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Perplexity was used to gather initial ideas with real-world references, and ChatGPT (2025) helped summarise content, which was then refined and supported with peer-reviewed and industry sources.
Claude.ai and GAMMA were used to draft some diagrams and visuals. Every figure was checked before use — a theme we return to throughout this lesson.
BrewLab, our Melbourne specialty coffee brand, has just run three months of social campaigns across Facebook, Instagram, and Twitter, in five states. The result is a spreadsheet of 100 posts with reach, engagement, revenue, cost, and ROI.
Same BrewLab dataset runs through every activity: prompts, dashboards, and the final story.
Marketing has shifted from posting on instinct to a data-driven discipline. The value of data is only unlocked when it changes what we decide.
A data-driven approach optimises engagement, targets audiences accurately, and drives business growth — but only if the numbers reach the people who make decisions.
The digital marketing world produces vast amounts of data. Without clear communication, most of that value is simply lost.
Effective data communication is what connects analysis to a decision. It sits in the middle — and it is the step most often skipped.
Imagine BrewLab's founder opens the campaign results. The difference between a wasted report and a good decision is communication.
"Average ROI across 100 posts is 115.25, engagement rate ranges from 0.05 to 0.24, and total cost was $292,170."
The founder nods, closes the file, and changes nothing.
"Instagram returned twice the ROI of Twitter for a similar spend. If we move next month's Twitter budget to Instagram, we expect more revenue for the same cost."
Now there is a decision on the table.
Same underlying numbers. Only the second version connects the analysis to an action — and that is what stakeholders pay for.
Communicating audience insights well lets BrewLab move from one generic message to the right message for each segment. That journey follows a clear pipeline.
Turn a broad audience into specific, actionable groups based on behaviour.
Identify patterns to optimise timing, format, and messaging.
Read the context so content connects emotionally, not just informs.
Clear communication does not remove risk, but it shrinks it — and it lets BrewLab react while a campaign is still running.
Live dashboards let the team adjust timing, creative, or budget mid-campaign instead of waiting for a post-mortem.
Decisions on content, timing, platform, and budget are grounded in what the data shows actually works.
Communicating uncertainty and confidence, not just a single number, leads to better strategic foresight.
Communicated well, marketing data stops being a side activity and starts moving the numbers the business cares about.
Insights are tied to indicators leaders track — ROI, revenue, growth — so social work visibly supports the strategy.
Clear benchmarking and trend reading reveal openings early, enabling proactive moves rather than reactive fixes.
Reporting shows which channels earn their spend, so budget flows to what works and away from what does not.
Sentiment, feedback loops, and community signals build a two-way relationship with customers, not just broadcasts.
In groups of two or three. The charts on the next slide show how consumers behave. Discuss for BrewLab, then share with the class.
Take a position and justify it. There is no single correct answer — the reasoning is what matters.
A good dashboard is not a data dump. It is an edited view — someone has already decided what matters for this reader.
For BrewLab, a dashboard answers a stakeholder's real question — "are we growing?", "which campaign should I fund?", "is the tracking working?" — without making them read the raw spreadsheet.
Six habits separate a dashboard people use from one they ignore.
Design around the reader's needs, not every metric you happen to have.
Show only the key strategic metrics. Cut clutter that competes for attention.
Use unambiguous, consistent labelling so nothing needs a second guess.
Add benchmarks, targets, and history so a number means something.
Keep it current through routine review, or trust in it decays.
Combine channels into one view instead of scattered exports.
A dashboard should read top to bottom like a sentence: the headline first, the trend next, the detail last.
The reader gets the answer immediately, and the supporting detail is there only if they want it.
In groups of two or three. Look at two real dashboards (a customer segmentation view and an Instagram performance view), then discuss.
Question 3 sets up the next section: charts can mislead even when the data is correct.
Below is BrewLab's weekly engagement rate — 11%, 12%, 12.5%, 14% — drawn twice. The data is identical. Only the y-axis changed.
The data can be correct and the chart still misleading. Watch for these — as a maker and as a reader.
A y-axis that does not start at zero makes a small change look dramatic.
Fix: start at zero, or label the break clearly.
Showing only the weeks that support your point hides the fuller trend.
Fix: show the full, relevant time period.
Two different scales side by side can invent a relationship that is not there.
Fix: avoid, or make both scales explicit.
A big number with nothing to compare against cannot be judged.
Fix: add a target, benchmark, or prior period.
Start from clarity and simplicity.
Lead with the key data points, not everything you found.
Cut detail that does not serve the message.
Make each insight easy to take in at a glance.
Frame the data as a story with a clear beginning, middle, and end — the same shape that makes any story easy to follow.
For BrewLab: context (we ran three campaigns) → finding (Instagram outperformed) → insight (twice the ROI for similar spend) → action (move budget next month).
Same BrewLab data — total reach by state. The left chart makes the reader work; the right chart makes the point.
The clean version uses one colour, highlights the winner, labels values directly, and puts the insight in the title.
Numbers persuade the head; a human story reaches the rest. Translate raw statistics into what they mean for a real person.
The BrewLab result is one dataset, but each audience needs a different altitude — from the summit view to the full detail.
Same numbers underneath — three very different views on top.
Executives want the high-level picture and business impact — concise visuals and a clear call to action.
| Metric | What it shows |
|---|---|
| Total social reach | Combined audience across channels |
| Engagement rate | Overall engagement vs previous periods |
| Top performing campaigns | Highest-ROI social campaigns |
| Revenue from social | Attributed sales from social channels |
| Regional performance | Engagement and sales by state |
Visual, concise, summary-first — built for a quick decision, not a deep dive.
Marketers need enough detail to refine campaigns — and the ability to drill down and filter.
| Metric | What it shows |
|---|---|
| Follower growth | Change in followers per platform |
| Engagement by post | Likes, shares, comments per post |
| Ad spend & ROI | Spend vs revenue for paid campaigns |
| Click-through rate | Share of users clicking on social content |
| Top content | Highest-performing posts and campaigns |
Interactive: filter by campaign or channel, drill into specifics, and spot what to optimise.
Technical teams need raw data and methodology — accuracy and completeness over polish.
| Metric | What it shows |
|---|---|
| Data sync status | Success or failure of recent data imports |
| API response time | Speed of data retrieval from platforms |
| Error logs | List of recent data or system errors |
| Custom metric tester | Sandbox for new metric calculations |
| System uptime | Percentage of time the analytics tools are up |
Detailed, often with logs and raw tables — built for troubleshooting, not storytelling.
| Stakeholder | Focus / needs | How to communicate |
|---|---|---|
| Executives | High-level overview, ROI, business impact | Summary metrics and trends; concise visuals and clear calls to action |
| Marketers | Campaign performance, audience, channel ROI | Detailed breakdowns, recommendations, comparisons across channels |
| Technical teams | Data quality, integration, methodology | Granular data, documentation, transparent methods |
Use the Week 8 dataset (100 BrewLab posts: Social_Channel, Campaign, Region, Reach, Engagements, Revenue, Cost, ROI) in Power BI or Tableau. Build the visuals and comment on the insights.
Ask your facilitator for a short Power BI demonstration. Then decide: what would change between the two dashboards, and why?
The right metric — and the right chart — depends on what the campaign is trying to achieve.
| If the goal is… | Foreground these metrics |
|---|---|
| Brand awareness | Reach and impressions |
| Leads | Form submissions and conversions |
| Sales | Revenue, ROI, and cost per acquisition |
| Retention | Repeat engagement and sentiment over time |
Pick the chart from the question you are answering, not the other way round.
A part-to-whole share is often clearer as a bar than a donut — simpler almost always wins.
When a campaign is live, communication turns into a feedback loop — watch, learn, adjust, repeat.
Track likes, shares, comments, and time spent to read audience preference as it happens.
Monitor sign-ups and sales to see funnel performance, not just attention.
Continuously adjust content and spend based on what the live data shows.
Make immediate changes while the campaign is still running, not after.
Predictive methods (churn, purchase intent) build on this — you will meet them in Weeks 10 and 11.
Take the insights from your Activity 3 dashboards and turn them into a short, compelling story for BrewLab's executive team.
Bring the same critical eye from Section 4 to anything the tool produces.
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