ผลการวิเคราะห์ข้อมูลของทีมที่ได้รางวัลชนะเลิศ The First NIDA Business Analytics and Data Sciences Contest

ABC Bookstore Analysis
The Three Data Musketeers
NIDA Business Analytics and Data Sciences Contest
September, 2, 2016

Thearasak Phaladisailoed
Team Position: Senior Data Scientist
Faculty of Information Technology
KMITL
Experienced with Python
Machine Learning Enthusiast
James Rakratchatakul
Team Position: Analyst
Faculty of Engineering
Chulalongkorn University
- Big Data & Statistical Analysis
- Experienced with RapidMiner
- Tableua Analyst
Korkrid Akepanidtaworn
Team Position: Data Scientist
- Economics and Statistics
- London School of Economics
- Big Data & Statistical Analysis
- Experienced with R Programming, STATA,
SAS, SPSS, PSPP, Python, BI Tools, and
Big Data Services
September, 2, 2016 Business Analytics and Data Sciences Contest 2
Our Team

Morning Agenda 1. Understanding our Dataset
2. Problem Statement
3. Data Preprocessing
4. Descriptive Analytics
5. Predictive Analytics
6. Data Insight Recap

4
Analytics Life Cycle
“Our team use mass analytics tools to build a
predictive model, communicate results,
operationalize data, and lead to discovery”

Dataset Snapshot
395 observations of 64 variables. The dataset comes with data dictionary, describing each variable.

Problem Statement
ABC Bookstore is currently experiencing problems
with a constant decline in profits, customer loyalty,
and customer satisfaction.
Our team will need to use data analytics to
investigate the ways to recover customer satisfaction,
books re-purchasing rate, and bookstore subscription.

Data Preprocessing
The ABC Bookstore dataset needs cleansing prior to
data analysis and visualization. The key challenges are:
 Outlier Detection (delete or impute extreme values)
 Missing Value Treatment
 Binning or Discretization
 Central of Tendency Imputation
Missing Values Replacement Policies:
• Ignore the records with missing values.
• Replace them with a global constant (e.g., “?”).
• Fill in missing values manually based on your
domain knowledge.
• Replace them with the variable mean (if numerical)
or the most frequent value (if categorical).
• Use modeling techniques such as nearest neighbors,
Bayes’ rule, decision tree, or EM algorithm.

8
Descriptive Analytics
What Happened When?
Explaining the past

Demographic Analysis

What will happen?
Predicting the future

Customer Satisfaction Model
Algorithms Title: Stepwise Linear Regression
How does the algorithms work?: predicting the value of target (numerical variable) by building a model based on
one or more predictors (numerical and categorical variables)
Business Objective: regain customer satisfaction and maximize customer utility
Goal: predict the levels of customer satisfaction from purchasing books from our store
Approach:
• Data cleansing and select relevant features
• Interpret the linear regression model
• Examine coefficient of determination and p-values.
• Parameter Tuning
• Model selection and evaluation

Regression Model Result

Model Evaluation – R Squared
The coefficient of determination (R2) summarizes the explanatory power of the regression model and is computed
from the sums-of-squares terms.
R2 describes the proportion of variance of the dependent variable explained by the regression model. If the
regression model is “perfect”, SSE is zero, and R2 is 1. If the regression model is a total failure, SSE is equal to SST,
no variance is explained by regression, and R2 is zero.

Re-Purchasing Algorithms
Algorithms Title: Linear Regression
How does the algorithms work?: predicting the value of target (numerical variable) by building a model based on
one or more predictors (numerical and categorical variables)
Business Objective: identify the rate at which a customer wants to re-purchase our books
Goal: predict the levels of customer satisfaction from purchasing books from our store
Approach:
• Interpret the linear regression model
• Examine coefficient of determination and p-values.
• Parameter Tuning

Re-Purchasing Regression

Model Evaluation - RMSE
RMSE is a popular formula to measure the error rate of a regression model. However, it can only be compared
between models whose errors are measured in the same units: RMSE = 7.29605. Accuracy = 92.70395

Subscription Model
Algorithms Title: Decision Tree
How does the algorithms work?: Decision tree builds classification or regression models in the form of a tree
structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated
decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. Decision
trees can handle both categorical and numerical data.
Business Objective: identify customer preference to subscribe ABC bookstore
Goal: apply vs. not apply by using classification algorithms
Approach:
• Interpret the decision tree model
• Parameter Tuning: entropy, information gain, and decision rules

Model Result
Significant var: CS5, CS8, CS9 + age, CS14, CS30

Model Evaluation
A confusion matrix shows the number of correct and incorrect predictions made by the classification model compared
to the actual outcomes (target value) in the data
• Accuracy : the proportion of the total number of predictions that were correct.
• Positive Predictive Value or Precision : the proportion of positive cases that were correctly identified.
• Negative Predictive Value : the proportion of negative cases that were correctly identified.
• Sensitivity or Recall : the proportion of actual positive cases which are correctly identified.
• Specificity : the proportion of actual negative cases which are correctly identified.

What’s Next?
Data-driven business strategy in the era of Thailand 4.0
 Better Campaign Performance
 Better Market Share
 Better Product Development
 Lasting Revenue
 Better customer satisfaction
 Better books re-purchasing rate
 Better bookstore subscription.

Business Analytics: Data-Driven ABC Bookstore
The Three Data Musketeers
NIDA Business Analytics and Data Sciences Contest
September, 2, 2016

Business Plan:
Table of Contents
1. Business Challenges and Expectations
2. Industry 4.0: Data-Driven Business
3. Marketing Strategy
4. Management
5. Service Recommendation
6. Project Timeline
7. Financial Analysis

Key Challenges & Outcomes
September, 2, 2016 NIDA Business Analytics and Data Sciences Contest 25
• Constant decline in profits
• Customer utility drops
• Low re-purchase
• Low the bookstore subscription
• Sustainable business growth
• Maximized customer utility
• Higher re-purchase
• High Incentive to subscribe

Industry 4.0

Data-Driven Marketing
If ABC Bookstore wants to succeed in the era of Thailand 4.0, the company
needs to deliver innovation, speed, cost reduction, and creative management
27September, 2, 2016 NIDA Business Analytics and Data Sciences Contest
the right message to the right person
at the right time for the right price

Strategy to Maximize Utility
According to previous analysis, it is found that “the employee can answer questions”, “activities/discussions”, “a
variety of books”, and “book wrapping” are statistically significant to the increase in customer satisfaction.
Management Challenge
 Lack of employee training
 Lack of store activities
Service Challenge
 A variety of books offered
 Add-on for book wrapping?

Strategy to Re-purchase
According to previous analysis, it is found that the top 10 factors that are statistically associated with re-
purchasing are a variety of books, bag claiming, respect for customers, employee’s good service, quality in book
categorization, feeling of security or freedom in reading, place to read books, Easy to search books, and discount
or promotion.

Strategy to Increase Subscription
Freedom for customers
Well-mannered
There’s book that I want!
Book Wrapping Service
Well-Organized
Quality in book categorization

Creative Management 4.0
Data indicates three following attributes of most importance:
 Well-Mannered
 Service-minded
 No pressure on customers
 Freedom for customers
 Respect for customers
 Balance of Can Do Capability and Will Do Motivation
 appropriate training for employees
 adequate skills and competencies, especially data-driven decisions
 updated knowledge: always keep up-to-date on social media
 Motivate employees in aspects of recognition, love of work, career
structure, and social respect.

ABC Serenade
Novel Business
Academic Magazine

ABC Serenade
Novel Top10

ABC Serenade
Novel
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xxxxxxxxxxxxxxxxxxx
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xxxx

ABC Counter

Timeline
Set up Impression Box
Train Employees 1st
2016/
9
2016/
11
2016/
13
2016/
10
2016/
12
App Development
for Serenade
Serenade Zone
Full Operation
Social Media Marketing
36
Result Check-up
Purchase Computers
and materials for
serenade zone

Financial Analysis
Service Costs
Impression Box 200 B
Training Venue 10,000 B
Computer 20,000 B
App Development 10,000 B
Serenade Bar 10,000 B
Total 50,200 B

ผลการวิเคราะห์ข้อมูลของทีมที่ได้รางวัลชนะเลิศ The First NIDA Business Analytics and Data Sciences Contest

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ผลการวิเคราะห์ข้อมูลของทีมที่ได้รางวัลชนะเลิศ The First NIDA Business Analytics and Data Sciences Contest