business analytics

1. • S – 36 LALIT MOHAN
THURIMELLA
• S - 41 MANOJ KUMAR
• S – 82 SUNIL KUMAR
• S – 52 P SIVIAH
• S - 94 SOMESH GILANI

2. INTRODUCTION
DEFINITION, DESCRIPTION &
BUSINESS APPLICATIONS
DRIVERS FOR PREDICTIVE
ANALYTICS
PREDICITVE ANALYTICS VS
FORECASTING
PREDICTIVE MODELLING
GAZING AT FUTURE
FUTURE IN OUR HANDS

4. IS A DATA SCIENCE
A MULTIDISCIPLINARY SKILL SET
ESSENTIAL FOR SUCCESS IN
BUSINESS, NONPROFIT
ORGANIZATIONS & GOVERNMENT
INVOLVES SEARCHING FOR MEANINGFUL RELATIONSHIPS AMONG VARIABLES & REPRESENTING
THOSE RELATIONSHIPS IN MODELS

5. RESPONSE
VARIABLES
• THINGS WE ARE
TRYING TO
PREDICT
EXPLANATORY
VARIABLES OR
PREDICTORS
• THINGS WE
OBSERVE,
MANIPULATE, OR
CONTROL THAT
COULD RELATE TO
THE RESPONSE
VARIABLES MODELS
REGRESSION
•PREDICTING A
RESPONSE WITH
MEANINGFUL
MAGNITUDE
•QUANTITY SOLD, STOCK
PRICE, OR RETURN ON
INVESTMENT
CLASSIFICATION
•PREDICTING A
CATEGORICAL
RESPONSE
•WHICH BRAND WILL BE
PURCHASED?
• WILL THE CONSUMER
BUY THE PRODUCT OR
NOT?
• WILL THE ACCOUNT
HOLDER PAY OFF OR
DEFAULT ON THE LOAN?
•IS THIS BANK
TRANSACTION TRUE OR
FRAUDULENT?

13. FORECASTING SALES
FOR MARKET SHARE
FINDING A GOOD
RETAIL SITE OR
INVESTMENT
OPPORTUNITY
IDENTIFYING
CONSUMER SEGMENTS
AND TARGET MARKETS
ASSESSING THE
POTENTIAL OF NEW
PRODUCTS OR RISKS
ASSOCIATED WITH
EXISTING PRODUCTS
USES

16. MOST ORGS APPLY PA TO CORE
FUNCTIONS THAT PRODUCE
REVENUE USE PA TO INCREASE
PREDICTABILITY
USE PA TO CREATE NEW
REVENUE OPPORTUNITY
OF ORGS USE PA FOR CUSTOMER
SERVICES
TOP 5 SOURCES OF DATA TAPPED FOR PA
SALES
MARKETING
CUSTOMER
PRODUCT
FINANCIAL
COMPANIES USE
SOCIAL MEDIA
DATA
USE RESULTS OF PA FOR
PRODUCT
RECOMMENDATIONS AND
OFFERS
ASSERT THAT PA WILL HAVE MAJOR
POSITIVE IMPACT ON THEIR ORG
OF ORG WHO USE PA HAVE REALIZED A
COMPETITIVE ADVANTAGE
WITH REAL TIME PA YOU CAN MAKE SURE
YOUR COMPANY DOESN’T MISS IT’S
WINDOW OF OPPORTUNITY

19. CUSTOMER-RELATED ANALYTICS
SUCH AS RETENTION ANALYSIS
AND DIRECT MARKETING
• PREDICT TRENDS
• UNDERSTAND CUSTOMERS
• PREDICT BEHAVIOUR
• PROVIDE TARGETED PRODUCTS
• COMPETITIVE DIFFERENTIATOR
• REDUCE FRAUDS
BUSINESS PROCESS REASONS
• PREDICTIVE ANALYTICS TO
DRIVE BETTER BUSINESS
PERFORMANCE
• DRIVE STRATEGIC DECISION
MAKING
• DRIVE OPERATIONAL
EFFICIENCY
• IDENTIFY NEW BUSINESS
OPPORTUNITIES
• FASTER RESPONSE TO
BUSINESS CHANGE

20. Based on survey: TDWI 2012

21. Based on survey: TDWI 2012

22. LACK OF
UNDERSTANDING OF
PREDICTIVE
ANALYTICS
TECHNOLOGY
LACK OF SKILLED
PERSONNEL
INABILITY TO
ASSEMBLE
NECESSARY DATA—
INTEGRATION ISSUES
NOT ENOUGH
BUDGET
BUSINESS CASE NOT
STRONG ENOUGH
INABILITY TO
ASSEMBLE
NECESSARY DATA—
CULTURAL ISSUES
THE TECHNOLOGY IS
TOO HARD TO USE

23. DECISION
TREES

25.  Process of predicting a future
event based on historical data
 Educated Guessing
 Underlying basis of
all business decisions
 Production
 Inventory
 Personnel
 Facilities

26. FORECASTING
• Predict the next number
a) 3.7, 3.7, 3.7, 3.7, 3.7, ?
b) 2.5, 4.5, 6.5, 8.5, 10.5, ?
c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?
Forecasting is the process of making statements about
events whose actual outcomes (typically) have not yet
been observed.
A commonplace example might be estimation of some
variable of interest at some specified future date.

27. • The term "forecasting" is used when it is a time series
and we are predicting the series into the future. Hence
"business forecasts" and "weather forecasts".
• Prediction is the act of predicting in a cross-sectional
setting, where the data are a snapshot in time (say, a
one-time sample from a customer database).
• Here you use information on a sample of records to
predict the value of other records (which can be a
value that will be observed in the future).

28. • Predictive analytics is something else entirely, going
beyond standard forecasting by producing a
predictive score for each customer or other
organizational element.
• In contrast, forecasting provides
overall aggregate estimates, such as the total
number of purchases next quarter.
• For example, forecasting might estimate the total
number of ice cream cones to be purchased in a
certain region, while predictive analytics tells
you which individual customers are likely to buy an
ice cream cone.

30. • Prediction is generally more about classification problems. In
sales, these could be at different stages of the customer
lifecycle.
– At acquisition stage - Predict whether you could be my
potential customer.
– At service stage - Predict whether you would buy my cross-
sell/up-sell offer.
– At the retention stage - Predict whether you would remain
my customer or not.
• Forecasting is more about understanding how my sales would
be given the historic trend, seasonal effects (if at all) etc etc.
Both are very different and different predictive techniques are
applied to solve each of the above problems.

31. Prediction is a generic term for gaining future knowledge on
diverse aspects using diverse predictive techniques and diverse
methods (e.g. numeric forecasting, predicting purchase patterns,
predicting attrition causes in sales decline)
Forecasting is jut one of multiple predictive methods, usually
referred to predicting the future state of a variable in a defined
future time (sales revenue for the next X months, cost structure
for the following year, etc.).

32. “Forecasting is about out-of-sample
observations while prediction is about in-
sample observations”

33. …process by which a model is created or chosen
to try to best predict the probability of an
outcome

34. Predictive modelling is a process used in predictive analytics to
create a statistical model of future behaviour
Fundamentals of Predictive Modelling
• Data Collection
• Data Extraction/transformation
• Predictive Model
• Business Understanding

35. Functionality Algorithm Applicability
Classification Logistic Regression
Decision Trees
Naïve Bayes
Support Vector Machine
Response Modeling
Recommending “Next likely
product”
Employee retention
Credit Default modelling
Clustering Hierarchical K-means Customer segmentation
Association rules Apriori Market Basket analysis

36. Regression analysis to predict the result of a categorical dependent variable based on one
or more predictors or independent variables
Useful to analyze and predict a discrete set of outcomes like
• success/failure of new product
• Likelihood of customer retention/loss
Logistic Regression, the connection between the categorical dependent variable and
the continuous independent variables is measured by changing the dependent
variable into probability scores
Y = b0 + b1x1 + b2x2 + ……………………….. + bkxk + E
Y = Dependent variable
b0 = Constant
b1 = Coefficient of variable X1
x1 = Independent Variable
E = Error Term

37. • Seven reasons you need predictive analytics today: Eric Segal, PhD
• Predictive Analytics for Business Advantage. Fern Halper
• www.predictionimpact.com
• Wikipedia
• www.slideshare.com