Overview business analytics 6th june 2020 v5

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© BMGI. Except as may be expressly authorized by a written license agreement signed by BMGI, no portion may be altered, rewritten, edited, modified or used to create any derivative works.
Business Analytics Overview
6th June 2020
Anurag Seksaria
anurags@BMGIndia.com

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Session Plan
1. About BMGI
2. Why Business Analytics & Need for Structured Approach
3. Introduction to Customer Segmentation Case
4. SCRUB, EXLPLORE & MODEL Data – Through Case Study
5. INTERPRET & Manage Change
6. AI & ML Opportunities
7. Unstructured Analytics
8. Software Tools Comparison
9. Expanding to Multiple Areas & Case Studies

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OUR MISSION
“To partner with our clients for unlocking their inherent potential
through innovative solutions and delivering breakthrough results”

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BMGI OVERVIEW
INSOURCING
LEADERSHIP
OBJECTIVITY
INSIGHT
GLOBAL
EMPLOYEES100+
GEOGRAPHIES
SERVICED16+
CLIENTS
SERVICED200+RESULTS
BMGI enables businesses to solve strategic, organizational and process problems.
COMPETENCIES
§ Innovation and Design
§ Customer Insighting
§ Strategy Planning & Deployment
§ Problem Solving
§ Process Improvement
INDUSTRIES
• Services.
• IT / ITeS / BPO
• Education
• BFSI
• Hospitality
PUBLICATIONS
§ The Innovator’s Toolkit
§ Insourcing Innovation
§ The Complete Idiot’s Guide
to Lean Six Sigma
§ Design for Lean Six Sigma
Manufacturing
• Pharma
• FMCG
• Chemicals
• Automotive

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BMGI’S Services For Enabling Business Success
StrategizeInnovate
Solve Problems Transform Business

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Anurag Seksaria – Business Head

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WHAT CAUSED THE CHANGE
Significant Reduction in
Accidents due to drunken driving
30% Move from Self
Driving to UBER
Local Police has become
Tough on Drunken Drivers
Drinking itself has reducing

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Why UPS Trucks Never Take Left Turn

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§ Business analytics (BA) refers to the skills, tools & techniques,
practices for continuous iterative exploration and
investigation of past business performance to gain insight and
drive improvements
§ Business analytics is based on data and statistical methods.
What is Business Analytics?

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Machine Learning – Training – Testing - Validation
Original Set
Training Set
Training Set Testing Set
Testing Set
Validation. Set
Machine Learning
Algorithmic
Predative Model

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Four Pillars of Business Analytics Studies
Ø Descriptive Analytics - What Happened?
Ø Diagnostic Analytics - Why Did it Happen?
Ø Predictive Analytics - What Happens If?
Ø Prescriptive Analytics - How to Make it Happen?

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üHow much volume did we sell last month in the GREEN ZONES?
üHow many of our consignments reached/arrived late?
üHow much was the delay in receiving the payments?
üHow much is the error rate or rejection rate?
Descriptive Analytics - What Happened?

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üA sudden drop/increase in sales?
üSudden increase in customer returns or Internal Rejection Rates?
üHigher number of employee taking leave?
Look for cause and effect to illustrate why something occurred
Techniques include classification and regression analysis (CART)
Diagnostic Analytics - Why Did it Happen?

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Beer and Diapers

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Beer and the Diapers
v Wal-Mart decided to combine the data from its loyalty card system (demographic Information)
with its point of sale systems (what customer bought)
v Once combined, the data was mined extensively and many correlations appeared.
v People who buy GIN in are also likely to buy TONIC, they often also buy LEMONS
v However, one correlation stood out
v On Friday afternoons, young American who buy DIAPERS (nappies) also buy BEER

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Strategic and Incidental Purchase
Ø Bread & Butter/Cheese
Ø Beer and Chips
How should we place them

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üIf the monsoon is delayed by a month, grains process might go up by xx%
üIf the competitor increases the discount by x% our sales will go down by y%
üIf RBI reduces interest rates by xx%, stock market will….
üIf we reduce our marketing spend by Rs xyz, we will loose sales by xx%
ê Combine outcomes of descriptive and diagnostic analytics into actionable insights
for decision making
ê Techniques include - Confidence intervals, T statistics and P values, applied
machine learning algorithms, classification models and regression models
Predictive Analytics - What Happens If

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üIf I use higher grade ingredient the yield will improve by x%
üIf We increase the bulk discount by x%, sales will go up by y%
üIf we increase the machine speed by 25%, rejections are likely to go up by 1%
ê Techniques include - Artificial intelligence, Machine Learning and Neural Network
algorithms
Prescriptive Analytics - How to Make it Happen

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Potential Value Delivery through Analytics?
§ Optimization
• What price should we charge for this product?
• How much inventory should we keep?
§ Segmentation
• Who should we market to?
• More importantly – who should we NOT market to?
§ Recommendations
• You bought X therefore you may want to buy Y?
• Right set of employees for given type of Roles?
§ Forecasting
• How much will we sell next quarter?
• Repeating weather patterns?
• Volumes by Day of The Week, Day of the Month?
• Attrition by Level of Employees
§ Risk Assessment
• Who will pay me, on time? Who will not?
• Potential Fraud

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Structured vs Unstructured Data
Ø Structured
§ Tables with rows and columns
§ Each data set follows a schema/structure
Ø Unstructured
§ Videos
§ Images
§ Documents
§ No schema/structure, but data is there

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Why is Analytics Gaining Importance
A. Availability of Good Data
B. Computing Capability
C. Higher number of influencing Factors
D. Dynamic Environment and Need to Decide Fast
E. Need to establish insights from unknown & unexplored factors

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OSEMN Methodology Overview
Public/Web data
Cloud Database
Centralized
database
Local machines
Obtain
Scrub
Explore
Model
iNterpret
Cleaning data
• Joining data
• Standardizing
• Missing Values & Duplicates
• Data Formatting
• Feature Scaling
• Data Encoding
Exploratory Analysis
• Descriptive
Statistics
• Graphical Analysis
• Comparing Groups
• Feature Engineering
Regression
Decision Tree
Logistic Regression
Cluster Analysis
Naive Bayes
classifier
Data Interpretation
Hyperparameter Tuning
Iterations
Delivering Business Value
Soft Skills
• Stakeholder Analysis
• Communication Plan
• Questioning Techniques
• Change Management
Case Studies

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BIG Data needs special attention
The term ‘Big Data’ is:
Ø Frequently abused…..
Ø Thrown around by bloggers and journalists to increase hits….
Ø Data ‘too big to fit in Excel’ – Really???

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What is Big Data: 5 Vs
Variety – different types of data
Velocity – speed at which new data is generated
Volume – amount of data
Veracity – lack of quality and accuracy
VALUE – turn data into information, into knowledge, into wisdom

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Where does all this Big Data come from?
What's important for me, for my
organization, my customer?
• Immediate
• Short Term
• Mid Term
• Long Term

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Supervised and Unsupervised Learning
Unsupervised
Supervised

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Supervised Learning
Characteristics of Employees Who Buy Our Product
Yes - Buy
No - Do Not Buy

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Types of Learning - Supervised
• Training data includes desired outputs.
• Machine is trained on past data and its output to learn the hidden pattern. Hence
Supervised

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Supervised Learning: Customer Behavior
Response on Personal Loan
0 –No Response, 1-Positive
ID Age Experience Income ZIP Code Family CCAvg Education Mortgage Personal Loan Securities Account CD Account Online CreditCard
1 25 1 49 91107 4 1.60 1 0 0 1 0 0 0
2 45 19 34 90089 3 1.50 1 0 0 1 0 0 0
3 39 15 11 94720 1 1.00 1 0 0 0 0 0 0
4 35 9 100 94112 1 2.70 2 0 0 0 0 0 0
5 35 8 45 91330 4 1.00 2 0 0 0 0 0 1
6 37 13 29 92121 4 0.40 2 155 0 0 0 1 0
7 53 27 72 91711 2 1.50 2 0 0 0 0 1 0
8 50 24 22 93943 1 0.30 3 0 0 0 0 0 1
9 35 10 81 90089 3 0.60 2 104 0 0 0 1 0
10 34 9 180 93023 1 8.90 3 0 1 0 0 0 0
Age
Experience
Income
Family Size

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Unsupervised Learning
Segment Employee with known Characteristics
Oval Body
Oval Body &
Square Head
Oval Body &
Circular Head
Oval & Gray Body
with Circular Head
Oval & White Body
with Circular Head

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Types of Learning - Unsupervised
• Training data does not include desired outputs
• Machine is not trained on any specific output. Hence, the name Unsupervised
Learning

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Unsupervised Learning: Clustering Example
ID Age Experience Income ZIP Code Family CCAvg Education Mortgage
Securities
Account
CD
Account
Online
Credit
Card
1 25 1 49 91107 4 1.60 1 0 1 0 0 0
2 45 19 34 90089 3 1.50 1 0 1 0 0 0
3 39 15 11 94720 1 1.00 1 0 0 0 0 0
4 35 9 100 94112 1 2.70 2 0 0 0 0 0
5 35 8 45 91330 4 1.00 2 0 0 0 0 1
6 37 13 29 92121 4 0.40 2 155 0 0 1 0
7 53 27 72 91711 2 1.50 2 0 0 0 1 0
8 50 24 22 93943 1 0.30 3 0 0 0 0 1
9 35 10 81 90089 3 0.60 2 104 0 0 1 0
10 34 9 180 93023 1 8.90 3 0 0 0 0 0
Feature Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Total
No of Customers 1581 621 2037 177 584 5000
Age 43.5 46.1 46.7 42.2 45.7 45.3
Experience 18.2 21.0 21.4 17.0 20.7 20.1
Income 29.3 108.2 66.1 188.6 149.6 73.8
Family 2.5 2.1 2.6 1.9 2.0 2.4
CCAvg 0.9 2.6 1.7 4.7 4.0 1.9
Education 2.0 1.7 2.0 1.7 1.5 1.9
One of the algorithm “Cluster
Observations” concludes that there are
“Five Distinct Groups (Clusters)” of
Customers with different attributes

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Types of Data
Ø Variable or Continuous
§ Salary in Dollars, Number of Years of experience
Ø Discreate or Attribute
§ Binary - Yes or No, Good or Bad, Present or Not Present
§ Ordinal - No of Family Members
§ Categorical – ZIP Code, Education, Profession

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Analytics Techniques
Learning Types
Continuous
Target Variable
Categorical
Target Variable
Clustering/Association
Supervised
Learning
Unsupervised
Learning
Target Variable not Available
Regression Classification
• Linear Regression
• Polynomial Regression
•Lasso Regression
•Ridge Regression
• PCA
• K-Means
•Hierarchical Clustering
•Association Rules
• KNN
• Logistics Regression
•SVM
•Naïve-Bayes

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Background of the Project
Probability of taking Personal Loan
q A retail bank is interested in finding out the influence of Cross Selling Efforts to the existing
customers w.r.t. decision to take a Personal Loan
q The data were collected on various influencing factors given in next slide

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Data Structure 1 of 2
ü Personal Loan - Did this customer accept the personal loan
offered in the last campaign?
ü Customer ID
ü Customer's age in completed years
ü Experience #years of professional experience
ü Income Annual income of the customer ($000)
ü ZIPCode Home Address ZIP code.
ü Family size of the customer
ü Credit Card Average spending on credit cards per month
ü Education Level – Non-Graduate, Graduate; Professional

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Data Structure 2 of 2
ü Mortgage Value of house mortgage if any. ($000)
ü Securities Account Does the customer have a securities account with the bank?
ü Certificate of Deposit (CD) account with the bank?
ü Online Does the customer use internet banking facilities?
ü Credit Card – Yes or No
ü Does the customer use a credit card issued by UniversalBank?

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Big Data with Large Number of Features is Often Unbalanced
Number of Customers by ZIP Code & Education
Education
ZIP Codes 1 2 3 Grand Total
9307 1 1
90007 5 1 6
90009 4 2 2 8
90011 1 1 1 3
90016 2 2
90018 2 2 4
90019 1 2 2 5
90024 20 12 18 50
90025 5 7 7 19
90027 2 1 3
90028 4 2 2 8
90029 4 1 5
90032 5 2 1 8
90033 4 2 3 9
90034 12 2 6 20
90035 1 1 4 6
90036 5 1 1 7
90037 2 1 2 5
90041 5 1 4 10
90044 1 1 2
90045 3 3
Number of Customers
ü Not Missing At Random – Difficult to tackle – Intentional
ü Missing at Random – Logically impute
ü Missing completely at Random – Can be deleted,
Interpolated

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Ø Missing Values
Ø Duplicate Value
Ø Dimensionality/Scale
Ø Outliers/Extreme Values
Ø Combining Data from Multiple Sources
SCRUB

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EXPLORE : How reliable is Analysis on Pivot Table….
No of Family Member
% Loan Availed
No Yes
1 93% 7%
2 92% 8%
3 87% 13%
4 89% 11%
Grand Total 90% 10%
Education
% Loan Availed
No Yes
Non Graduate 96% 4%
Graduate 87% 13%
Post Graduate 86% 14%
Grand Total 90% 10%
% Avail Loan Family Size
Education 1 2 3 4 Overall
Non Graduate 1% 1% 11% 10% 4%
Graduate 12% 19% 11% 11% 13%
Post Graduate 12% 14% 18% 12% 14%
Overall 7% 8% 13% 11% 10%

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EXPLORE - Data Distributions

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Complex relationships between Output and Inputs
Number of relationships cannot be fitted with Liner, Quadratic or even advance
polynomial equations

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MODEL - CART

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Cost of Misclassification

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Relative Importance of Factors

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Decision Tree
Overall Hit Rate
9.5%
Salary < 92.5K
0.7%
Income>92.5%
33%
Non Graduates
10%
Graduates +
72%
Family 1,2
Near 0%
Family 3+
38%
Focus on Clients with
• Income >92.5K
• Graduates +
• Non-Graduates - Family Size 3+

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Train and Test Model Summary

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Prediction
q The model provides ability to predict hit rate for multiple
profiles
q Individuals with income 150K & Non-Graduates, the hit
rate is below 1% if family size is 1 or 2, this goes up to
72% if family size is 3 or 4.

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Confusion – Train v/s Test

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Unsupervised Learning – Cluster Observations
q Scenarios where no specific outcome is specified or known
q In the same banking environment we have information of 5000 customers profiles.
q Objective is to Segment customers in the logical groups

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Cluster Observations - Dendrogram

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Cluster Centroids
Variable Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Grand centroid
Age 43.4 46.1 46.7 42.1 45.7 45.3
Experience 18.1 21.0 21.4 17.0 20.7 20.1
Income 29.3 108.2 66.1 188.6 149.6 73.8
Family 2.5 2.1 2.6 2.0 2.0 2.4
CCAvg 0.9 2.6 1.7 4.7 4.0 1.9
Education 2.0 1.6 2.0 1.7 1.5 1.9
Cluster Properties – Income and Credit Card Spend
No of Observations. 1998 499 1761 104 158 5000
Chances of Taking Loan. 0%. 29%. 3%. 51%. 43%

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I Shall Never Mix Water
Prof Juran’s Example on Root Cause Analysis

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Point to Remember
q Any such analysis should be used with complete care as misinterpretations can add to frustration
and result in disbelief in scientific methods
q While we are using advanced analytics tools to further strengthen our ability to identify root cause,
Team Engagement, Brainstorming, FMEA, Why-Why Analysis, 6-3-5, Mistake Proofing, Daily Work
Management remain equally important
q Good old Rule applies Business Problem – Statistical Problem – Statistical Solution – Business
Solution

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Data Enrichment - Using Secondary Data
Data enrichment is the process of creating new features by introducing data from external sources.
ü Information on Bank’s Customer Salary Increase Decrease can be extracted from Saving Bank
Transaction Records
ü Expenditure pattern can be extracted from Credit Card Bills
ü Addition in family member can be accessed through Birth and Death Register, Mediclaim’s, etc.

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Opportunity to Use AI and ML
q The model can be linked to multiple data sources within/outside bank to update the information on
existing parameters and add additional parameters like Age of Family Members, Changes in Income and
investment, etc
q This information can be used to auto trigger the agents to enable enhancement of cross selling
effectiveness, launching new products etc.

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Unstructured Analytics
Sourcing
Extracting
Categorizing
Visualization

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Customer Sentiment Analysis

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Tracing LinkedIn Profile
Application
ü Swipe in swipe out
ü New Employee Onboarding
ü Guest/Visitor Management

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Security Enhancement

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SPACE
CUSTOMER FEEDBACK – RESORT
BEACH ROOM
Far
Crowd
Water
Room
Service
Fun
Great
Shower
Staff
Clean

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Software Tools R and Python
Licensing Open Source Open Source SAS High/One Time SAS
Ease of Use Coding Coding Coding Moderate Easy
Coverage High High High Moderate Specific
Application

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BMGI Certified Business Analytics Professional
Module – 1 – Foundation
Module 2 - Advanced Modelling
+
Python & R (Optional)
Module 3 - Forecasting &
Visualisation
Module 4 - Optimisation,
AI & ML

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Threat to Causal Inferences
1. Selection
2. Simultaneity
3. Omitted Variable
4. Measurement Error

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Selection – Sample is Not Representative

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Correlation Causation

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Simultaneity – X and Y Cause Each Other

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Case Studies

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Sample Case Studies
1. Reduction in Processing Time and Error Rate
2. Profitability improvement in Auto Ancillary
3. Predicting and reducing days of Sales Outstanding
4. Deriving actionable insights from Employee Satisfaction Survey
5. Reduction in Customer Complaints
6. Reduction in Procure to Pay Cycle Time

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Wish you All the Best!!!!

Overview business analytics 6th june 2020 v5

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