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. In a recent
article1 based on a survey of nearly 3000 executives, MIT Sloan Management Review reported
that there is striking correlation between an organization’s analytics sophistication and its
competitive performance. The biggest obstacle to adopting analytics is the lack of knowhow
about using it to improve business performance. Business Analytics uses 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.
1
M S Hopkins, S LaValle, F Balboni, N Kruschwitz and R Shockley, “10 Insights: A First look at The New Intelligence
Enterprise Survey on Winning with Data”, MIT Sloan Management Review, Vol. 52, No. 1, 21–31.
4. Manage business processes using analytical and management tools.
5. To develop ability to use analytics in various functional areas of management.
Course Contents:
Predictive Analytics:
Analysis of transactional data using binary logistic and multinomial logistic regression models
and their applications in different functional areas: Customer classification, Customer churn
prediction; Discrete choice models: Logit and Probit; Non-linear regression.
Classification Trees: chi-squared automatic interaction detection (CHAID), Classification and
Regression Tree (CART).
Forecasting: Trend and Seasonality Estimation, Auto-Regressive Integrated Moving Average
(ARIMA) Models
Panel data regression: Introduction to panel data, fixed and random effects model.
Cases: German Bank credit rating, IPL player selection, in-play betting in football
Marketing Analytics:
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. Hidden Markov Models with applications in retail.
Case(s): Promotions effectiveness in retail, mergers and acquisitions based on CLV
Operations Analytics:
Poisson and compound Poisson process; procurement and asset management decisions, survival
analysis and warranty management.
Multi-criteria decision making: Analytic Hierarchy Process; Data Envelopment Analysis and
their applications in Operations, Marketing and Finance.
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. 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.
Case(s): Supply chain with performance guarantee(s), Dosa King
Social Media Analytics and Big Data:
Analysis of unstructured data, Natural Language Processing, Sentiment analysis.
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
Attendance Required:
75% attendance is compulsory for this course. Students who fail achieve 75% attendance will be
penalized one grade point.
Session-wise Plan:
Session
1
2
3
4
(only a plan!!!)
Predictive Analytics
Introduction to Business Analytics and Intelligence
Reading material(s): Chapter 1 from Davenport, T H., and Harris, J G., “Competing
on Analytics: The new Science of winning”, Harvard Business School Press, 2007.
Analytics: the New Path to values, “MIT Sloan Management Review”, Research
Report, 2010.
Case-let: Challenger Shuttle
Logistic regression and its application
Case-let: IPL Auction
Case-let: German credit applications – Classification of credit applicants
Reading material: Chapter 4 (Logistic Regression) from D T Larose, “Data Mining
Methods and Models”, John Wiley, 2007
Binary Logistic Regression Diagnostics: LR test, wald’s test, hosmer lemshow test,
classification plots. ROC curve
Binary Logistic Regression Diagnostics
5
6
7
8
9
10
11
12
13
14
15
16
Multinomial Logistic Regression & Discrete choice models
Case 1: A game of two halves: in-play betting in football
Probit Models: Model development and diagnostics
Forecasting: Trend and Seasonal Estimation; ARIMA (Auto-regressive integrated
moving average)
Case 2: Harmon Foods Inc
Panel Data Regression: Fixed and Random effects models
Case-let: Board Membership and Corporate Performance
Marketing Analytics
Introduction to Markov Chains
Reading material: Ross, S M, “Introduction to Probability Models”, Academic Press,
2006.
Applications of Markov chain in Marketing : Brand Switching and Market Share
estimation
Case 3: Consumer choice between national and store brand in detergent
purchases
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
Case 4: MNB One Credit Card Portfolio
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.
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.
Operations Analytics
Introduction to Multi-criteria Decision Making: Analytical Hierarchy Process
Case-let: Super Selector
Reading material: Saaty, T L., “How to make decisions: the analytic hierarchy
process”, Interfaces, Vol. 24, 19-43, 1994.
Wind, Y and Saaty, T L, “Marketing applications of Analytic Hierarchy Process”,
Management Science, Vol. 26, No. 7, 1980.
Data Envelopment Analysis
Six Sigma as a Problem Solving Methodology
Reading Material: Dinesh Kumar, U, “Six Sigma: Status and Trends”, in Handbook
of Performability Engineering (Ed. K B Misra)” Springer, 2008.
Case 5: Delivering doors in a window – supply chain management at Hindustan
Aeronautics Limited
Case 6: Dosa King
Social Media Analytics
Natural Language Processing; SMA tools and sentiment Analysis
17
18
19 &
20
Reading Material: R Prabowo and M Thelwall, “Sentiment Analysis a Combined
Approach”, Journal of Informatics, Vol 3, 143-157, 2009
Handling Big Data: Models for big data analysis
Project Presentations
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 McGraw 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.