{"id":1312,"date":"2021-04-11T16:46:53","date_gmt":"2021-04-11T08:46:53","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=1312"},"modified":"2021-04-14T10:39:17","modified_gmt":"2021-04-14T02:39:17","slug":"digital-marketing-and-e-commerce-case-study","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=1312&lang=en","title":{"rendered":"Digital Marketing and E-commerce Case Study"},"content":{"rendered":"<h2><span style=\"color: #008000;\"><strong>Digital Marketing and E-commerce Data Case Study<\/strong><\/span><\/h2>\n<h3><span style=\"color: #008000;\"><strong>Course Arrangement<\/strong><\/span><\/h3>\n<p>The two central ideas of modern marketing are: &#8220;Digital marketing of customer value-centric&#8221; and &#8221; Strategic planning of data analysis-based&#8221;. Therefore, in addition to using data cases, this course allows students to practice customer value management and digital marketing tools. Also, We plan to cooperate with domestic e-commerce companies, and ask the company to provide front-end (customer behavior data collected from websites, traffic analysis tools and various digital media) and back-end (customer basic data and transaction records) data to cooperate with us through online Tools and web crawlers collect external data from the Internet (text content of social media or review sites, or website traffic, keyword research, competitive analysis data, etc.), and integrate them into a combination of front-end, back-end and external data, and at the same time A huge relational data set with structured and unstructured data, used to do the Capstone Project of the entire digital marketing and e-commerce micro-learning process; imitating the most common situation encountered by the planners of ordinary companies, the Capstone Project will only to provide information without specifying the target of analysis, students need to comprehensively use the analysis tools and methods they have learned during the course of study:<\/p>\n<ol>\n<li>&nbsp;Use a big data analysis platform to organize structured and unstructured data<\/li>\n<li>&nbsp;Use exploratory analysis methods and data visualization tools to see the structure and trends hidden in the data<\/li>\n<li>&nbsp;Ask key questions from the structure and trends,<\/li>\n<li>&nbsp;Use predictive and diagnostic methods as well as simulation and optimization tools to propose feasible countermeasures to key issues.<\/li>\n<\/ol>\n<h4>&nbsp;<\/h4>\n<hr>\n<h3 style=\"text-align: left;\"><span style=\"color: #008000;\"><strong>Course Syllabus<\/strong><\/span><\/h3>\n<h5>PART-I Digital Media And Online Tool Operation Practice<\/h5>\n<ul>\n<li>Digital Marketing<\/li>\n<li>The Business Implication of Big Data<\/li>\n<li>Company WebSite<\/li>\n<li>Web Analytics by Google Analytics<\/li>\n<li>Keyword Research &amp; Search Engine Optimization<\/li>\n<li>Keyword and Display Advertisment<\/li>\n<li>Content and Virus Marketing<\/li>\n<li>Affiliate Marketing and Celebraties<\/li>\n<li>Social Media &amp; Social Marketing<\/li>\n<li>Mobile Marketing<\/li>\n<li>Integrated Marketing and Communications<\/li>\n<\/ul>\n<h5>PART-II Case Study of Digital marketing<\/h5>\n<ul>\n<li>The Forecast Of New Product Sales<\/li>\n<li>Advertising effectiveness evaluation<\/li>\n<li><a href=\"http:\/\/www.kaggle.com\/c\/acquire-valued-shoppers-challenge\">Case of Customer Value Management in the Retail Industry<\/a>\n<ul>\n<li>Acquire Valued Shoppers Challenge (Kaggle)<\/li>\n<li>349,655,789 transaction items, 26,500,000 transactions, 311,500 customers<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"http:\/\/www.kaggle.com\/c\/instacart-market-basket-analysis\">Case of E-Commerce Website Recommendation System <\/a>\n<ul>\n<li>Instacart Market Basket Analysis (Kaggle)<\/li>\n<li>22,026,608 transactions, 2,178,586 transactions, 131,209 customers, 49,688 products<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"http:\/\/www.kaggle.com\/c\/coupon-purchase-prediction\">Case of forecasting the efficiency of discount coupons in the retail industry<\/a>\n<ul>\n<li>Coupon Purchase Prediction (Kaggle)<\/li>\n<li>2,833,178 product page clicks and 1,046,668 website visits<\/li>\n<li>168,996 transactions, 22,873 customers<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"http:\/\/2015.recsyschallenge.com\/challenge.html\">&nbsp;Case of E-Commerce Website Browsing Record<\/a>\n<ul>\n<li>Yoochoose RecSys 2015 Competition Dataset<\/li>\n<li>33,003,944 clicks\uff0c9,297,691 sessions<\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/www.yelp.com\/dataset\/challenge\"> YELP review website essays<\/a>\n<ul>\n<li>Yelp Academy Challenge Round 12<\/li>\n<li>5,996,996 review articles (2005~2018), 280,992 photos<\/li>\n<li>1,518,169 reviewers, 188,583 stores<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>PART-III &nbsp;Capstone Project of Digital marketing<\/p>\n<ul>\n<li>Data Content\n<ul>\n<li>Front-end data: customer behavior data collected from websites, traffic analysis tools and various digital media<\/li>\n<li>Background information: customer basic information and transaction records<\/li>\n<li>External data: external data collected from the Internet through online tools and web crawlers, such as the text content of social media or review sites, or website traffic comparison, keyword research, competitive analysis data, etc.<\/li>\n<\/ul>\n<\/li>\n<li>Steps of Analytics\uff1a\n<ul>\n<li>Use big data analysis platform to organize structured and unstructured data,<\/li>\n<li>Use exploratory analysis methods and data visualization tools to see the structure and trends hidden in the data,<\/li>\n<li>Ask key questions from the structure and trends,<\/li>\n<li>Use predictive and diagnostic methods to make models,<\/li>\n<li>Use simulation and optimization tools to propose feasible countermeasures to key issues,<\/li>\n<li>Prepare final presentation<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Self-Made Materials<\/span><\/strong><\/h3>\n<p>This course does not use textbooks, we will build on past self-made teaching materials, according to the needs of the curriculum re-preparation of teaching materials. The teaching materials include: &nbsp;&nbsp;<\/p>\n<ul>\n<li><a href=\"https:\/\/bap.cm.nsysu.edu.tw\/?page_id=268\">Course Website<\/a>\uff1aIntegrate self-made teaching materials and online resources to promote mutual learning (HTML)<\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=SXb1LgoFBEs&amp;list=PLSHbx5RUDxJ2ETaFSJRvhiQVJPIiO2_Jl\">Preview Video<\/a>\uff1aTo help students understand online preview content<\/li>\n<li><a href=\"https:\/\/drive.google.com\/open?id=122Lb1LwC3jv3VKJxJChCz0MnyohGnW-u\">Material<\/a>\uff1aPowerPoint slide of every unit&nbsp;<\/li>\n<li><a href=\"http:\/\/rpubs.com\/tonychuo\/AS8-0-CVM\">In-class Note<\/a>\uff1aIn-class R-Notebook of every unit<\/li>\n<li><a href=\"http:\/\/rpubs.com\/tonychuo\/AS7-1\">Homework Note<\/a>\uff1aHomework R-Notebook of every unit<\/li>\n<li><a href=\"http:\/\/140.117.69.135:4949\/A0011\/CustPolicy\/\">Online Simulate Program<\/a>\uff1aHelp students understand more complex concepts in an interactive simulation (R: Shiny)<\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=ZNC3AQj1Pv8&amp;index=1&amp;list=PLZgYls1tojKn4cRSjR2q0vVys4AWXDsKP\">Class Video<\/a>\uff1a&nbsp;It is convenient for students to review or make up course. (YouTube)<\/li>\n<\/ul>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Business Analytics Platform from College of Management, NSYSU<\/span><\/strong><\/h3>\n<p>This course will use more than 300 million data set.&nbsp;Students can use the big data computing resources in <a href=\"https:\/\/bap.cm.nsysu.edu.tw\/?page_id=1319\">Business Analytics Platform from College of Management, NSYSU<\/a> via the student account, including:<\/p>\n<ul>\n<li>All large data sets will be arranged in the Hadoop network file system in advance<\/li>\n<li>Big data can be loaded into the <a href=\"https:\/\/bap.cm.nsysu.edu.tw\/?page_id=1287\">Spark Integrated Analysis Engine<\/a> to process the data by the data frame interface,<\/li>\n<li>Big data can also be loaded into the GreenPlum distributed database and processed by the SQL interface<\/li>\n<li>In addition to the data frame and SQL interface, the<a href=\"http:\/\/cm.nsysu.edu.tw\/~msrc\/wp\/?page_id=1376\"> Integrated Analysis Platform<\/a> also provides data uploading and exploration functions.<\/li>\n<li>Through the platform, you can use a high-end interface (Keras) to use a GPU-equipped deep computing host to create artificial intelligence models<\/li>\n<li>When the user logs in for the first time, user can use the above resources through the demo program under the root directory (exercise)<\/li>\n<li>Users can upload and analyze corpus data in a semi-automated manner through the <a href=\"http:\/\/cm.nsysu.edu.tw\/~msrc\/wp\/?page_id=1297\">Text Analysis platform<\/a>. Recently, the text platform has stored text data on most pages of the PTT website in the past 10 years. Users can directly use keywords and dates to filter PO Text content and analysis.<\/li>\n<\/ul>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Online Course<\/span><\/strong><\/h3>\n<p>This course will also cooperate with the &#8220;<a href=\"https:\/\/bap.cm.nsysu.edu.tw\/?page_id=925\">Digital Marketing and Website Data Analysis Online Course Self-study Map<\/a>&#8221; of College of Management, National Sun Yat-Sen University and make full use of the online course content of well-known foreign universities. We take the following online courses (MOOC\u2019s) as references:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.coursera.org\/specializations\/digital-marketing\">Digital Marketing Specialization <\/a>offered by the University of Illinois at Urbana-Champaign\n<ul>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/marketing-digital?specialization=digital-marketing\">Marketing in a Digital World<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/marketing-analytics?specialization=digital-marketing\">Marketing Analytics in Theory<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/digital-analytics?specialization=digital-marketing\">Marketing Analytics in Practice<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/marketing-channels?specialization=digital-marketing\">Digital Media and Marketing Principles<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/www.coursera.org\/specializations\/social-media-marketing\">Social Media Marketing Specialization<\/a> offered by Northwest University\n<ul>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/what-is-social?specialization=social-media-marketing\">What is Social?<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/importance-of-listening?specialization=social-media-marketing\">The Importance of Listening<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/nurture-market-strategies?specialization=social-media-marketing\">Engagement &amp; Nurture Marketing Strategies<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/social-imc?specialization=social-media-marketing\">Content, Advertising &amp; Social IMC<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/www.edx.org\/micromasters\/berkeleyx-marketing-analytics\">Marketing Analytics<\/a> offered by the University of Berkeley,<\/li>\n<li>\n<ul>\n<li><a href=\"https:\/\/www.edx.org\/course\/marketing-analytics-marketing-measurement-strategy-0\">Marketing Measurement Strategy<\/a><\/li>\n<li><a href=\"https:\/\/www.edx.org\/course\/marketing-analytics-price-and-promotion-analytics-0\">Price and Promotion Analytics<\/a><\/li>\n<li><a href=\"https:\/\/www.edx.org\/course\/marketing-analytics-competitive-analysis-and-market-segmentation-0\">Competitive Analysis and Market Segmentation<\/a><\/li>\n<li><a href=\"https:\/\/www.edx.org\/course\/marketing-analytics-products-distribution-and-sales-0\">Products, Distribution and Sales<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/wharton-customer-analytics?specialization=business-analytics\">Customer Analytics<\/a>&nbsp;and&nbsp; <a href=\"https:\/\/www.coursera.org\/learn\/wharton-contagious-viral-marketing\">Viral Marketing and How to Craft Contagious Content<\/a> online course offered by the Wharton School of Business, University of Pennsylvania<\/li>\n<li><a href=\"https:\/\/www.edx.org\/course\/social-media-marketing-bux-qd504x-0\">Strategic Social Media Marketing<\/a> offered by Boston University<\/li>\n<li><a href=\"https:\/\/www.edx.org\/course\/marketing-analytics-columbiax-bamm-104x-0\">Marketing Analytics<\/a> offered by Columbia University<\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/foundations-marketing-analytics\">Foundations of marketing analytics<\/a> offered by French ESSEC Business School and the world-class management consulting company ACCENTURE<\/li>\n<\/ul>\n<p>We will choose appropriate teaching units, homework exercise or case study as preview materials or cite it in our teaching materials from those online course.<\/p>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Open Resource Reference<\/span><\/strong><\/h3>\n<p>This course will collect the data sets form the following open data websites to edit and manage data cases:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.kaggle.com\/\">Kaggle<\/a> Open Information Contest website<\/li>\n<li><a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets.html\">UCI Machine Learning Repository<\/a><\/li>\n<li><a href=\"https:\/\/data.gov.tw\/\">Data.Gov.TW<\/a><\/li>\n<li><a href=\"https:\/\/scidm.nchc.org.tw\/\">National Center of for High-performance Computing<\/a><\/li>\n<\/ul>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Online Course Certification<\/span><\/strong><\/h3>\n<p>We also plan to help students complete the two free online courses provided by <a href=\"https:\/\/analytics.google.com\/analytics\/academy\/\">Google Analytics Academy<\/a> and obtain certification through mutual (self) assisted learning:<\/p>\n<ul>\n<li><a href=\"https:\/\/analytics.google.com\/analytics\/academy\/course\/6\">&nbsp;Basic knowledge of Google Analytics<\/a><\/li>\n<li><a href=\"https:\/\/analytics.google.com\/analytics\/academy\/course\/7\">Advanced concepts of Google Analytics<\/a><\/li>\n<\/ul>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Other Online Resource<\/span><\/strong><\/h3>\n<p>In addition, we also plan to guide students to use the following free trial online services during the course:<\/p>\n<ul>\n<li>Establish group and personal websites through<a href=\"https:\/\/gsuite.google.com\/learning-center\/products\/sites\/get-started\/#!\/\">Google Site<\/a><\/li>\n<li>Collect and analyze website traffic data through<\/li>\n<li><a href=\"https:\/\/analytics.google.com\/analytics\/academy\/\">Google Analytics<\/a> and <a href=\"https:\/\/cloud.google.com\/bigquery\/\">Google BigQuery<\/a><\/li>\n<li>Compare various traffic indicators of competing websites through <a href=\"https:\/\/www.similarweb.com\/\">SimilarWeb<\/a><\/li>\n<li>Do keyword research and search engine optimization through <a href=\"https:\/\/serpstat.com\/\">Serpstat<\/a><\/li>\n<li>Monitor social media content through <a href=\"https:\/\/brand24.com\/\">Brand24<\/a><\/li>\n<li>Rent and set up a cloud deep learning host through <a href=\"https:\/\/cloud.google.com\/gcp\/?hl=zh-tw\">Google Cloud Platform (GCP)<\/a><\/li>\n<li>Practice the use of automated machine learning systems through <a href=\"https:\/\/www.ibm.com\/watson-analytics\">IBM Watson Analytics<\/a><\/li>\n<li>Practice operating social network monitoring and text content analysis through <a href=\"https:\/\/www.ibm.com\/support\/knowledgecenter\/en\/SS4QC9\/com.ibm.solutions.wa_an_overview.2.0.0.doc\/wa_an_hlp_wasm_intro.html\">IBM Watson Analytics for Social Media<\/a><\/li>\n<\/ul>\n<hr>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Reference<\/span><\/strong><\/h3>\n<p>Here are reference of this course\uff1a<\/p>\n<ol>\n<li>Chapman, C., &amp; Feit, E. M. (2015). R for marketing research and analytics. Springer.<\/li>\n<li>James, G., Witten, D., &amp; Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer.<\/li>\n<li>Kaushik, A. (2009) Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex.<\/li>\n<li>Stokes, Rob. (2013). eMarketing: The essential guide to marketing in a digital world. Quirk eMarketing.<\/li>\n<\/ol>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Digital Marketing and E-commerce Data Case Study Course Arrangement The two central ideas of modern marketing are: &#8220;Digital marketing of customer value-centric&#8221; and &#8221; Strategic planning of data analysis-based&#8221;. Therefore, in addition to using data cases, this course allows students to practice customer value management and digital marketing tools. Also, We plan to cooperate with domestic e-commerce companies, and ask the company to provide front-end (customer behavior data collected from websites, traffic analysis tools and various digital media) and back-end (customer basic data and transaction records) data to cooperate with us through online Tools and web crawlers collect external data from the Internet (text content of social media or review sites, or website traffic, keyword research, competitive analysis data, etc.), and integrate them into a combination of front-end, back-end and external data, and at the same time A huge relational data set with structured and unstructured data, used to do the Capstone Project of the entire digital marketing and e-commerce micro-learning process; imitating the most common situation encountered by the planners of ordinary companies, the Capstone Project will only to provide information without specifying the target of analysis, students need to comprehensively use the analysis tools and methods they have learned during the course of study: &nbsp;Use a big data analysis platform to organize structured and unstructured data &nbsp;Use exploratory analysis methods and data visualization tools to see the structure and trends hidden in the data &nbsp;Ask key questions from the structure and trends, &nbsp;Use predictive and diagnostic methods as well as simulation and optimization tools to propose feasible countermeasures to key issues. &nbsp; Course Syllabus PART-I Digital Media And Online Tool Operation Practice Digital Marketing The Business Implication of Big Data Company WebSite Web Analytics by Google A&#8230;<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":513,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-1312","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/1312","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1312"}],"version-history":[{"count":10,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/1312\/revisions"}],"predecessor-version":[{"id":1436,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/1312\/revisions\/1436"}],"up":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/513"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1312"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}