SEARCH ENGINE OPTIMIZATION AND ANALYTICS
2018/2019, Semester 1
School of Computing (Information Systems & Analytics)
Modular Credits: 4
This course teaches the concepts, techniques and methods to analyse and improve the visibility of a website or a web page in search engines via the “natural” or un-paid (“organic” or “algorithmic”) search results. Students will be taught concepts and knowledge in terms of how search engines work, what people search for, what are the actual search terms or keywords typed into search engines, which search engines are preferred by their targeted audience, and how to optimize a website in terms of editing its content, structure and links, and associated coding to both increase its relevance to specific keywords and to remove barriers to the indexing activities of search engines. Importantly, the module will emphasize the relationship of search engine optimization to digital marketing in terms of building high quality web pages to engage and persuade, setting up analytics programs to enable sites to measure results, and improving a site's conversion rate.
Completed 80 MCs
Week 1 (August 14) Overview of SEO and Course Logistics,
Overview of BT4212
Basics of Internet Marketing
What is SEO?
Three major search engines: Google, Bing, and Yahoo
What is “Keywords Research” and how to conduct keywords research?
What is the well-known long tail concept and how it relates to SEO?
Assignment 0 =>
Maximum 5 students in one group before Week 3’s class. Group of 4 is strongly preferred and group of 5 is only allowed for assigning the few students who cannot find a group to join.
When you form a team, it is better that you have at least one teammate who has stronger technical expertise, including Python programming, HTML and blogging.
It is also better that at least some of your team members use Windows, rather than all of you use Mac.
Week 2 (August 21):
Dr. Huang needs to attend the largest annual data mining conference in UK. Makeup class will be provided as the following way. In the past, the last week’s class is a final group presentation. I will still provide lecture in Week 13. The final group presentation will be in Week 14. You are encouraged to attend but attendance won’t be required and participation won’t be graded.
Team Assignment 1 => (1) Conduct your keyword research and decide the topic of your blog, (2) publish your first article on 2-3 blogging sites, (3) setup Google analytics and Rank Tracker tools to track your blog’s search ranking.
Week 3 (August 28) On-Page SEO
One-Page SEO Factors
H1 H2 paragraphs
“Similarity” to Keywords in Search Phrases
Length of Articles
Google Pagespeed Test
Week 4 (September 4) The most important off-page SEO: Link Building Strategies
White Hat Link Building Strategies
Black Hat Link Building Strategies: Link pyramid, Link circle, Article directories, and Spams.
History of Google Updates
Team Assignment 2 distributed => (1) Finishing up Week 2 assignment, (2) Also post at least one article per week. (3) Prepare a proposal of field experiment.
Week 5 (September 11) Analytics: Causality, Randomized Experiment, Field Experiment, and other methods.
This week’s class is more about analytics and is less about SEO. I will cover important foundations and methods for field experiments or other methods that provides much stronger empirical evidence of causality than OLS you have used many times.
Randomized experiment and procedure of conducting field experiment will be covered. If the time allows, I will cover the following subjects: (1) instrumental variables (2) regression discontinuity.
Team Assignment 1 Due. Before the class of Week 5, I will ask TA to check the keywords performance of your blogs. More details will be announced later.
Week 6 (September 18) Other Factors of SEO,
Top Social Network Sites: although not that critical for SEO, but this is important for you to learn about digital/social marketing and it will be fun. Facebook, Twitter, Google Plus, Pinterest, Stumbleupon, among others.
(fake) Social sharing.
Recess Week (September 25) Assignment 2
Field Experiment Proposal Due
. I will try to provide feedback asap and you can start conduct experiment asap you hear from me. We will have about 1.5 months to see the treatment effect and SEO efforts.
Week 7 (October 2) Online Advertising
Google Adwords and Facebook Ads
Pay-per-click versus pay-per-impressions and other advertising metrics.
Week 8 (October 9) More on Related Statistical Models
Applying DID on your dataset and compare the estimated treatment effects with the realized treatment effects from randomized experiment.
Predicting the traffic or ranking of your blogs by Gradient Boosting Machine (GBM, XGBoost and LightGBM).
Week 9 (October 16) Case Studies of Analytics based on SEO and traffic, Part I
Because BT4212 is designed for BA students, I will cover more about the analytics and decision making models, less about the traditional technical aspects of SEO. So I plan to use the following three weeks to cover case studies or academic studies related to SEO and online advertising. The exact topics will be announced later.
Selected papers will be from business schools by advanced regressions, field experiments, or from computer science by data mining methods.
Team Assignment 3 Distributed=> Applying analytics for prediction. For example, predicting the page ranking of search results of your focal keywords by OLS or XGBoost…etc.
Week 10 (October 23) Case Studies of Analytics based on SEO and traffic, Part II
Week 11 (October 30) Case Studies of Analytics based on SEO and traffic, Part III
Week 12 (November 6): National Holiday.
Team Assignment 3 DUE
Week 13 (November 13) Non-traditional and Multimedia SEO
Non-traditional SEOs on websites such as Youtube, Instagram, Taobao (merchant results), Chinese SEO on Baidu, and others.
Week 14 (November 20) Final Group Project Presentation of Your Experimental Results. Final Report Due Right before the final presentation.
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week