• S – 36 LALIT MOHAN THURIMELLA

• S - 41 MANOJ KUMAR• S – 82 SUNIL KUMAR• S – 52 P SIVIAH• S - 94 SOMESH GILANI

INTRODUCTION

DEFINITION, DESCRIPTION & BUSINESS APPLICATIONS

DRIVERS FOR PREDICTIVE ANALYTICS

PREDICITVE ANALYTICS VS FORECASTING

PREDICTIVE MODELLING

GAZING AT FUTURE

FUTURE IN OUR HANDS

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

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?

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

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

MARKETINGCUSTOMER

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

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

Based on survey: TDWI 2012

Based on survey: TDWI 2012

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

DECISION TREES

Process of predicting a future event based on historical data

Educated Guessing

Underlying basis of all business decisions Production

Inventory

Personnel

Facilities

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.

• The term "forecasting" is used when it is a time seriesand we are predicting the series into the future. Hence"business forecasts" and "weather forecasts".

• Prediction is the act of predicting in a cross-sectionalsetting, where the data are a snapshot in time (say, aone-time sample from a customer database).

• Here you use information on a sample of records topredict the value of other records (which can be avalue that will be observed in the future).

• 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.

• 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.

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.).

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

sample observations”

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

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

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

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

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