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Transcript of 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