Saaty, T L, “Marketing applications of Analytic Hierarchy Process”, Management Science, Vol. 26, No. 7, 1980. Introduction to Non-linear Programming and its applications Analytics in Marketing Introduction to Markov Chains Reading material: Ross, S M, “Introduction to Probability Models”, Academic Press, 2006. 7 8 Applications of Markov chain in Marketing : Brand Switching and Market Share estimation Reading Material: Styan G P H and Smith, H, “Markov chains applied to Marketing”, Journal of Marketing Research, 50-56, 1964. Application of Markov Chain in estimation of customer retention probability and customer life time value Reading material: Ching, W. K., Ng, M K., and Wong, K K and Altman, E., “Customer lifetime value: Stochastic Optimization Approach”, The Journal of Operational Research Society, Vol. 55 (8), 860-868, 2004. 9 Design of experiments and its applications in Marketing – Marketing ROI Reading material: Almquist, E and Wyner, G, “Boost your marketing ROI with experimental design”, Harvard Business Review, 5-11, October 2001. 10 11 12 Introduction to Markov Decision Process (MDP) Reading material: Ross, S M, “Introduction to Probability Models”, Academic Press, 2006. Applications of MDP: Evaluating effectiveness of consumer and trade promotions Labbi, A., and Berrospi, C., “Optimizing marketing planning and budgeting using Markov decision process: Airline case study”, IBM Journal of Research and Development, Vol 51, No.3-4, 422-433, 2007. Analytics in New Product Development Quality Function Deployment: Understanding the customer and the market 14 Hauser, J and Clausing, D. “House of Quality”, Harvard Business Review, MayJune 1988. Systems approach to new product development, idea to product, concept selection through pugh matrix Life Cycle cost and Total Cost of Ownership and its applications 15 Case Study: BOXN Wagons of Indian Railways Analytics in Operations Six Sigma as a Problem Solving Methodology 13 Reading Material: Dinesh Kumar, U, “Six Sigma: Status and Trends”, in Handbook of Performability Engineering (Ed. K B Misra)” Springer, 2008. 16 17 Case Study: Cargo operations in Hong Kong DMAIC and DMADV Methodology Case: DAMIC in ITeS (IT enables services) Analytics in Finance Introduction to Random Walks and Brownian Motion Process and its applications in option pricing Ross, S M, “An introduction to mathematical finance: Options and other topics”, 18 Cambridge University Press, 1999. Geometric Brownian Motion Process, Black Scholes Option Pricing, Arbitrage theorem. Ross, S M, “An introduction to mathematical finance: Options and other topics”, Cambridge University Press, 1999. 19 Student project presentation 20 Student project presentation Course Reference Material: 1. Almquist, E and Wyner, G, “Boost your marketing ROI with experimental design”, Harvard Business Review, 5-11, October 2001. 2. Ching, W. K., Ng, M K., and Wong, K K and Altman, E., “Customer lifetime value: Stochastic Optimization Approach”, The Journal of Operational Research Society, Vol. 55 (8), 860-868, 2004. 3. Davenport, T H., and Harris, J G., “Competing on Analytics: The new Science of winning”, Harvard Business School Press, 2007. 4. Dinesh Kumar, U, “Six Sigma: Status and Trends”, in Handbook of Performability Engineering (Ed. K B Misra)” Springer, 2008. 5. Gujarati, D N, and Sangeetha, “Basic Econometrics”, 4th Edition, The Mc_Graw Hill, 2008. 6. Hauser, J and Clausing, D. “House of Quality”, Harvard Business Review, May-June 1988. 7. Labbi, A., and Berrospi, C., “Optimizing marketing planning and budgeting using Markov decision process: Airline case study”, IBM Journal of Research and Development, Vol 51, No.3-4, 422-433, 2007. 8. Lin, S X., “Introductory Stochastic Analysis for Finance and Insurance”, WileyInterscience, 2006. 9. Marker, J O, “Studying policy retention rate using Markov chains”, 10. Ross, S M, “An introduction to mathematical finance: Options and other topics”, Cambridge University Press, 1999. 11. Ross, S M, “Introduction to Probability Models”, Academic Press, 2006. 12. Saaty, T L., “How to make decisions: the analytic hierarchy process”, Interfaces, Vol. 24, 19-43, 1994. 13. Styan G P H and Smith, H, “Markov chains applied to Marketing”, Journal of Marketing Research, 50-56, 1964. 14. Wind, Y and Saaty, T L, “Marketing applications of Analytic Hierarchy Process”, Management Science, Vol. 26, No. 7, 1980.