24761 Data-Driven Insights
The Complete Study & Assessment Guide · Spring 202624761 Overview
- University of Technology Sydney
- Spring 2026
- Marketing
- Postgraduate
- Weekly recorded lectures, fortnightly workshops
- Three individual coursework tasks
What the subject is about
Data-Driven Insights at the University of Technology Sydney teaches you to turn information from many sources into marketing insights that a manager can act on. You work as a Reporting and Insights Analyst for one assigned brand across the whole Spring session, which in Spring 2026 is Tesla Australia.
- Assessed by A secondary data report (30%), an AI-assisted insights report (30%) and a poster presentation with Q&A (40%), all individual.
- Hardest step Moving from describing what a source says to an insight that explains why it matters for the brand.
- Do first Learn the CRAAP test and triangulation early: validation and triangulation carry their own rubric line in the first report.
What you get in the full 24761 Study & Assessment Guide
- 11 chapters, 51 pages — every assessed topic, week by week
- 28 original practice questions, each with a model answer
- 11 worked examples, solved the way 24761 marks
- 16 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Task 3 (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How 24761 is assessed
| Component | Weight | Format |
|---|---|---|
| Task 1: Marketing Secondary Data Insights (Individual) | 30% | Written report of 1,400 to 1,500 words on the assigned brand, using the subject template; due Week 6 |
| Task 2: AI-Assisted Marketing Insights (Individual) | 30% | Written report of 1,100 to 1,200 words analysing an extra secondary data source with generative AI and recommending a primary data approach; due Week 9 |
| Task 3: Poster Presentation and Q&A (Individual) | 40% | Single-page A1 portrait poster submitted as a PDF in Week 11, then presented in the Week 12 workshop with an individual Q&A |
The three weights, 30%, 30% and 40%, appear in the assessment overview, on each Canvas assignment page and on the Workshop 1 slides, and they add to 100%. The Workshop 1 slides state that you need at least 50% of the total assessment to pass; no individual task is described as a hurdle. The overview notes that due dates vary with your allocated workshop, so check your own assignment page. The poster brief describes a Q&A of about three minutes, while the Workshop 1 slides give three to five minutes.
Assessment structure
What other students are asking about 24761
- Where can I find lecture notes or study guides for 24761 at UTS?
- The 24761 study guide in AskSia Library covers Data-Driven Insights in 11 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in 24761?
- No final exam is listed in the offering this AskSia Library guide is built from; 24761 is assessed by Task 1: Marketing Secondary Data Insights (Individual) 30%, Task 2: AI-Assisted Marketing Insights (Individual) 30%, Task 3: Poster Presentation and Q&A (Individual) 40%.
- What does 24761 cover on secondary data sources and the marketing mix?
- In AskSia Library's 24761 guide, the Secondary Data Sources and the Marketing Mix chapter explains internal vs external sources · government, industry, company, customer, search and academic sources · sources by marketing domain and mix element · (Week 2).
- What does 24761 cover on validating sources with the craap test?
- In AskSia Library's 24761 guide, the Validating Sources with the CRAAP Test chapter explains why secondary data needs validation · currency, relevance, authority, accuracy and purpose · bias · (Week 3).
- What does 24761 cover on triangulating marketing evidence?
- In AskSia Library's 24761 guide, the Triangulating Marketing Evidence chapter explains data, methodological, investigator and theoretical triangulation · consistent vs contradictory evidence · (Week 3).
Current 24761 dates
| Date | Item | Control |
|---|---|---|
| Thursday of Week 5 | Census date | As shown in the subject schedule. |
| Friday 4 September 2026, 11.59 pm | Task 1 report due | Week 6. The overview says due dates vary with your allocated workshop. |
| 30 September 2026, 11.59 pm | Task 2 report due | Week 9, after the mid-session StuVac. |
| 14 October 2026, 11.59 pm | Task 3 poster PDF due | Week 11 on the Canvas assignment page. |
| Week 12 workshop (week of 19 October) | Poster presentation and Q&A | Held in your Workshop 6 session. |
Dates are as published in the Canvas assignment pages, the assessment overview and the subject schedule for the Spring 2026 offering. Confirm exact deadlines and submission settings in the live LMS.
What 24761 covers
24761 runs over twelve teaching weeks in Spring 2026, with recorded lectures every week and six workshops from Week 2. Chapters 1 to 9 follow the Week 1 to 6 lectures, from what data is, through secondary sources, validation, triangulation, charts, interpretation, insights and problem framing, to primary research methods.
Chapters 10 and 11 prepare the AI-assisted report and the poster, using the assignment briefs and the later weekly topics.
Marketing Data and Its Types
data forms · nominal, ordinal, discrete and continuous · data, information and insight · primary vs secondary · (Week 1)Data before analysis
The first week defines data as raw, unprocessed facts, numbers, observations or symbols, and it reminds marketers that customers leave data in four forms: numbers, text and images, audio such as call recordings, and sensory experience such as taste or smell.
- What counts as data, and public versus private data
- Numerical, textual and visual, audio and sensory data
- Nominal, ordinal, discrete and continuous levels
- Which summaries each level allows
- Data, information and insight as separate products
- Primary versus secondary marketing data
Secondary Data Sources and the Marketing Mix
internal vs external sources · government, industry, company, customer, search and academic sources · sources by marketing domain and mix element · (Week 2)Borrowed evidence
Week 2 treats secondary data as information that already existed before your question, gathered by another person or organisation for a different reason.
- What makes data secondary, and how primary data can become secondary
- Gains and risks of relying on secondary data
- Internal versus external secondary sources
- Government, industry, company, customer, search and academic sources
- Peer-reviewed journals versus trade press and specialist platforms
- Choosing sources by marketing mix element and marketing domain
Validating Sources with the CRAAP Test
why secondary data needs validation · currency, relevance, authority, accuracy and purpose · bias · (Week 3)Why borrowed data needs checking
Secondary data was collected for someone else's purpose, so its quality, age and slant vary from source to source.
- Why secondary data must be validated before use
- What validation means for a specific marketing problem
- Currency, relevance, authority, accuracy and purpose
- Bias and the purpose behind a source
- Scoring and comparing several sources
- Writing validation into a report sentence
Triangulating Marketing Evidence
data, methodological, investigator and theoretical triangulation · consistent vs contradictory evidence · (Week 3)Several views of one issue
Triangulation means investigating one marketing issue through more than one source, method, analyst or theory, so that the weakness of any single view does not decide the conclusion.
- What triangulation is and why it raises credibility
- Data triangulation across sources, times and places
- Methodological triangulation between and within methods
- Investigator and theoretical triangulation
- Handling consistent versus contradictory evidence
- Checking that agreeing sources are really independent
Choosing the Right Data Visualisation
data type and purpose · comparison, relationship, composition and distribution charts · common chart mistakes · (Week 4)Why charts work
The Week 4 lecture names two routes for processing information, systematic and heuristic, and argues that charts help because visuals are easier to understand than numbers or text, simple to share and quicker to draw conclusions from.
Three questions, four purposes
Before choosing a chart, ask what type of data you hold, how many variables must be shown and what one message the reader should take away.
- Systematic and heuristic processing of visual information
- Three questions to ask before choosing a chart
- Comparison, relationship, composition and distribution
- Reading a histogram: mode, median and tail
- Word clouds, radar charts, bubble charts and perceptual maps
- When a chart choice distorts the message
Interpreting Data: Signals, Correlation and Causation
descriptive, exploratory and causal reading · patterns, trends and relationships · correlation vs causation · reading industry reports · (Week 4)Three depths of reading
Week 4 treats interpretation as giving data meaning so that it answers a question and supports a decision.
- Descriptive, exploratory and causal reading
- Patterns, trends and relationships as signals
- Positive, negative and zero correlation
- Mean, median, mode and interquartile range
- Selection effects and reversed direction
- Designing a test before claiming an effect
Marketing Insights and Thematic Analysis
findings vs insights · customer, brand, market and competitive insights · sentiment analysis and social listening · six steps of thematic analysis · (Week 5)What makes an insight
Week 5 defines a marketing insight as an actionable discovery about consumers or markets drawn from several data sources. Data says what is happening; an insight explains why and leads to a decision.
- Findings versus marketing insights
- Customer, brand, market and competitive insights
- Braun and Clarke’s six phases of thematic analysis
- Deductive versus inductive and semantic versus latent coding
- Sentiment analysis, social listening and competitor analysis
- Certainty and emotion in consumer language
Finding Marketing Problems and Opportunities
reactive vs proactive monitoring · 5 Whys · gap and perception gap analysis · unmet needs and white space · (Week 5)From insight to a decision
The second half of Week 5 asks what an insight is for.
- Why an insight must lead to a decision
- Market problems versus internal marketing challenges
- Reactive and proactive monitoring of secondary data
- Root-cause analysis with the 5 Whys
- Gap analysis and perception gaps
- Where opportunities come from and unmet needs analysis
Primary Data Methods for Validating Insights
interviews, focus groups, ethnography and surveys · experiments, A/B tests and blind tests · choosing a method · (Week 6, Assessment 2)Why primary data enters the story
Week 6 turns from evidence that already exists to evidence a firm gathers first-hand for one question.
- What makes data primary, and why it complements secondary evidence
- Interviews, focus groups and ethnography for exploring the why
- Survey question types for measuring how many
- Experiments: independent and dependent variables, control groups, random assignment
- A/B testing and blind testing in marketing
- Matching research objectives to methods
AI-Assisted Analysis and Critical Evaluation
iterative prompting · verifying AI output · limits and bias · from AI findings to a refined problem or opportunity · (Week 7, Assessment 2)A tool, not an analyst
The second report asks you to analyse one more secondary source with a generative AI tool, then show your own judgement about the output.
- Generative AI in data-driven insights
- Iterative prompting and the evidence log
- Checking AI counts, quotes and themes
- Bias and limits in forum and comment data
- Support, refine or challenge the earlier issue
- Section word guides for the second report
Data Storytelling and the Insight Poster
What, So What, Now What · narrative for managers · A1 poster layout · marketing mix recommendation · Q&A · (Weeks 8 to 12, Assessment 3)From analysis to persuasion
The later lectures cover visual communication, storytelling and persuasive reporting, and they all lead to one task: an A1 poster that tells your whole project to a management team, followed by a short individual Q&A in the Week 12 workshop.
- Visual communication, storytelling and persuasive reporting
- Borton’s What, So What, Now What model
- A1 portrait poster format and compulsory details
- Laying out zones for a managerial reader
- Recommendations tied to the marketing mix
- Preparing for the individual Q&A
The subject assesses outcomes from the Master of Marketing program, and its four learning outcomes run from synthesising information across sources, through validating evidence, to communicating recommendations visually and persuasively and combining marketing knowledge with relevant technologies.
How the weeks build
Week 1 defines data and data-driven insights and separates primary from secondary data.
Week 2 surveys the secondary sources open to marketers, from government statistics and industry reports to customer reviews and search trends. Week 3 adds validation with the CRAAP test and four kinds of triangulation. Week 4 covers choosing and reading charts, and Week 5 turns interpretation into marketing insights, thematic analysis, and named problems or opportunities.
Week 6 introduces primary research methods, and the later weeks move to AI in marketing, visual communication, storytelling, persuasive communication and data ethics.
How it is taught and assessed
Recorded lectures are released weekly from Week 1 and three-hour workshops run every second week from Week 2, six in total.
The three tasks form one project: a secondary data report that names a problem or opportunity, an AI-assisted report that tests it and recommends a primary research approach, and an A1 poster presented with a short individual Q&A in the Week 12 workshop.
Worked example · free
Audit four summaries on a checkout survey dashboard
- 1Payment method is nominal: the codes are labels, so the mean of 1.6 is meaningless. Report counts instead: card 3 of 5, PayPal 1, gift voucher 1, so the mode is card.
- 1Delivery stars are ordinal. Sorted, the ratings are 2, 3, 4, 4, 5, so the median of 4 stars is a valid summary.
- 1Plants per order is discrete. The total is 1 + 2 + 2 + 3 + 5 = 13 over 5 orders, so the mean of 2.6 is valid when read as an average across orders.
- 1Dispatch minutes are continuous, so a pie chart, which shows parts of a whole, is the wrong chart; a histogram or box plot shows how the times spread.
Key terms
- Secondary Data
- Information that already exists because someone else collected it for a different purpose, reused to answer a new marketing question.
- Primary Data
- First-hand evidence a researcher gathers for the specific problem at hand, for example through interviews, surveys or experiments.
- Data-Driven Insight
- An actionable conclusion about customers, markets or brand performance reached by systematically analysing data rather than relying on intuition.
- Ordinal Data
- Categories that can be ranked, such as satisfaction ratings, where the distance between neighbouring categories is not measured.
- CRAAP Test
- A five-part screen for any source covering currency, relevance, authority, accuracy and purpose.
- Data Triangulation
- Checking one issue against several independent sources, times or places so that no single source drives the conclusion.
- Marketing Insight
- An explanation of why a finding matters for the brand, which points to a problem, an opportunity or a decision.
- Thematic Analysis
- A qualitative method that codes text such as reviews and groups the codes into recurring themes of meaning.
- Sentiment Analysis
- Classifying the emotional tone of reviews, posts or answers as positive, negative or neutral, by word lists or trained models.
- Gap Analysis
- Comparing a brand’s current performance with a desired or benchmark level to show what has to change.
- White Space Analysis
- Searching for customer needs or market positions that no existing competitor currently serves.
- A/B Testing
- An experiment that randomly shows people one of two versions of a marketing element and compares how each performs.
- Perceptual Map
- A two-axis chart placing brands where consumers see them, used to spot clusters of rivals and unclaimed positions.
24761 FAQ
How is the mark made up in this data-driven insights subject?
Three individual tasks make up the whole mark. A secondary data report on the assigned brand is worth 30% and is due in Week 6. An AI-assisted insights report is worth 30% and is due in Week 9. A single-page A1 poster with an individual Q&A is worth 40%: the PDF is due in Week 11 and you present it in your Week 12 workshop.
What do I need to pass the subject?
The Workshop 1 slides state that students must obtain at least 50% of the total assessment to receive a pass grade. None of the three tasks is labelled a hurdle in the subject materials. Missing the in-person poster session is serious, though: the brief says you must apply for Special Consideration, otherwise that task scores zero.
What does the secondary data report need to include?
Five parts: a brand overview, the secondary sources you used and why each is relevant, the insights you generated across those sources, one specific marketing problem or opportunity, and why it matters to management. The rubric gives 8 points each to source use and insight development, 5 each to overall quality and the problem or opportunity, and 4 to triangulation and validation.
How is generative AI used in the second report?
You use a tool such as Microsoft Copilot to analyse one additional secondary data source, then critically evaluate what it produced: accuracy, relevance, bias and gaps. The brief rewards an iterative prompting process over a single prompt, and the prompts, outputs and supporting evidence go in an appendix that sits outside the word count.
What happens in the poster presentation?
You submit one A1 portrait poster (594 by 841 mm) as a PDF; it is shown on screen in your workshop, so no print is needed. You then answer questions from your facilitator. The rubric splits 40 points into visualisation, design and storytelling 15, use of tools 10, managerial implications 5 and Q&A performance 10.
Am I allowed to use AI tools in the assignments?
Yes. The assessment overview allows generative AI for ideas, outlines, drafts, feedback and polishing, provided you can explain and defend the work. You must declare it in APA format, keep search history, drafts and notes for four months, upload a true transcript of your chat, and you may be asked to attend a viva about your submission.
What penalties apply for late work or going over the word limit?
Work more than an hour late loses 5% per day for up to seven days, a maximum of 35%, and anything later scores zero. The first report loses 1 point between 1,500 and 1,600 words and 4 points above 1,600. The second loses 1 point between 1,200 and 1,300 words and 4 above 1,300. The poster loses 4 points for a missing name and photo, and 4 more if it ignores the A1 format.
Questions students ask in each 24761 chapter
- Is a star rating ordinal or interval data?
- The Workshop 1 answer key treats a one-to-five star rating as ordinal. Five stars beats four, but nobody has shown that each step between stars is the same size, so the median and the full spread of ratings are the safest summaries. If you do report an average rating, say that you are treating the steps as equal. Chapter 01 · Marketing Data and Its Types
- What counts as secondary data in marketing research?
- Any evidence that already existed before your project, collected by someone else or by the business for another reason. Census tables, industry reports, competitor websites, customer reviews and a firm's own sales records all qualify once they are reused for a new question. Because it is cheaper and faster than fresh research, it is usually where an analyst starts. Chapter 02 · Secondary Data Sources and the Marketing Mix
- What does CRAAP stand for in source evaluation?
- Currency, relevance, authority, accuracy and purpose. Currency asks how up to date the information is, relevance whether it fits your question and audience, authority who stands behind it, accuracy whether it is supported and verifiable, and purpose whether it was written to inform, teach, sell or persuade. Together they screen any source before you rely on it. Chapter 03 · Validating Sources with the CRAAP Test
- What does triangulation mean in marketing research?
- It is the practice of examining one issue through several sources of evidence or several approaches and comparing what they say. Because each source or method has its own blind spots, combining them reduces the skew any single one introduces and makes the resulting insight more credible to a manager who has to act on it. Chapter 04 · Triangulating Marketing Evidence
- How do I choose the right chart for marketing data?
- Start with three questions from the Week 4 lecture: what type of data you have, how many variables you need to show, and what you want the reader to understand. Then match the purpose: bars or lines to compare, scatter or bubble charts for relationships, pies for parts of a whole, histograms or box plots for spread. Chapter 05 · Choosing the Right Data Visualisation
- What is the difference between correlation and causation in marketing?
- Correlation means two measures move together, such as card membership and basket size. Causation means changing one directly changes the other. Marketing data is full of correlations created by who chooses what, so a causal claim needs a test, ideally a random comparison between people who did and did not receive the change. Chapter 06 · Interpreting Data: Signals, Correlation and Causation
- What is the difference between a finding and a marketing insight?
- A finding reports what the evidence shows, such as how many reviews mention a feature. An insight explains why that matters for customers or the brand and points to a problem, an opportunity or a decision. The Week 5 lecture frames it as answering why and so what, not only what. Chapter 07 · Marketing Insights and Thematic Analysis
- What is the difference between a marketing problem and a marketing opportunity?
- A problem is an obstacle to sales, brand or customer goals and is usually revealed by negative signals; fixing it closes a performance gap. An opportunity is a favourable situation, often signalled by emerging or positive evidence, and acting on it creates growth or advantage. The lecture stresses that an opportunity is more than a pleasing statistic. Chapter 08 · Finding Marketing Problems and Opportunities
- Do I have to collect primary data for this subject?
- No. The Week 6 lecture page says students are not required to collect it. The second report instead asks you to recommend a primary data approach in no more than 200 words, explaining the method, the variables you would collect, why they matter and how they would validate the issue your secondary analysis found. Chapter 09 · Primary Data Methods for Validating Insights
- How do I check an AI analysis of forum posts or open-ended comments?
- Treat the tool’s first answer as a lead. Recount any number it gives against the raw rows, look up every quote it offers, and read a random sample of the text yourself to find themes it missed. Record each correction beside the request that produced it, because that record shows your professional judgement in the appendix. Chapter 10 · AI-Assisted Analysis and Critical Evaluation
- What size and format does the insight poster need to be?
- The brief asks for an A1 portrait design, 594 by 841 mm, submitted as a single-page PDF. You do not print it, because the file is displayed on screen during your discussion, so it must keep its layout and stay legible when shown. A poster that ignores the size or format loses four points. Chapter 11 · Data Storytelling and the Insight Poster
How to prepare for the assessments
Treat the semester as one evidence file that grows week by week. From Week 2, keep a source log for your assigned brand: for every report, review platform, search trend or article, record who published it, when, what it says and how it scores on the CRAAP test. That log becomes the backbone of the sources section in the first report.
From Week 3, look for two or three sources that point at the same issue and note where they disagree, because triangulation and validation carry their own rubric line. When you write findings, add a second sentence that explains why each one matters; that habit is what separates an insight from a description.
Before the second report, rehearse prompting on a small text set and keep every prompt and output you would need to show. For the poster, sketch the story first, problem, evidence, insight and recommendation, then choose one chart per claim. If a step does not click, ask Sia to explain it a different way or set a fresh practice case in another industry, and keep your own analysis your own.
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