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ABC Bookstore AnalysisThe Three Data MusketeersNIDA Business Analytics and Data Sciences ContestSeptember, 2, 2016

Thearasak PhaladisailoedTeam Position: Senior Data Scientist

Faculty of Information Technology KMITL

Experienced with PythonMachine Learning Enthusiast

James RakratchatakulTeam Position: Analyst

Faculty of Engineering Chulalongkorn University

- Big Data & Statistical Analysis- Experienced with RapidMiner

- Tableua Analyst

Korkrid AkepanidtawornTeam 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 Agenda1. 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.

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Descriptive AnalyticsWhat 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:• Data cleansing and select relevant features• Interpret the linear regression model• Examine coefficient of determination and p-values. • Parameter Tuning• Model selection and evaluation


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:• Data cleansing and select relevant features• Interpret the decision tree model• Parameter Tuning: entropy, information gain, and decision rules• Model selection and evaluation


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 ContestSeptember, 2, 2016

Business Plan: Table of Contents

1. Business Challenges and Expectations2. Industry 4.0: Data-Driven Business3. Marketing Strategy4. Management 5. Service Recommendation6. Project Timeline7. 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 customersWell-mannered

There’s book that I want!Book Wrapping Service

Well-OrganizedQuality 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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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 OperationSocial Media Marketing

36

Result Check-up

Purchase Computersand 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

Thank YouTime for Q & A

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