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Marketing Analytics (MKTG90039)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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April
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It has become increasingly important to know how marketing actions translate into revenue and profit growth. Within the big data phenomenon, new ways of analysing data (e.g. text mining), running social media experiments and gleaning insights on customers’ digital behaviours have taken centre stage to inform business decision making. Simply put, market research, the methods that surround it, and the inferences derived from it have put marketing “on the map.” Although these methods are here to stay, as big data becomes mainstream, it is fundamentally altering the way we collect and analyse data to demonstrate ROI.

“Marketing Analytics” does not teach how to do marketing. Instead, Marketing Analytics is a purely data-driven subject, which explores the new ways to harness digital customer and market data and enhance marketing decision-making. In this subject, students will (i) learn reflective and predictive marketing analytics, (ii) learn how to choose which approach to use to uncover what type of market insight, (iii) apply new learnings hands-on to real-life datasets (iv) and draw managerial implications on ROI, customer satisfaction, and virality.

 

Intended learning outcomes

On completion of this subject, students should be able to:

  • Understand how to derive insights from new secondary data (e.g. text mining).
  • Use predictive analytics (e.g., online field experiments) to test the viability of different marketing actions.
  • Understand how to predict the impact of marketing actions using count-, choice- at multi-level models.
  • Segment markets of customers using a variety of segmentation methods and choose segments to target using a set of criteria.
  • Conduct market structure analysis by mapping customers' perceptions of brands and products in a market, and translate the maps into different positioning choices.
  • Model the impact of alternative marketing mix combinations on sales, accounting for moderational shifts and interactions to optimise the mix.

Generic skills

  • High level of development: problem solving; statistical reasoning; application of theory to practice; interpretation and analysis; synthesis of data and other information; evaluation of data and other information; use of computer software; accessing data and other information from a range of sources.
  • Moderate level of development: written communication; critical thinking; receptiveness to alternative ideas.
  • Moderate level of development: collaborative learning; team work.

Last updated: 3 November 2022