The document discusses the increasing reliance of companies on business analytics (BA) to gain a competitive edge, driven by the demand for analytical talent to translate data into actionable insights. It highlights the rapid growth of BA due to the low cost of data capture and the shift towards data-driven decision-making, emphasizing the importance of data-savvy managers. Additionally, it covers various data mining strategies used by major companies to improve customer engagement and business outcomes.

1. Datamining and Business
Analytics
BIKRAM GHOSH
MOORE SCHOOL OF BUSINESS

2. An Exciting time for BA folks…
— “Companies are increasingly turning to analytics to
gain a competitive edge. As they do, they must
resolve unique demands on their information
technology, their structure, their processes, and their
culture. Most critical, however, is the
challenge posed by analytical talent, the
people at all levels who help turn data into
better decisions and better business results.”
÷ Accenture, Counting on Analytical Talent (emphasis added)

3. Why is BA experiencing such rapid growth?
— Data has become extremely inexpensive to capture and store
— However, turning that data into managerial insights is not easy
¡ In fact, it is a skill that is in huge demand
— Exciting shift in business: less decision making based on HIPPO,
more data-driven (LinkedIn, Google, etc.)
— Key player in this shift: the “data-savvy manager” (McKinsey)
— IBM executive: “Business Analytics is our number one recruiting
need…across the entire corporation…for the coming several years”
— Similar initiatives at Deloitte, Accenture, Ernst & Young

4. Datamining
— Finding hidden patterns in data
— Usually it starts with a target problem – increase sales,
reduce churn, increase profitability, reduce marketing
costs, increase cross-selling, customer segmentation etc..
— Then you variables that have appreciable affect on the
target variable
— You build a model to predict the future

5. Extent of Involvement of The Three Main Groups
Participating in a Data-Mining Project
Get
Raw
Data
Identify
Relevant
Variables
Gain
Customer
Insight
Act
Objectives
Raw
Identify
Relevant
Variables
Customer
Insight
(Re)Define
Business
Groups
1. Business
2. Data Mining
3. IT

6. Objective: Customer based strategies
Prod
C
Customers reached
Customer
Needs
Satisfied
1 to 1© Strategies
Traditional Marketing
.
Place Prom
Price

7. Implementation
— IDIC
¡ Identify
¡ Differentiate
¡ Interact
¡ Customize

8. Success stories
— Capital one
• The moment a customer calls them they know they
behavioral traits of the customer, try to predict the likely
reason to call, routes the call accordingly, at the same time
tries to see what other products the customer may potentially
buy.
• Test and learn strategies
• Their stock price looks like an internet start up

9. Moore school – CMS study
— Ingersoll Rand
• consumption modeling
• Model for part sales – find out the opportunity segment

10. • American Airlines
• Fidelity
• Dell
• Caterpillar
• McDonald’s
• MSB

11. Commonly used methods and softwares
— Methods depends on the type of target you have.
o Regressions, decision tree algorithms, machine learning
techniques, artificial neural networks….
— SAS, IBM – SPSS, ERP systems such as SAP- BI.

12. Where are we heading?
— Social CRM and Datamining
— Global CRM and Datamining
— Use of Datamining in B-to-B.