bUsIness AnaLYtICs
Lesson #1
CHAPTER 1: INTRODUCTION TO BUSINESS ANALYTICS
Business Analytics is the use of:
Data
Information technology
Statistical analysis
Quantitative methods; and
Mathematical or computer-based models
to help managers gain improved insights about their
business operations and make better, fact-based
business decisions.
EXAMPLES OF APPLICATIONS
Pricing
o setting prices for consumer and industrial goods,
government contracts, and maintenance contracts.
Customer Segmentation
o identifying and targeting key customer groups in
retail, insurance, and credit card industries.
Merchandising
o determining brands to buy, quantities, and
allocations.
Location
o finding the best location for bank branches and
ATMs, or where to service industrial equipment.
Social Media
o understand trends and customer perceptions;
o assist marketing managers and product designers.
A VISUAL PERSPECTIVE OF BUSINESS ANALYTICS
IMPACTS AND CHALLENGES
Benefits:
o Reduced costs
o Better risk management
o Faster decisions
o Better productivity; and
o Enhanced bottom-line performance such as
profitability and customer satisfaction
Challenges:
o lack of understanding of how to use analytics
o competing business priorities
o insufficient analytical skills
o difficulty in getting good data and sharing
information
o not understanding the benefits versus
perceived costs of analytics studies.
SCOPE OF BUSINESS ANALYTICS
Descriptive Analytics: shows “what happened”. This
uses historic business data to understand past and
current business performance and make informed
decisions.
Predictive Analytics: purpose is to predict “what is
likely to happen”. Predicts the future by examining
historical data, detecting patterns or relationships in
these data, and then extrapolating these relationships
forward in time.
Diagnostic Analytics: addressed the “why things
happened”. This is used by firms to identify patterns
of behavior and make deep connections within the
data they have collected. To be effective, this must be
detailed, specific, and accurate.
Prescriptive Analytics: purpose is to know “what
should be done next”. It identifies the best alternatives
to minimize business risks or maximize business
objectives. It is however the most costly in terms of
skills required and resources to be used.
EXAMPLE 1.1: RETAIL MARKDOWN DECISIONS
➢ Most department stores clear seasonal inventory
by reducing prices.
➢ Key question: When should price be reduced and
by how much to maximize profits?
Potential Applications of Analytics:
Descriptive Analytics: examine historical data for
similar products (prices, units sold, advertising,
etc.)
