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MARK5814 Digital Marketing
Creation date：2024-06-11 15:51:17
Belonging category：市场营销Marketing
Digital Marketing

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MARK5814 Digital Marketing
Assessment Information
1. Class Participation (10%). 2
2. Case Study (15%).
3. Individual Presentation (15%).
3
5
4. Group Project – Digital Marketing Plan (30%). 7
5. Individual Assignment – Digital Marketing Evaluation Report (30%). 13
MARK5814 T1-2024 Assessment Information
7 February 2024
2
1. Class Participation (10%)
Class participation will provide opportunities for peer learning which will strengthen
students’ ability to actively argue their decisions regarding digital marketing issues. This
will foster the development and understanding for alternative approaches and provide an
opportunity for reflective learning. This is designed to stimulate and encourage new ideas
through the process of class discussion.
Your participation includes the responsibility to share your understanding and judgment
with the class in order to advance the group's collective skills and knowledge. This means
we will need your active participation to make the class a rewarding experience for
everyone. If you read the materials assigned for every class, thoroughly prepare cases, take
part in pertinent discussions, listen to others with respect, you will be rewarded.
Marking Criteria Weighting
Evidence of preparation (e.g., advance reading of the related
materials, such as book chapter, journal article or case) and
ability to discuss/debate the issues.
35
Quality of expression and presentation of arguments. 30
Quality of contribution during the class and group discussion
(e.g., using theory to explain real-world phenomena, using
theory to propose a solution or identify a problem).
35
Total 100
Students are expected to thoroughly read any case discussion materials, textbook chapters
or handout materials prior to the lecture and actively participate in discussions. Each
week, students will be allocated textbook chapter or recommended article to read prior to
attending the class. Active participation refers to making intelligent comments and
providing arguments related to the discussion topic. You are also required to respond to
the lecturer/tutor’s questions as much as you can during the class. Additionally, students
should respond to the responses of others in the class.
Moreover, each week, students will be asked to provide written comments or questions
during class/tutorial exercises, either as a group or as an individual.
If you are unable to attend the tutorial on a regular basis during the term and have a valid
reason, please email Dr Terrence Chong ([email protected]) by 15 February
2024 to discuss an alternative arrangement.
MARK5814 T1-2024 Assessment Information
7 February 2024
3
2. Case Study (15%)
Due date: 16:00, Monday, 4 March 2024.
Word limit: Maximum 750 words (All included except UNSW assignment cover sheet,
references, and appendices).
Submission: To TurnitinTM via Moodle.
Task:
You are required to analyse the case:
Atomberg Technologies - Leveraging Digital Marketing to Accelerate Its
Omnichannel Strategy
https://hbsp.harvard.edu/tu/2d11e22c
Please copy the entire link into a new browser window to get access to the case study.
Based on the information provided in the case and your own research, answer all two
questions below. Use theory and/or secondary data, e.g., published government report,
consulting report to form critical discussions and support your arguments.
1. Conduct a situation analysis for Atomberg to identify a key marketing problem or
opportunity in the Australian market if it decides to officially enter it.
2. Which two digital channels (as noted in case Exhibit 7) would you most recommend
for launching a marketing campaign targeting the Australian market? Give reasons.
Required format:
• The School of Marketing Assignment Cover Sheet as the first page.
• Question 1 answer.
• Question 2 answer.
• References.
You are free to include an appendix; however, no mark is allocated to the information
contained in the appendix.
Case category
Tag cloud
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EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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