analytics techniques

7/29/2019 analytics techniques

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ANALYTICS PPT


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DEFINITION

Analytics is the analysis of data emanating fromorganizations as part of its business process to

generate actionable insights to have better

informed decisions to gain competitive

advantage.


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ANALYSIS

Mathematical

MachineLearning

Pattern recognition


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APPLICATIONS

BFSI

Whom to lend?

Who are the profitable customers?

How much to lend? Retail

Recency frequency monitoring

Loyal customers

Buying pattern


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Telecom

customer satisfaction and retention percent

Profitable customers?

No of profitable users added?

PharmaDrug lifecycle?


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Other applications

Supply Chain

Marketing

HR

IT services


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Types of analytics company

.Captive .Extensions .Standalone


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Various Techniques of Analytics

Exploratory Data Analysis

Attribution Modelling

Forecasting

Predictive Modelling Classification

Design of Experiments

Optimization and Simulation

Text Mining


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Exploratory Data analysis

It helps to get an initial feel of the data

EX: Count, Min, Max, Sum, Range, Std and

Variance, Mean

Types:

1)Character 2)Numeric

3)Data 4)Binary

5)Ordinal/Ranking 6)Mutually exclusive

7)Open Text


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Typesof Data sets

1) Time Series

Compared a variable across time2) Cross sectional

Compare one or more variable at an instant

across diff.

sections

3) Pannel

Consists of both Time series and Cross

SectionalComparison of 2 variables

2 character variable(Cross Tab)

1 Char and 1 Num (Summary over class)

2 Numeric Corelation


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Attribution Modelling It is used in situations where there are KPIs and

we want to know what influences them

Market Mix Modelling(Application)

It is the attribution model too estimate

contribution of the elements of marketing mix andenvironmental factors to sales and market share

1) Product

2) Price

3) Place Marketinginputs/mix

4) Promotion

5) Packaging


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6) Season

7) Trend Marketing

Evironvent8) Competition

KPIs

1) Sales

2) Market share etc

Terminologies in Maket mix modeling

Vintage: how long ie Last day-First day

Granuality: Difference ie daily, monthly etc

Cut-off Date:


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Kinds of Promotions

1) Value Promotions

2) Volume Promotions

3) Kind Promotions


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Credit Risk Modeling

Uses logistical regression technique

This model uses the logistical regression model to

find out the credit worthiness of a customer

EX- A bank customer

BASEL- 8% of risk weighed asset

DPD- Date past due


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Predictive Modeling

Insurance is about protecting yourself fromfinancial loss due to an unforeseen event.

It is a matter of solicitation

A person transfers the risk of loss by paying

premium

When should we get insured?

When we get job

When we buy homeWhen we get married


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Types of policies

Term No savings component(pure risk)

Whole Life Whenever you die, nominee getsmoney

Endowment Saving (Bond market)+ Insurance.Gets money at end of term

Unit Linked Savings(stock market) + Risk.Gets money at end of term

Pension Risk + Bond/Stock

Premium calculation : Prob. of Death*Expectedloss


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Logistic Regression: using log of odds of claim

Whether the policy will have a claim

Likelihood of event Probability

Odd(Fav./Unfav.)

HazardLog(odds) = a0 +a1x1 + .anxn

The logarithm of odds is a linear combination of

explanatory variables(provided by insurance

company)


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Stock markets

Mutual fund Mgt. = Wealth mgt.

Share = part ownership of a company

Fundamental Analysis = Looking at the basics of

company

Technical Analysis = Speculator and traders

Expected return of stock- Mean of return over a

period of time(Ri)

Risk- Std deviation of Return


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Factor Analysis

Classification Tech. based on correlation

It puts similar stocks in the same category

Eigen Value-Amt. of information a particularfactor contributes to the overall knowledge. More

the value, better is factor

Simulation- To mimic real data

Monte Carlo Simulation- Simulates data based

on data like people walking in the store.


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THANK YOU

analytics techniques

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