WMK01800 Web Survey & Digital Marketing

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program

Lecture Code WMK01800 Subject Classification Specialized Education

Subject Name Web調査とデジタルマーケティング

Subject Name
（Katakana）ウェブチョウサトデジタルマーケティング

Subject Name in
English Web Survey & Digital Marketing

Instructor KAJIKAWA HIROAKI,HARADA YUSUKE

Instructor
(Katakana) カジカワ　ヒロアキ,ハラダ　ユウスケ

Campus Higashi-Senda Semester/Term 1st-Year, Second Semester, Second Semester

Days, Periods, and Classrooms (2nd) Fri11-14

Lesson Style Seminar Lesson Style
(More Details) Face-to-face, Online (on-demand)

Credits 1.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 5 : Graduate Basic

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 09 : Information Management

Eligible Students Social Data Science Program

Keywords Questionnaire Design, Quality Control, Customer Journey, Machine Learning, Generative AI

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Course Objectives
The goal of this course is to master the processes of "data collection, analysis, and business implementation" in the digital age, cultivating data utilization skills directly linked to solving social issues.
Mastery of Research Design: Acquire skills ranging from formulating research questions based on business issues to Questionnaire Design with minimized bias, as well as digital methods for qualitative and quantitative research.
Data Analysis and Strategy Planning: Enhance the ability to grasp increasingly complex consumer behaviors through web analytics and text mining, directly linking data to business Key Performance Indicators (KPIs).
Understanding AI Implementation and DX Promotion: Learn the practical application of Generative AI and Machine Learning, alongside data ethics (privacy protection) and practical approaches to driving organizational Digital Transformation (DX).

Course Overview
Part 1: Basics and Design of Web Research
Learn purpose-driven survey design, questionnaire UI/UX, Quality Control, and digitalization methods for qualitative research.

Part 2: Digital Marketing Theory and Data Utilization
Unravel consumer behavior using data through Customer Journey visualization, web analytics, LTV analysis, and social listening.

Part 3: Application of Advanced Technologies and Ethics
Learn about the marketing implementation of AI and Machine Learning, data ethics, and organizational theory for promoting DX.

Class Schedule Session 1: Introduction We will overview how research methods have evolved through digitalization and share the overall picture of data science in marketing, as well as the roadmap for this course.
Session 2: Building a Research Design You will learn how to formulate the most important question of "why collect data" (research question) and understand the risks of bias in sampling.
Session 3: Practice of Questionnaire Design You will acquire specific question design skills and UI/UX best practices to reduce respondent burden (cognitive load) and extract accurate data.
Session 4: Web Survey Specific Challenges and Quality Control You will learn how to prevent, detect, and remove "dishonest responses" (careless answering), which is the biggest weakness of web surveys, to ensure data reliability.
Session 5: Digitalization of Qualitative Research You will understand how qualitative research, which delves into the "why" unseen in questionnaires (quantitative), has evolved through digital tools (such as MROCs and online interviews).
Session 6: Consumer Behavior Models and Customer Journey Breaking away from traditional models like AIDMA, you will learn methods to visualize the modern purchasing process where SNS and search are intricately intertwined.
Session 7: Web Analytics and Web Tracking You will develop the ability to interpret users' web behavioral logs (footprints) by linking them to business Key Performance Indicators (KPIs) through tools like GA4.
Session 8: Advertising Effectiveness Measurement and Attribution Analysis Instead of just evaluating the "last clicked ad," you will learn advanced effectiveness measurement techniques that properly evaluate all touchpoints that contributed to gaining awareness.
Session 9: CRM and LTV (Customer Lifetime Value) Analysis You will learn the importance of shifting from acquiring new customers to retaining existing ones, and design measures to identify loyal customers and increase loyalty using data.
Session 10: Social Listening and Text Mining You will learn how to extract honest opinions and trends about a brand from massive amounts of word-of-mouth (unstructured data) on SNS using natural language processing, and utilize them in strategy.
Session 11: AI and Machine Learning in Marketing You will understand how Machine Learning is implemented in the marketing field, such as predicting customer churn and e-commerce recommendation engines, and the mechanisms behind them.
Session 12: Marketing Practice in the Generative AI Era Utilizing LLMs (Large Language Models) as practical partners, you will learn prompt techniques to streamline the automatic generation of questionnaires, refinement of personas, and mass production of creative ideas.
Session 13: Privacy Protection and Data Ethics You will acquire ethical literacy to avoid "dark patterns" associated with the advancement of data utilization and to comply with domestic and international regulations (Cookie regulations, GDPR, etc.).
Session 14: DX Promotion and Organizational Implementation No matter how excellent the data analysis is, it is meaningless if the organization does not act. You will understand the unique barriers of SMEs and specialized fields (such as healthcare) and learn how to advance DX while involving the entire company.
Session 15: Final Presentation / Summary As the culmination of all 14 sessions, you will design your own research, incorporate data into marketing measures and business decisions, and present your strategy.

Students are required to submit reports.

Text/Reference
Books,etc. The textbooks will be assigned in the class.

PC or AV used in
Class,etc. Text, Handouts, Visual Materials, Microsoft Teams, Zoom, moodle

(More Details)

Learning techniques to be incorporated Discussions, Post-class Report

Suggestions on
Preparation and
Review Session 1: Introduction Overview digital research evolution and course roadmap. Prep/Review: Define personal data challenges and required data types.
Session 2: Building a Research Design Formulate research questions and understand sampling bias. Prep/Review: List a business issue; define hypotheses and variables.
Session 3: Practice of Questionnaire Design UI/UX best practices to reduce respondent burden. Prep/Review: Analyze poor surveys; draft 5 questions.
Session 4: Web Survey Challenges & Quality Control Detect and remove dishonest responses. Prep/Review: Research trap questions; create data cleaning criteria.
Session 5: Digitalization of Qualitative Research Explore the "why" using digital tools like MROC. Prep/Review: Compare quant/qual methods; draft an online ethnography proposal.
Session 6: Consumer Behavior & Customer Journey Visualize modern purchasing processes across SNS/search. Prep/Review: Track a recent purchase; draft a target's journey map.
Session 7: Web Analytics & Tracking Interpret web logs via GA4 to measure KPIs. Prep/Review: Learn basic terms (PV, CVR); set 3 main KPIs for your site.
Session 8: Ad Effectiveness & Attribution Evaluate all touchpoints contributing to awareness. Prep/Review: Review ad billing; analyze hurdles for Marketing Mix Modeling.
Session 9: CRM & LTV Analysis Identify loyal customers and increase loyalty. Prep/Review: Analyze personal brand loyalty; plan RFM segmentation.
Session 10: Social Listening & Text Mining Extract brand trends from unstructured SNS data. Prep/Review: Check SNS mentions; design a text mining visualization plan.
Session 11: AI and Machine Learning in Marketing Understand churn prediction and recommendation engines. Prep/Review: Guess Amazon's data usage; propose a predictive model.
Session 12: Marketing Practice in the Generative AI Era Use LLMs to automate surveys, personas, and creatives. Prep/Review: Try simple AI prompts; use AI to refine your survey draft.
Session 13: Privacy Protection & Data Ethics Comply with GDPR/Cookie rules and avoid dark patterns. Prep/Review: Check a privacy policy; assess legal/ethical risks.
Session 14: DX Promotion & Organizational Implementation Overcome organizational barriers to implement data analysis. Prep/Review: Analyze DX barriers; plan a "small success" project.
Session 15: Final Presentation / Summary Present a marketing strategy based on your research design.

Requirements

Grading Method Comprehensive evaluation of attitude to class and report

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it Statistical Research Certificate (Japan Statistical Society)

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program
Lecture Code	WMK01800	Subject Classification	Specialized Education
Subject Name	Web調査とデジタルマーケティング
Subject Name （Katakana）	ウェブチョウサトデジタルマーケティング
Subject Name in English	Web Survey & Digital Marketing
Instructor	KAJIKAWA HIROAKI,HARADA YUSUKE
Instructor (Katakana)	カジカワ　ヒロアキ,ハラダ　ユウスケ
Campus	Higashi-Senda	Semester/Term	1st-Year, Second Semester, Second Semester
Days, Periods, and Classrooms	(2nd) Fri11-14
Lesson Style	Seminar	Lesson Style (More Details)	Face-to-face, Online (on-demand)

Credits	1.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	5 : Graduate Basic
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	09 : Information Management
Eligible Students	Social Data Science Program
Keywords	Questionnaire Design, Quality Control, Customer Journey, Machine Learning, Generative AI
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Course Objectives The goal of this course is to master the processes of "data collection, analysis, and business implementation" in the digital age, cultivating data utilization skills directly linked to solving social issues. Mastery of Research Design: Acquire skills ranging from formulating research questions based on business issues to Questionnaire Design with minimized bias, as well as digital methods for qualitative and quantitative research. Data Analysis and Strategy Planning: Enhance the ability to grasp increasingly complex consumer behaviors through web analytics and text mining, directly linking data to business Key Performance Indicators (KPIs). Understanding AI Implementation and DX Promotion: Learn the practical application of Generative AI and Machine Learning, alongside data ethics (privacy protection) and practical approaches to driving organizational Digital Transformation (DX). Course Overview Part 1: Basics and Design of Web Research Learn purpose-driven survey design, questionnaire UI/UX, Quality Control, and digitalization methods for qualitative research. Part 2: Digital Marketing Theory and Data Utilization Unravel consumer behavior using data through Customer Journey visualization, web analytics, LTV analysis, and social listening. Part 3: Application of Advanced Technologies and Ethics Learn about the marketing implementation of AI and Machine Learning, data ethics, and organizational theory for promoting DX.
Class Schedule	Session 1: Introduction We will overview how research methods have evolved through digitalization and share the overall picture of data science in marketing, as well as the roadmap for this course. Session 2: Building a Research Design You will learn how to formulate the most important question of "why collect data" (research question) and understand the risks of bias in sampling. Session 3: Practice of Questionnaire Design You will acquire specific question design skills and UI/UX best practices to reduce respondent burden (cognitive load) and extract accurate data. Session 4: Web Survey Specific Challenges and Quality Control You will learn how to prevent, detect, and remove "dishonest responses" (careless answering), which is the biggest weakness of web surveys, to ensure data reliability. Session 5: Digitalization of Qualitative Research You will understand how qualitative research, which delves into the "why" unseen in questionnaires (quantitative), has evolved through digital tools (such as MROCs and online interviews). Session 6: Consumer Behavior Models and Customer Journey Breaking away from traditional models like AIDMA, you will learn methods to visualize the modern purchasing process where SNS and search are intricately intertwined. Session 7: Web Analytics and Web Tracking You will develop the ability to interpret users' web behavioral logs (footprints) by linking them to business Key Performance Indicators (KPIs) through tools like GA4. Session 8: Advertising Effectiveness Measurement and Attribution Analysis Instead of just evaluating the "last clicked ad," you will learn advanced effectiveness measurement techniques that properly evaluate all touchpoints that contributed to gaining awareness. Session 9: CRM and LTV (Customer Lifetime Value) Analysis You will learn the importance of shifting from acquiring new customers to retaining existing ones, and design measures to identify loyal customers and increase loyalty using data. Session 10: Social Listening and Text Mining You will learn how to extract honest opinions and trends about a brand from massive amounts of word-of-mouth (unstructured data) on SNS using natural language processing, and utilize them in strategy. Session 11: AI and Machine Learning in Marketing You will understand how Machine Learning is implemented in the marketing field, such as predicting customer churn and e-commerce recommendation engines, and the mechanisms behind them. Session 12: Marketing Practice in the Generative AI Era Utilizing LLMs (Large Language Models) as practical partners, you will learn prompt techniques to streamline the automatic generation of questionnaires, refinement of personas, and mass production of creative ideas. Session 13: Privacy Protection and Data Ethics You will acquire ethical literacy to avoid "dark patterns" associated with the advancement of data utilization and to comply with domestic and international regulations (Cookie regulations, GDPR, etc.). Session 14: DX Promotion and Organizational Implementation No matter how excellent the data analysis is, it is meaningless if the organization does not act. You will understand the unique barriers of SMEs and specialized fields (such as healthcare) and learn how to advance DX while involving the entire company. Session 15: Final Presentation / Summary As the culmination of all 14 sessions, you will design your own research, incorporate data into marketing measures and business decisions, and present your strategy. Students are required to submit reports.
Text/Reference Books,etc.	The textbooks will be assigned in the class.
PC or AV used in Class,etc.	Text, Handouts, Visual Materials, Microsoft Teams, Zoom, moodle
(More Details)
Learning techniques to be incorporated	Discussions, Post-class Report
Suggestions on Preparation and Review	Session 1: Introduction Overview digital research evolution and course roadmap. Prep/Review: Define personal data challenges and required data types. Session 2: Building a Research Design Formulate research questions and understand sampling bias. Prep/Review: List a business issue; define hypotheses and variables. Session 3: Practice of Questionnaire Design UI/UX best practices to reduce respondent burden. Prep/Review: Analyze poor surveys; draft 5 questions. Session 4: Web Survey Challenges & Quality Control Detect and remove dishonest responses. Prep/Review: Research trap questions; create data cleaning criteria. Session 5: Digitalization of Qualitative Research Explore the "why" using digital tools like MROC. Prep/Review: Compare quant/qual methods; draft an online ethnography proposal. Session 6: Consumer Behavior & Customer Journey Visualize modern purchasing processes across SNS/search. Prep/Review: Track a recent purchase; draft a target's journey map. Session 7: Web Analytics & Tracking Interpret web logs via GA4 to measure KPIs. Prep/Review: Learn basic terms (PV, CVR); set 3 main KPIs for your site. Session 8: Ad Effectiveness & Attribution Evaluate all touchpoints contributing to awareness. Prep/Review: Review ad billing; analyze hurdles for Marketing Mix Modeling. Session 9: CRM & LTV Analysis Identify loyal customers and increase loyalty. Prep/Review: Analyze personal brand loyalty; plan RFM segmentation. Session 10: Social Listening & Text Mining Extract brand trends from unstructured SNS data. Prep/Review: Check SNS mentions; design a text mining visualization plan. Session 11: AI and Machine Learning in Marketing Understand churn prediction and recommendation engines. Prep/Review: Guess Amazon's data usage; propose a predictive model. Session 12: Marketing Practice in the Generative AI Era Use LLMs to automate surveys, personas, and creatives. Prep/Review: Try simple AI prompts; use AI to refine your survey draft. Session 13: Privacy Protection & Data Ethics Comply with GDPR/Cookie rules and avoid dark patterns. Prep/Review: Check a privacy policy; assess legal/ethical risks. Session 14: DX Promotion & Organizational Implementation Overcome organizational barriers to implement data analysis. Prep/Review: Analyze DX barriers; plan a "small success" project. Session 15: Final Presentation / Summary Present a marketing strategy based on your research design.
Requirements
Grading Method	Comprehensive evaluation of attitude to class and report
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	Statistical Research Certificate (Japan Statistical Society)
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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