Stratégie: Business Intelligence & Analytics

By Dr. Anupama Rajesh, Havish Madhvapaty and Vatsal Sahani

Business Intelligence and Analytics (BI&A) has been one of the leading technological trends in recent years and is one of the top most priority technology investments. Enterprises require the support of extensive data processing and analytical techniques to bolster their processes. The book comes at an opportune time to provide a holistic overview of BI&A along with its associated concepts, components, infrastructure etc. It also details its applications in various verticals of management such as Marketing, Finance and HR. This book also discusses relevant software such as Excel, SPSS, R and Eviews. STRATGIE can be an invaluable resource for students, instructors, and practitioners alike.

• Language: English
• Publisher: Partridge Publishing India
• Release date: Jul 6, 2017
• ISBN: 9781543700510

Dr. Anupama Rajesh

DR. ANUPAMA RAJESH PROFESSOR AMITY BUSINESS SCHOOL AMITY UNIVERSITY UTTAR PRADESH, INDIA Dr. Anupama Rajesh is Professor at Amity Business School, Amity University, India. Her qualifications include Ph.D. in the area of Technology in Education, M.Phil. (IT), M.Phil. (Mgmt.), M.Ed., M.Sc. (IT), PGDCA, PGDBA. She has also been trained for Case Writing at INSEAD Paris. She has a teaching experience of about 20 years including international assignments which include a teaching stint in London and Singapore and training of Italian and French delegates and students. She has written more than 40 research papers and case studies for prestigious international journals and has eight books and several book chapters to her credit. She is reviewer of renowned Sage and Emerald journals. Her research interests are Business Intelligence, Educational Technology, Marketing Analytics etc. while her teaching interests are Business Intelligence, E-Commerce, IT enabled processes and so on. She is an avid trainer and has trained Union Bank of India, NHPC, ILFS, TATA Motors, Bhutan Power Company employees as well as Commonwealth Games Volunteers and army personnel. She is a Master Trainer from Microsoft, Infosys Partner for Business Intelligence and Academic Partner for SAP ERM Sim. She has recently won the ADMA Research Award. She has also been awarded Shiksha Rattan Puruskar and won several Outstanding Paper Awards at prestigious conferences at institutes such as IIM Ahmedabad. She also has a MOOC to her credit.

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copyright © 2017 by dr. anupama rajesh, havish madhvapaty, vatsal sahani.

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Contents

PREFACE

DATA MUST INDEED TELL A STORY BUSINESS INTELLIGENCE: THE INTELLIGENT WAY OF DOING BUSINESS

Dr. Anupama Rajesh

1. INTRODUCTION

2. THE EVOLUTION

A. BI&A 1.0

B. BI&A 2.0

C. BI&A 3.0

3. BUSINESS INTELLIGENCE & ANALYTICS (BI&A)

4. ADOPTION DETERMINANTS

5. BI&A FRAMEWORK

A. Big Data

B. Data Warehousing And Mining

A) Data Warehouses

B) Olap Servers

C) Data Mining

C. Analytics

A) Text Analytics

B) Web Analytics

C) Network Analytics

D) Mobile Analytics

D. Reporting

E. Complex Event Processing

6. BI&A IMPLEMENTATION STRATEGY ROADMAP

7. BENEFITS

A. Applications In Various Verticals

a) Marketing

b) E- Governance

c) Health Industry

d) Crime Control

e) Scholarship

8. VENDORS

A. Big Data

B. Data Warehouses

C. Bi Engines / Suites

D. Visualizations

E. Open Source

9. CONCLUSION

Section: Marketing Analytics

MARKETING ANALYTICS

Prateek Mangal

1. INTRODUCTION

A. Contemporary Marketing

2. BIG DATA

A. Customer Data

B. Operational Data

C. Financial Data

3. WHAT IS MARKETING ANALYTICS?

A. Customer Engagement

B. Customer Retention And Loyalty

C. Marketing Optimization

4. BIG DATA ANALYTICS

5. HOW BIG DATA ANALYTICS CAN IMPROVE MARKETING

A. Getting To Know Your ‘Target Audience’

B. Improve ‘Customer Retention And Loyalty’

C. Real Time Personalization

D. Competitor Analysis

E. Using Big Data To Gain Competitive Advantage

F. Use Of Analytics For Pricing Advantage

6. BIG DATA ANALYTICS IN E-COMMERCE

A. Online Marketing Analytics

B. Recommender Systems

C. Product Specific Analytics

D. User Experience Analytics

E. Merchant Analytics

7. WHAT DO CUSTOMERS WANT

A. Conjoint Analysis

a) Adaptive/ Hybrid Conjoint Analysis

b) Choice Based Conjoint Analysis

8. CUSTOMER VALUE ASSESSMENT

A. Historical Clv

B. Predictive Clv

a) Moving Averages

b) Regression

9. USE OF S-CURVE TO FORECAST SALES OF A NEW PRODUCT

10. RESOURCE ALLOCATION

A. The Power Curve

B. The Adbudg Curve

C. The Gompertz Curve

11. PAY PER CLICK ANALYSIS

A. Defining Ppc Advertising

B. Profitability Model For Ppc Advertising

12. SOCIAL NETWORK ANALYSIS

13. VIRAL MARKETING

14. TEXT MINING ANALYSIS

15. USING MARKETING ANALYTICS TO DRIVE GROWTH

A. Anchoring Marketing Analytics To Strategy

B. Making Better Decisions

C. Identify The Best Analytical Approaches

D. Integrate Capabilities To Generate Insights:

E. Put The Analytical Approach At The Heart Of The Organization

16. MARKETING ANALYTICS TOOLS SHAPING THE INDUSTRY

A. Adobe Marketing Cloud (Amc)

B. Google Analytics

C. Mixpanel

17. CONCLUSION

MARKETING ANALYTICS: CASES

Kritika Nagdev

1. INTRODUCTION

2. FACTOR ANALYSIS

A. Factor Analysis Used In Marketing Analytics

B. Case: Two-Wheeler

3. CLUSTER ANALYSIS

A. Case: Fmcg Company

4. MULTI-DIMENSIONAL SCALING

A. Case: Television Company

5. CONCLUSION

6. APPENDIX

Section Financial Analytics

FINANCIAL ANALYTICS

Vatsal Sahani

FINANCIAL ANALYTICS USING EVIEWS

Parul Kumar

1. INTRODUCTION

2. TIME SERIES

3. CREATION OF EVIEWS WORKFILE

A. Normality

B. Stationarity

4. BUILDING A REGRESSION MODEL IN EVIEWS

5. ANALYZING THE MODEL FIT OR RESIDUAL DIAGNOSTICS

A. Normality

B. Residual Graph

C. Serial Correlation Or Autocorrelation

A) Durbin Watson

B) Breusch Godfrey Lm Test

D. Heteroskedasticity

A) Breusch Pagan Godfrey Test

B) White Test

C) Arch Test

E. Multicollinearity

6. CONCLUSION

SectionHR Analytics

HR ANALYTICS

Shweta Shrivastava

1. UNDERSTANDING HUMAN RESOURCE MANAGEMENT

A. Human Resource Management

A) Managerial Functions

B) Operative Functions

C) Human Capital Management

2. UNDERSTANDING ANALYTICS

A. Analytics In Human Resources

B. Why Is Hr Analytics Important?

C. What Does It Do?

D. Hr Metrics Vs. Hr Analytics

3. BALANCED SCORECARD APPROACH

4. HR ANALYTICS: SOME EXAMPLES

Case 1: Pioneering The Cause Of Analytics At Google

Case 2: Accenture’s Use Of Contextual Analytics

Case 3: Application Of Talent Analytics Software At Pepsico

5. HR ANALYTICS: SUCCESS FACTORS

6. HR ANALYTICS: SOME INHIBITORS

7. CRUCIAL ANALYTICS TECHNIQUES

8. MAJOR SERVICE PROVIDERS OF HR ANALYTICS SOFTWARE

9. CONCLUSION

Section Analytics Software

MICROSOFT EXCEL

Havish Madhvapaty

1. FORMULAS AND FUNCTIONS

A. Formula Elements

B. Relative, Absolute And Mixed References

C. Errors

2. CONDITIONAL STATEMENTS

A. Ignoring Errors When Summing

B. Summing The Top N Values

3. RANGES AND TABLES

A. Defining Range

B. Creating Tables

4. LOOKUP FORMULAS

A. Vlookup

B. Reverse Lookup

5. TIME AND DATE

A. How Excel Deals With Dates And Times

B. Displaying Current Date

C. Converting A Nondate String To A Date

D. Determining The Date Of The Most Recent Sunday

E. Summing Times That Exceed 24 Hours

F. Converting From Military Time

6. TEXT FORMULAS

A. Identifying If Two Strings Are Identical

B. Joining Two Or More Cells

C. Changing The Case Of Text

D. Extracting Characters From A String

E. Replacing Text With Other Text

F. Finding And Searching Within A String

G. Counting Specific Characters In A Cell (Uppercase Only)

H. Counting Specific Characters In A Cell (Uppercase And Lowercase)

I. Extracting The First Word Of A String / Extracting First Name

J. Extracting The Last Word Of A String / Extracting Last Name

K. Removing Titles From Names

L. Counting The Number Of Words In A Cell

7. DATA VALIDATION

A. Creating A Drop-Down List

B. Using Formulas For Data Validation Rules

8. PIVOT TABLES

A. Pivot Table Terminology

B. Pivot Table Calculations

C. Changing Pivot Table Elements

D. Creating A Calculated Field Or Calculated Item

E. Slicers And Timeline

F. Referencing Cells Within A Pivot Table

9. ARRAYS

A. A Multicell Array Formula

B. A Single-Cell Array Formula

C. Creating An Array Constant

E. Dimensions Of An Array

F. Working With Array Formulas

G. Expanding Or Contracting A Multicell Array Formula

H. Examples

a) Creating an array from values in a range

b) Performing operations on an array

c) Using functions with an array

d) Transposing an array

e) Generating an array of consecutive integers

f) Counting characters in a range

g) Summing the three smallest values in a range

h) Counting text cells in a range

i) Counting the number of error values in a range

j) Summing the n largest values in a range

k) Count the number of differences in two ranges

l) Determining whether a particular value appears in a range

m) Returning the location of the maximum value in a range

n) Finding the row of a value’s nth occurrence in a range

o) Returning the longest text in a range

p) Summing every nth value in a range

q) Determining the closest value in a range

r) Returning the last value in a column

s) Returning the last value in a row

t) Returning only positive values from a range

u) Sorting a range of values dynamically

v) Returning a list of unique items in a range

10. WHAT-IF ANALYSIS AND GOAL SEEK

A. Types Of What-If

B. Creating A One-Input Data Table

C. Creating A Two-Input Data Table

D. Scenario Manager

E. Goal Seek

11. STATISTICAL FUNCTIONS

A. Using The Analysis Tools

Analysis Toolpak Tools

a) Analysis Of Variance

b) Correlation

c) Covariance

d) Descriptive Statistics

e) Exponential Smoothing

f) F-Test (Two Sample Test For Variance)

g) Moving Average

h) Regression

i) T-Test

B. List Of Statistical Functions

12. FINANCIAL APPLICATIONS

A. Key Concepts

B. Worksheet Functions For Calculating Loan Information

a) PMT

b) PPMT

c) IPMT

d) RATE

e) NPER

f) PV

13. CONCLUSION

14. SHORTCUTS

IBM SPSS

Havish Madhvapaty

1. INTRODUCTION TO SPSS

A. How It Works

B. Help Options

C. File Compatibility

D. Data Output

2. ENTERING DATA: AN EXAMPLE

3. ENTERING DATA

A. Options

B. Recoding Variables

4. GRAPHING DATA

A. Elements Properties Dialog Box

B. Simple Line Charts

C. Charts With Multiple Lines

D. Simple Scatterplots

E. Scatterplots With Multiple Variables

F. Simple Three-Dimensional Scatterplots

G. Grouped Three-Dimensional Scatterplots

H. Grouped Three-Dimensional Scatterplots

I. Simple Dot Plots

J. Scatterplot Matrices

K. Drop-Line Charts

L. Simple Bar Graphs

M. Clustered Bar Charts

N. Stacked Bar Charts

O. Simple Error Bars

P. Clustered Error Bars

Q. Simple Histograms

R. Stacked Histograms

S. Frequency Polygons

T. Population Pyramids

U. Simple Area Graphs

V. Stacked Area Charts

W. Pie Charts

X. Simple Boxplots

Y. Clustered Boxplots

Z. Differenced Area Graphs

Aa. Dual Y-Axes With Categorical X-Axis

5. ANALYSIS

A. Comparison Of Means Analyses

a) Simple Means Compare

b) One-Sample T Test

c) Independent-Sample T Test

d) Paired-Sample T Test

e) One-Way Anova

B. Correlation Analyses

A) Bivariate

B) Partial Correlation

C. Regression Analyses

a) Linear

b) Curve Estimation

6. CONCLUSION

R (PROGRAMMING LANGUAGE)

Vipul Pandey

1. INTRODUCTION TO R

A. Installing R

B. Starting With R Programming

a) Getting Accustomed With The Interface

b) Variables And Data Types

C. Examples On Creating Various Objects

a) Create A Vector With Different Fruits

b) Create A List With Different Elements

c) Create An Array With Different Elements

d) Create A Data Frame With Different Elements.

e) Loops

f) Conditional Statement

D. Reading Data From Excel File

E. Accessing Columns

F. Frequency Calculation

G. Mean And Median

H. Simple Bar Chart

I. Pie-Chart

J. Sentiment Analysis

2. CONCLUSION

ABOUT THE AUTHORS

ENDNOTES

PREFACE

Ubiquitous digitization and connectivity has brought a significant shift in the functioning of organisations. Often the problem is not in the collection of data but to make sense of it.

To combat globalized competition, enterprises strive to create a differentiated position for themselves. They require the support of extensive data processing and analytical techniques to bolster their processes. New tools and techniques are required consistently to support and empower decision-making capabilities. Hence, there has been a phenomenal rise in decision support technologies of which Business Intelligence and Analytics (BI&A) form a substantial part.

IBM Tech Trends Reports have for consecutive years identified Business Intelligence and Analytics as one of the leading technological trends. Research by Gartner (world’s leading IT research firm) also states that BI&A is one of the top most priority technology investments.

The book STRATÉGIE: Business Intelligence & Analytics comes at an opportune time to provide a holistic overview of this very significant technology. The introductory chapter discusses Business Intelligence and Analytics and various associated concepts, components, infrastructure etc. in detail. The subsequent chapters deal with BI&A in various verticals of management such as Marketing, Human Resources, Finance etc.

Marketing analytics is the practice of collecting, managing, measuring and analyzing marketing performance of an organization through certain processes and technologies. Marketing analytics chapter introduces core concepts to the readers giving them working knowledge and understanding of the topic. The readers will also gain by learning the concept of customer analytics and its most relevant techniques used by companies around the world. The techniques are elaborated and explained by way of sample examples for understanding consumer insights and application for market segmentation and positioning.

Financial analytics is the art of putting together data in a derivative and comprehensible view from a large set of financial data to aid decision making. The section on financial analytics discusses key concepts as well as tools used such as econometric modeling, time series, regression analysis etc.

HR analytics simplifies critical human resource challenges and makes the function more quantitative rather than qualitative. The chapter on HR analytics details associated concepts taking examples from the business world. It also discusses the statistical tools most relevant to HR.

Prevalent analytical software is also discussed and their options detailed in an easy-to-understand manner. Excel and SPSS are the most widely used spreadsheet and statistical software respectively. Excel is a very powerful tool to collate and analyze data. The chapter on Excel serves to demystify these capabilities for the user who intends to leverage from Excel’s diverse tools. The chapter on SPSS covers the core essentials of the most relevant tools.

R is an open source software providing a low- cost alternative to expensive proprietary software. It has a command language for various statistical techniques. The chapter deals with its more important features.

This book is, therefore, a comprehensive treatise of analytics basics, software and applications. It aims at providing useful knowledge to students, instructors and practitioners alike.

This book is a result of the best wishes and blessings of my family & friends and above all the Almighty.

signature.jpg

Dr. Anupama Rajesh

DATA MUST INDEED TELL A STORY BUSINESS INTELLIGENCE: THE INTELLIGENT WAY OF DOING BUSINESS

AUTHOR BIO

Dr. Anupama Rajesh

Dr. Anupama Rajesh is Professor at Amity Business School, Amity University, India. She has a teaching experience of over 20 years including international assignments which include a teaching stint in London and Singapore and training of Italian and French delegates and students. She has been trained for Case Writing at INSEAD Paris, and has written more than 40 research papers and case studies for prestigious international journals and has eight books and several book chapters to her credit. She is reviewer of renowned Sage and Emerald journals. Her research interests are Business Intelligence, Educational Technology, Marketing Analytics etc. while her teaching interests are Business Intelligence, E-Commerce, IT enabled processes and so on.

She is an avid trainer and has trained Union Bank of India, NHPC, ILFS, TATA Motors, Bhutan Power Company employees as well as Commonwealth Games Volunteers and army personnel. She is a Master Trainer from Microsoft, Infosys Partner for Business Intelligence and Academic Partner for SAP ERM Sim.

She has recently won the ADMA Research Award. She has also been awarded Shiksha Rattan Puruskar and won several Outstanding Paper Awards at prestigious conferences at institutes such as IIM Ahmedabad. She also has a MOOC to her credit.

1. INTRODUCTION

Ubiquitous digitization and connectivity has brought a significant shift in the functioning of organisations. Increasingly data is being born digital. At times organisations are faced with deluge of data and cases of data overload. Often the problem is not in the collection of data but to make sense of it.

To combat globalized competition, enterprises strive to create a differentiated position for themselves. They require the support of extensive data processing and analytical techniques to bolster their processes. New tools and techniques are required consistently to support and empower decision-making capabilities. Hence there has been a phenomenal rise in decision support technologies of which Business Intelligence and Analytics (BI&A) form a substantial part.

IBM Tech Trends Reports have for consecutive years identified Business Intelligence and Analytics (BI&A) as one of the leading technological trends. Research by Gartner (World’s leading IT research firm) also states that BI&A is one of the top most priority technology investments. This exponential growth of each aspect of these technologies may also be attributed to a sharp decline in the costs of collection and storage of large amounts of data of all aspects of business – for example customer data, inventory data, logistics data, social media data. Associated low connectivity costs have further fuelled this growth. Self-service models of implementation are further catalysing its implementation. User-friendly interfaces such as drag and drop, and point and click functionalities are adding to their popularity. These empower users to perform different analytical operations as per their own requirements without the interventions of the IT department / personnel.

Business Intelligence has always been used as an encompassing terminology for all processes, tools, applications and technologies which can be used for collecting, storing, organising, analysing and reporting data. Hence it is also referenced as Business Intelligence and Analytics (BI&A). It helps to gain useful insights into all kinds of enterprise, customer, user, product data. These then become valuable inputs to better and quicker decision making. With increased pressures of competition on enterprises, and resultant need for agility – almost real-time analytics is required – where the time lag between data collection and analysis is reduced significantly.

2. THE EVOLUTION

BI&A will always be associated with Data Management. Business Intelligence has had a distinct evolution since 1990s, when it was first introduced by Business and IT communities. The term BI was first coined by IBM researcher Hans Peter Luhn in 1958, who defined intelligence as ability to apprehend the inter-relationships of presented facts in such a way as to guide action towards a desired goal. It gained popularity due to Gartner Analyst Howard J Dresner who described BI, in 1989, as a set of concepts and methods to improve business decision making by using fact based support system.

Analytics was given a special mention by Davenport in their very popular book Competing by Analytics. BI&A in its earliest avatar relied largely on structured data stored in Relational Databases obtained from various operational databases across the enterprise. With the advent of Big Data (used to refer to very large and complex datasets of company, sensor, mobile and web data), it is often such kinds of enormous collections of data which are being used for intelligence and analytics.

A. BI&A 1.0

This refers