Unit rationale, description and aim

Modern marketing decisions are shaped by technology, so behind every campaign, ad or customer interaction sits a system that gathers and translates data into insights. This unit helps students make sense of these systems and understand how they influence strategy, customer experience and business success. As businesses increasingly rely on complex marketing technology stacks, graduates who can evaluate these systems bring essential value to organisations.

The aim of this unit is to develop student's ability to evaluate and apply marketing technology stacks and visualisation tools to support ethical and effective marketing practice. Students will explore how marketing technologies contribute to ethical decision-making, customer outcomes and organisational performance, while considering privacy, inclusion, Indigenous data sovereignty and the dignity of all stakeholders. Students will examine the structure and role of marketing technology stacks and visualisation tools, including CRM systems, automation platforms, analytics dashboards, content management systems and data visualisation frameworks. The unit will build student's knowledge to analyse how marketing technologies operate, how they support strategy and performance, and how they can be applied responsibly in real-world business settings.


2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

Nil

Incompatible

MKTG319 Marketing Analytics

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.

Describe the key components of marketing technolog...

Learning Outcome 01

Describe the key components of marketing technology stacks and visualisation frameworks used in contemporary marketing practice
Relevant Graduate Capabilities: GC2, GC10

Explain how marketing technologies contribute to c...

Learning Outcome 02

Explain how marketing technologies contribute to customer experience, business performance, and strategic decision-making
Relevant Graduate Capabilities: GC2, GC8, GC10

Analyse the ethical, social and cultural considera...

Learning Outcome 03

Analyse the ethical, social and cultural considerations involved in the use of marketing technologies, including privacy, inclusion and Indigenous data sovereignty
Relevant Graduate Capabilities: GC2, GC3, GC8, GC9

Evaluate the role and application of different mar...

Learning Outcome 04

Evaluate the role and application of different marketing technology platforms in supporting organisational objectives and responsible marketing practice
Relevant Graduate Capabilities: GC2, GC3, GC8, GC9

Communicate insights and recommendations about mar...

Learning Outcome 05

Communicate insights and recommendations about marketing technology use through clear, well-structured written analysis
Relevant Graduate Capabilities: GC2, GC9, GC11

Content

Topics will include:

  • Introduction to marketing technology and martech stacks (the martech ecosystem, stack architecture and its strategic role)
  • Customer data, privacy, and ethical data management (data collection, consent, privacy laws)
  • Customer relationship management (CRM) systems (customer databases, segmentation, personalisation, customer lifecycle management
  • Marketing analytics and visualisation (interpreting data outputs, KPI reporting, dashboards, attribution, storytelling with data and visual insights)
  • Marketing automation and campaign management (automated workflows, lead nurturing, campaign planning, execution, and monitoring)
  • Content and experience management platforms (CMS systems, personalisation engines, digital experiences)
  • Social media, influencer and community platforms (social media management, influencer marketing, customer advocacy and community building)
  • Artificial intelligence, predictive analytics and emerging martech (ai-powered martech functions, predictive analytics, machine learning, chatbots)
  • Martech strategy, integration, and platform selection (building a martech stack, vendor selection, integration issues, organisational capabilities)

Assessment strategy and rationale

The assessments in this unit are designed to reflect real-world marketing tasks, helping students engage with marketing technologies as professionals do. In order to pass this unit, students must demonstrate achievement of all learning outcomes and obtain an aggregate mark of at least 50%.

Assessment 1 focuses on interpreting marketing data, applying visualisation principles, and communicating insights to stakeholders. It builds essential analytical and communication skills used in data-driven marketing environments.

Assessment 2 builds on this by requiring students to evaluate, design, and justify a Martech stack for a business scenario. This task develops higher-order thinking, ethical reasoning, and problem-solving skills, encouraging students to integrate multiple technologies into a strategic solution.

The sequencing of the assessments reflects increasing complexity. Assessment 1 introduces data interpretation without requiring technical software skills, laying the groundwork for the more complex task in Assessment 2, which involves broader strategic and evaluative thinking.

The weightings—45% for Assessment 1 and 55% for Assessment 2—reflect this progression. The structure ensures that early success helps build student confidence while recognising the integrative and strategic demands of the final task.

Overall, the assessments aim to equip students with practical skills and critical thinking needed to navigate and apply marketing technologies effectively in professional contexts. The unit employs a two-lane approach for incorporating AI into selected assessment items, teaching students how to use AI ethically.

Overview of assessments

Assessment Task 1: Business Sceanrio Analysi...

Assessment Task 1: Business Sceanrio Analysis

This assessment is an authentic problem-solving task that requires students to analyse marketing data outputs and communicate actionable insights for business decision-making.

Students are provided with a business scenario and pre-prepared data summaries, including tables and visual outputs (e.g., dashboards, charts). They will interpret the data, identify key trends and issues, and present their analysis in a written report aimed at a non-technical stakeholder.

The task develops students’ ability to connect marketing technology outputs to business performance, apply ethical considerations, and communicate findings clearly in a professional context.

Submission Type: Individual

Assessment Method: Business Scenario Analysis Report

Artefact: Portfolio Written report

Weighting

45%

Learning Outcomes LO1, LO2, LO3, LO4, LO5
Graduate Capabilities GC2, GC8, GC9, GC11

Assessment Task 2: Marketing Technology Stack Pro...

Assessment Task 2: Marketing Technology Stack Propsoal

This assessment is an authentic applied project that requires students to design a suitable marketing technology stack for a provided business scenario.

Students will evaluate the organisation’s marketing needs, customer challenges, and strategic goals.

Based on this analysis, they will propose and justify an appropriate combination of marketing technology categories (e.g. CRM, automation, analytics, content, social platforms, data management).

Students will demonstrate their understanding of how stack components integrate, address ethical and cultural considerations, and support responsible marketing practice.

The task develops students’ ability to apply conceptual Martech knowledge to real-world business situations and present a clear, justified proposal suitable for organisational stakeholders.

Submission Type: Individual

Assessment Method: Applied project

Artefact:  Written Proposal

Weighting

55%

Learning Outcomes LO1, LO2, LO3, LO4, LO5
Graduate Capabilities GC2, GC3, GC8, GC9, GC10

Learning and teaching strategy and rationale

Campus Attendance (C)

This unit uses an active, problem-based learning (PBL) approach to introduce students to marketing technology. Learning is scaffolded using simplified business scenarios and data tasks to build understanding and confidence. Weekly face-to-face workshops combine interactive lectures, group discussions, worked examples, and problem-solving exercises. Students explore real-world Martech challenges and apply concepts in collaborative settings. Activities are designed to strengthen critical thinking, communication, and ethical reasoning. This approach supports undergraduate learners by providing hands-on opportunities to apply knowledge ahead of authentic assessments.

Online Unscheduled (OU)

In the online mode, students engage in active learning through scaffolded PBL tasks. Weekly modules offer structured learning supported by milestones to ensure steady progress. Students build Martech knowledge by working through mini-lectures, case studies, datasets, and quizzes. Interactive elements and real-world scenarios allow for application of concepts, data interpretation, and ethical reflection. This flexible strategy empowers students to manage their learning while remaining aligned with unit outcomes and the practical demands of assessments.

Representative texts and references

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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