Marketing Analyticsu A Machine Learning Approach 1st edition by Mansurali 9781000608908 1000608905

1. Introduction to Marketing Analytics

1.1 Introduction

1.2 Analytics

1.3 Marketing Analytics: The Definition

1.4 Overview of Marketing Analytics

1.5 Marketing Analytics: The Process

1.6 Importance and Application of Statistics in Marketing Analytics

1.7 Importance of Marketing Analysis and Its Impact

1.8 Application of Marketing Analytics

1.9 Marketing Models and Analytics

1.10 Segmentation and Targeting

1.11 New Product Development and Customer Value

1.12 Price Prediction and Modeling

1.13 Customer Buying Behavior

1.14 Market Basket Analysis (MBA)

1.15 Sales Forecasting

1.16 Brands and Sentiment Analysis

1.17 Web Analytics and Social Media Analytics

1.18 Challenges in Marketing Analytics

1.19 Conclusion

References

2. Statistics for Marketing

2.1 Introduction

2.1.1 Descriptive Statistics

2.1.2 Measures of Dispersion or Measures of Variation

2.2 Types of Scales

2.2.1 Nominal Scale

2.2.2 Ordinal Scale

2.2.3 Interval Scale

2.2.4 Ratio Scale

2.3 Inferential Statistics

2.3.1 Hypothesis Testing

2.3.1.1 Steps For Hypothesis Testing

2.4 One-Tailed and Two-Tailed Tests

2.4.1 One Tailed Test

2.4.2 Two Tailed Test

2.5 Parametric and Non-Parametric Test

2.6 Test Statistics for Hypothesis Testing Based on Different Parameters

2.6.1 Hypothesis Testing When Population Mean and Population Variance is Known: Z-Test

2.6.2 Hypothesis Testing For Population Proportions: Z-Test Population Proportion

2.6.3 Hypothesis Test for Population Mean When Population Variance Is Not Known: T-Test

2.6.4 Paired Sample T-Test

2.6.5 Comparing Two Populations: Two-Sample Z-Test and T-Test

2.6.5.1 Two Sample Z-Test (When Population Standard Deviations Are Known)

2.6.5.2 Two-Sample T-Test (When Population Standard Deviations are Unknown and Believed to be Equal)

2.6.6 Two-Sample T-Test With Unequal Variances

2.6.7 Hypothesis Test for Difference in Population Proportion Under Large Samples: Two Samples Z-Test for Proportions

2.6.8 Hypothesis Test for Equality of Population Variances

References

3. Evolution of Data Analytics

3.1 Definitions

3.2 Statistics

3.3 Relational and Non-Relational Database (NoSQL)

3.3.1 Data Warehouses

3.3.2 Data Warehouse Vs. Database

3.3.3 Advantage of Data Warehousing

3.4 Olap (Online Analytical Processing)

3.4.1 Working of Olap

3.4.2 Types of Olap Systems

3.5 Data Mining

3.6 Big Data Analytics and Hadoop

3.6.1 Foundation of Big Data Analytics

3.6.2 Hadoop

3.7 Map Reduce

3.8 Apache Spark

3.8.1 Apache Spark Analytics

3.8.2 Merits and Demerits of Apache Spark

3.8.3 Benefits of Apache Spark

3.8.4 Limitation of Apache Spark

3.8.5 Features of Apache Spark

3.8.6 Functionalities of Spark

3.8.7 Apache Spark Vs. Hadoop Mapreduce

3.8.8 Summarization of Capability and Compatibility of Spark with Other Search Engines

References

4. Segmentation and Targeting Analysis

4.1 Definitions

4.2 Cluster Analysis

4.2.1 Types of Clustering

4.2.2 Steps for Cluster Analysis

4.2.3 Application for Cluster Analysis

4.3 Segmentation

4.3.1 Market Segmentation

4.3.2 Cluster Analysis on Market Segmentation

4.3.3 Conditions for Successful Targeted Market Segmentation

4.3.4 Analyzing Data Using Clustering and Segmentation

4.3.5 Household Objects Clustering

4.4 Marketing Mix

4.4.1 4Ps of Marketing Mix

4.4.2 Extended Components of Marketing Mix

4.4.3 Characteristics of Marketing Mix

4.5 Swot

4.5.1 Overview of Swot Components

4.5.2 Uses of Swot

4.5.3 Adopting Swot for New Business

4.5.4 Coca-Cola: An Extensive Research

4.6 Competitive Analysis

4.6.1 Importance of Competitive Analysis Important in Marketing

4.6.2 Identifying the Powerful Competitors

4.6.3 Competitive Analysis in Marketing

4.6.4 How Investigation Is Done for Competitive Analysis?

4.7 Target Analysis

4.7.1 Target Marketing Examples

4.7.2 Analyzing the Target Marketing Strategy

4.7.3 Strategies Involves in Target Marketing Techniques

4.7.4 Target Marketing Vs. Corresponding Consumers

References

5. Important Marketing Metrics: A Snapshot

5.1 Introduction

5.2 Marketing Metrics

References

6. Consumer Buying Behavior

6.1 Introduction

6.2 Meaning

6.3 Characteristics

6.3.1 Organized Procedure

6.3.2 Affected By Diverse Elements

6.3.3 Varies from Customer to Customer

6.3.4 Varies From Product To Product

6.3.5 Change In Experiences

6.3.6 Hunt For Information

6.4 Components (Figure 6.4)

6.4.1 Negotiations

6.4.2 Buyer Concerns

6.5 Process

6.5.1 Problem Recognition

6.5.2 Information Search

6.5.3 Evaluation Of Alternatives

6.5.4 Purchase Decision

6.5.5 Post Purchase Behavior

6.6 Types

6.6.1 Complex Buying Behavior

6.6.2 Dissonance Reducing Buying Behavior

6.6.3 Variety Seeking Buying Behavior

6.6.4 Habitual Buying Behavior

6.7 Models

6.7.1 Economic Model

6.7.2 Learning Model

6.7.3 Psychoanalytic Model

6.7.4 Sociological Model

6.8 Determinants Of Consumer Behavior

6.8.1 Psychological Factors

6.8.2 Social Factors

6.8.3 Cultural Factors

6.8.4 Personal Factors

6.8.5 Economic Factors

6.9 Importance

6.9.1 Consumer Differentiation

6.9.2 Retention Of Consumers

6.9.3 Designing Of Marketing Program

6.9.4 Predictions Of Market Trends

6.9.5 Competition

6.9.6 Improve Customer Services

6.10 Conclusion

References

7. Understanding Consumer Behavior Using Market Basket Analysis

7.1 Introduction

7.2 Applications of Association Rule Mining (ARM)

7.3 Market Basket Analysis (MBA)

7.4 Fundamental Concepts Of Association Rule Mining (Arm)

7.5 Association Rule Mining (ARM) Algorithm

7.5.1 Dataset

References

8. Neuromarketing Techniques for Consumer Analytics

8.1 Introduction

8.2 Neuromarketing

8.3 Neuromarketing for Consumer Analytics

8.3.1 Mapping The ‘Buy Button’ From Brain

8.4 Tools and Techniques of Neuromarketing for Consumer Analytics

8.4.1 Functional Magnetic Resonance Imaging (fMRI)

8.4.2 Electroencephalography (EEG)

8.4.3 Facial Coding and Facial Electromyography (fEMG)

8.4.4 Eye Tracking

8.4.5 Magnetoencephalography (MEG)

8.4.6 Positron Emission Tomography (Pet)

8.4.7 Galvanometer Test

8.5 Ethical Implications of Using Neuromarketing Tools for Consumer Analytics

8.6 Conclusion

References

9. New Product Development

9.1 Introduction

9.1.1 New Product

9.1.2 Role of Marketer in Npd

9.2 Analytics and Product Development

9.3 Models For New Product Development

9.3.1 Conjoint Model

9.3.2 Building a Full Profile Conjoint Model

9.3.3 Customer Value Modeling

References

10. Natural Language Processing for Branding

10.1 Introduction

10.2 NLP: A Brief History

10.3 NLP Basics

10.4 Data Scraping and Brand Management

10.5 Retrieving Tweets For Brand Monitoring

10.6 Text Mining Using R

10.6.1 Sentiment Analysis

References

11. Forecasting Sales and Price

11.1 Introduction

11.2 Correlation Analysis

11.2.1 Scatter Plot

11.3 Techniques Of Forecasting

11.3.1 Simple Regression Model

11.3.2 Multiple Linear Regression Model

11.3.3 Moving Average (MA)

References

12. Sales Prediction and Conversion

12.1 Introduction

12.1.1 Importance of Sales Prediction

12.1.2 Application of Sales Prediction

12.1.3 Types of Sales Prediction

12.2 Time Series Prediction

12.2.1 Sales Prediction Using Time Series Analysis

12.3 Decision Tree Algorithm

12.3.1 Splitting and Pruning the Decision Tree

12.3.2 Classification and Regression Tree

12.3.3 Sales Prediction Using Decision Tree

12.4 Logistic Regression Algorithm

12.4.1 Sales Prediction Using Logistic Regression

12.5 Conclusion

References

13. Role of Supply Chain Analytics in Marketing Analytics

13.1 Introduction

13.2 Marketing Analytics

13.2.1 Marketing Intelligence

13.2.2 Relevance of Supply Chain Management (SCM) Vs. Marketing Analytics

13.3 Integration of Supply Chain Analytics

13.4 Role of Supply Chain Management (SCM) in Business

13.5 Significance of Supply Chain Management (SCM)

13.6 Business Processes: An Overview

13.7 Supply Chain Complexity

13.8 Analytics the Right Tool for Efficient Supply Chain

13.8.1 Forecasting Future Demand

13.8.2 Materials

13.8.3 Different Types of Supply Chain Analytics

13.9 Conclusion

References

14. Web and Social Media Analytics

14.1 Introduction

14.2 Web Analytics

14.2.1 Types of Web Analytics

14.2.2 Usage of Web Analytics

14.2.3 The Process of Web Analytics

14.3 Social Media Analytics

14.3.1 The Process of Social Media Analytics

14.3.2 Usage of Social Media Analytics

14.3.3 Social Media Analytics Framework

14.4 Linkage between Web Analytics and Social Media Analytics

14.5 Difference Between Web Analytics and Social Media Analytics

14.6 Terminologies Using in Web And Social Media Analytics

14.7 Open Source Tools for Analytics

14.7.1 Google Analytics

14.7.2 Clicky

14.7.3 Open Web Analytics Tools (Owa)

14.7.4 Piwik

14.7.5 Woopra

14.7.6 Heap Analytics

14.7.7 Mint Analytics

14.7.8 Kiss Metrics

14.7.9 Crazy Egg

14.7.10 Clicktale

14.8 Conclusion

References

15. Marketing Analytics and Its Applications

15.1 Introduction

15.2 Why Marketing Analytics is Important?

15.3 Marketing Analytics and Its Applications

15.3.1 Cross-Selling And Up-Selling

15.3.2 Recommendation Engine

15.3.3 Attribution Modeling

15.3.4 Pricing Analytics

15.3.5 Marketing Mix Model

15.3.6 Customer Segmentation and Product Segmentation

15.3.7 Demand Forecasting

15.3.8 Customer Analytics

15.3.9 Churn Analytics

15.3.10 Purchase Probability Scale

15.4 Conclusion