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: Customer classification, Customer churn prediction;
Non-linear regression; Bayesian Inference.
Multi-criteria decision making: Analytic Hierarchy Process; Sequential Decision Making; 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 (CLV) estimation: applications of CLV, effectiveness of promotions and strategy; Markov
decision process and their application in optimizing customer lifetime value; policy iteration and
value iteration algorithms.
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 methodologies 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:
End-term exam:
Group Project*:
30%
40%
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
Case: Challenger Shuttle
Logistic regression and its application
Case study: German credit applications – Classification of credit applicants
3
4
Reading material: Chapter 4 (Logistic Regression) from D T Larose, “Data
Mining Methods and Models”, John Wiley, 2007
Logistic Regression Diagnostics; Multinomial Regression Model – Marketing
strategy under promotions
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
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
6
Meta Heuristics Models
Analytics in Marketing
Introduction to Markov Chains
Reading material: Ross, S M, “Introduction to Probability Models”, Academic
Press, 2006.
7
Applications of Markov chain in Marketing : Brand Switching and Market Share
estimation
8
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); policy and value iteration
algorithms.
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
Case Study: Cargo operations in Hong Kong
DMAIC and DMADV Methodology
17
18
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”,
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.