Credit points


Campus offering

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MKTG207 Marketing Toolkit OR MKTG100 Marketing: Creating and Capturing Customer Value


MKTG315 Marketing Analysis: Evidence-Based Decisions

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

Unit rationale, description and aim

This unit introduces the conceptual and practical issues in developing strategies and models to run a business successfully. It also provides students necessary knowledge and skills to facilitate business decision making. Considering a wide range of contemporary problems and using data and advanced marketing analytics techniques, students will also translate the analytic models into ethical and socially responsible competitive strategies for business owners and social enterprises.

Marketing analytics help students understand how well an organisation’s marketing investments and initiatives may achieve key performance indicators (KPIs). Accordingly, an organisation can formulate and adjust its marketing strategy, investments and budgets for higher positive impact. Marketing analytics also helps organisations to improve its social CRM strategies and to anticipate crises.

The aim of this unit is to prepare students for marketing analyst roles and assist them to develop skills and knowledge in managing an organisation’s data through MIS (or Big data) to make strategic and tactical decisions conforming to high ethical standards.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Explain contemporary marketing trends with models, frameworks and theories to identify a real-world marketing problem (GA5, GA8).

LO2 - Develop competitive marketing analytics strategy for real-world organisations considering issues related to different cultures, including indigenous communities (GA3, GA5).

LO3 - Apply the data analytics techniques to solve contemporary marketing and business problems taking into consideration issues of sustainability and subsidiarity (GA5, GA6).

LO4 - Utilise contemporary marketing analytics techniques and technologies to communicate feasible marketing strategies to relevant stakeholders (GA5, GA10).

LO5 - Critically analyse comparative practices in the industry and reflect how this comparison would improve performance outcomes (GA4, GA5).

Graduate attributes

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA6 - solve problems in a variety of settings taking local and international perspectives into account

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.


Topics will include:

  • Marketing analytics: Picking the Tools of the Trade
  • The marketing analytics and big brands
  • Marketing consumer, audience, content and engagement analysis
  • Search analysis and mobile analytics for marketing campaigns
  • Using online data to anticipate a crisis and social CRM
  • Improving customer service and new product launch through analytics run
  • Digital strategy and Business Intelligence
  • Advance topics on Data analytics
  • Ethical and social aspects of digital marketing analytics 

Learning and teaching strategy and rationale

The learning and teaching strategy is based on student engagement in the learning process by participation in workshops or equivalent and in practical activities designed for each workshop. These workshops and equivalent support students to actively participate in the construction and synthesis of knowledge both individually and in small groups. By taking part in activities, students will systematically develop their understanding of the key aspects of marketing metrics analytics. Students will be involved in practical marketing analytics projects to experience the real world issues in managing and studying metrics data to determine the ROI of marketing activities e.g. calls-to-action, blog posts, channel performance, and thought leadership pieces, and to identify opportunities for improvement. The experiential approach underpinning the learning and teaching strategy for this unit extends to practical analytical approaches used in marketing metrics based on ‘real world’ examples.

Mode of delivery: This unit is offered in different modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In attendance mode, students will require face-to-face attendance in specific physical location/s. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops, most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.

Online Mode

In an online mode, students engage in asynchronous learning and participate in the construction and synthesis of knowledge, while developing their knowledge. Students are required to participate in a series of online interactive activities to enhance their learning including knowledge checks, discussion boards and self-paced exercises. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

ACU Online

This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous weekly discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.

Assessment strategy and rationale

In order to pass this unit, students are required to demonstrate mastery of all learning outcomes and achieve an aggregate mark of at least 50%. Marking of each assessment task will be in accordance with rubrics specifically developed to measure student level of achievement of the learning outcomes for each assessment item.

Students will be awarded a final grade which signifies their overall achievement in the unit. The assessment strategy for this unit allows participants to sequentially develop their knowledge and skills in marketing analytics to the point where they can understand, examine and apply the data analytics models & concepts along with relevant technologies to formulate digital strategies for real-world organisations. At the same time, students will also need to demonstrate ability to identify, describe, analyse and evaluate the use of key marketing metrics in an existing organisation. 

The assessment tasks for this unit are designed for students to demonstrate their achievement of each learning outcome as indicated in the following Assessment Table.

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Assessment Task 1: Marketing analytics auditing report

Requires students to demonstrate their ability to understand and explain a range of conceptual, practical and ethical issues in developing marketing analytics strategies and models for organisations.

Submission Type: Individual

Assessment Method: Diagnostic Report

Artefact: Written report


LO1, LO2

GA3, GA5, GA8

Assessment Task 2: Portfolio of Engagement

From weeks 4-9 students will actively participate in online discussion forums and online activities. Students will be evaluated on a combination of their real time engagement in the unit via discussion board questions, responses to postings and evidence of successful engagement in online activities

Submission Type: Individual

Assessment Method: online engagement and completion of regular learning tasks

Artefact: Portfolio evidencing engagement


LO1, LO5

GA4, GA5, GA8 

Assessment Task 3: Marketing analytics strategy report

Requires students to apply data analytics and relevant technologies to design and develop a marketing analytics strategy for real-world organisations. A live case will be introduced to students, and an industry expert would provide a presentation.

Submission Type: Individual

Assessment Method: Live case

Artefact:  Written report


LO3, LO4

GA5, GA6, GA10

Representative texts and references

Cao, G. Duan, Y., Banna, A. 2019, A dynamic capability view of marketing analytics: Evidence from UK firms, IMM, 76, pp 72-83

Grigsby, M., 2018. Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques. Kogan Page Publishers.

Venkatesan, R., Farris, P.W. and Wilcox, R.T., 2021. Marketing Analytics: Essential Tools for Data-driven Decisions. University of Virginia Press.

Kehal, M. and El Alfy, S. eds., 2021. Data Analytics in Marketing, Entrepreneurship, and Innovation. CRC Press.

Petrescu, M., Krishen, A. and Bui, M., 2020. The internet of everything: implications of marketing analytics from a consumer policy perspective. Journal of Consumer Marketing.

Hallikainen, H., Savimäki, E. and Laukkanen, T., 2020. Fostering B2B sales with customer big data analytics. Industrial Marketing Management86, pp.90-98.

Sarkar, M. and De Bruyn, A., 2021. LSTM response models for direct marketing analytics: Replacing feature engineering with deep learning. Journal of Interactive Marketing53, pp.80-95.

Rahman, M.S., Hossain, M.A. and Fattah, F.A.M.A., 2021. Does marketing analytics capability boost firms' competitive marketing performance in data-rich business environment?. Journal of Enterprise Information Management.

Vollrath, M.D. and Villegas, S.G., 2021. Avoiding digital marketing analytics myopia: revisiting the customer decision journey as a strategic marketing framework. Journal of Marketing Analytics, pp.1-8.

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