Skills

Digital Media and Audience Analysis

8 weeks · 0 milestones

Conduct a real audience or platform data analysis using actual platform analytics (with appropriate consent) or a real publicly available dataset (Ofcom, Nielsen, Pew Research, or equivalent). Document your methodology, analysis output, and conclusions about audience behaviour or platform dynamics. Proof is the analysis plus a documented review by a media practitioner or communications researcher who challenges your interpretation — specifically, what alternative explanations exist for the behaviour patterns you found and whether your sample or dataset is representative for the question you are answering. Your written response is a required part of the proof.

Part of: Social Sciences, Law & Humanities · Proves for: UG

Milestone map

3 milestones

Collect and analyse a real digital audience dataset

4 weeks

Collect a real digital media audience dataset — publicly available social media engagement data from a page or account you manage (or a public account using a free tool), publicly accessible web analytics data you have access to, or a published research dataset on digital media consumption. Produce an audience analysis: describe who the audience is (demographics where available, behaviour patterns from the data), what content they engage with (engagement rate by type, timing, topic), and what patterns emerge from the data.

Proof required

Audience analysis document (600+ words) with the dataset source and collection method documented, descriptive statistics on audience size and engagement, at least three audience behaviour patterns identified from the data, and charts or tables supporting the findings.

What gets checked

Dataset source and collection method are documented — not 'I got this data from social media'
Three distinct behaviour patterns are identified from the data with supporting statistics
Charts or tables are included that support (not just illustrate) the stated patterns

Common mistakes

Describing audience size without engaging with behaviour — follower counts are not an audience analysis; engagement patterns are
Not documenting the data collection method — without method documentation the analysis cannot be evaluated for bias
Identifying patterns without supporting data — patterns must be demonstrable from the dataset, not inferred from general knowledge

Powstik Guide

A 600-word analysis of a real audience dataset.

Steps

Document the dataset and how it was collected.
Report descriptive statistics.
Note sampling limits.
State one question for the next step.

Template

Dataset:        Collection method:
Descriptives:
Limits:
Next question:

What gets sent back

Source missing.
Under 600 words.
Limits ignored.

Resources

Foundationstart here

Depthgo deeper

What a verifier looks for

Confirm the dataset source is real and documented — not described vaguely
Review the identified patterns — confirm each has supporting statistics
Check charts/tables — confirm they support the stated patterns, not just decorate the document

You'll sign in first, then come straight back here.

Develop and justify a content strategy recommendation

3 weeks

Using the audience analysis from M1, develop a content strategy recommendation: what content types should be prioritised, what topics and formats resonate with this audience, what timing strategy maximises engagement, and what success metrics will be used to evaluate the strategy. The recommendation must be evidence-based — every claim must be tied to specific findings from the M1 dataset. Include a 30-day pilot plan with specific measurable objectives.

Proof required

Content strategy document (500+ words) covering content priorities, format and topic recommendations, timing strategy, success metrics, and a 30-day pilot plan with specific measurable objectives — each recommendation tied to specific M1 dataset findings.

What gets checked

Every recommendation is tied to a specific finding from the M1 dataset — not general social media best practice
Success metrics are specific and measurable — not 'increase engagement'
30-day pilot plan has specific week-by-week actions, not a high-level description

Common mistakes

Writing generic social media strategy advice that is not grounded in the specific M1 audience findings — a strategy that works for any audience is not a strategy for this audience
Setting unmeasurable success metrics — 'improve brand awareness' cannot be evaluated after 30 days
Not connecting recommendations to the audience data — recommendations without evidence are opinions, not strategy

Powstik Guide

A content strategy built on your audience data.

Steps

List the three strongest findings from your M1 data.
Tie each recommendation to one finding.
Set metrics you can check in 30 days.
Plan a 30-day pilot with weekly checkpoints.

Template

| M1 finding | Recommendation | Metric | 30-day target |
|------------|----------------|--------|---------------|
Pilot plan
Week 1:
Week 2:
Week 3:
Week 4:

What gets sent back

Generic social media advice.
Metrics like 'brand awareness'.
Recommendations with no data link.

Resources

Foundationstart here

Depthgo deeper

What a verifier looks for

Review recommendations — confirm each is tied to a specific M1 dataset finding, not generic best practice
Check success metrics — confirm they are specific and measurable
Review the 30-day plan — confirm it has week-by-week specifics

You'll sign in first, then come straight back here.

Present strategy and defend audience insights under media practitioner challenge

1 week

Present the audience analysis and content strategy to an experienced digital media practitioner, communications professional, or media studies academic for a Q&A challenge. The reviewer must probe specific data interpretations — 'your engagement pattern — is this a real preference or a platform algorithm artefact?', 'your timing recommendation — does it hold outside your data collection period?'. Document the challenges and your responses. This Q&A satisfies the ADVERSARIAL VERIFICATION RULE.

Proof required

Q&A notes (200+ words) documenting the reviewer's media background, at least two specific challenges to the data interpretation or strategy recommendations, your responses, and any analysis updates committed to.

What gets checked

Reviewer is an experienced digital media practitioner or media studies academic
At least two specific challenges to data interpretation or strategy are documented
At least one analysis update or caveat is documented based on the session

Common mistakes

Presenting to a social media user rather than a media practitioner — the reviewer must have enough expertise to challenge interpretations of digital audience data
Not addressing the algorithm artefact concern when raised — distinguishing real audience preference from algorithmic amplification is a core challenge in digital media analysis
Not updating any recommendations based on the challenge — a Q&A that leaves all recommendations unchanged was not genuinely adversarial

Powstik Guide

Your strategy, challenged by a media practitioner.

Steps

Note where a pattern might be an algorithm effect.
Book a digital media practitioner or academic.
Ask for challenges to your data reading.
Log each challenge and your answer.

Template

Challenge session log
Date:            Duration:
Reviewer name:
Reviewer role and credentials:

Challenge 1 (data interpretation):
  What they asked:
  My answer:
  Did it hold?
Challenge 2:
  What they asked:
  My answer:
  Did it hold?
Challenge 3:
  What they asked:
  My answer:
  Did it hold?

Update I am committing to:

What gets sent back

A casual social media user as reviewer.
The algorithm question left unanswered.
No update after the session.

Resources

Foundationstart here

Depthgo deeper

What a verifier looks for

Confirm the reviewer is a digital media practitioner or media studies academic with relevant experience
Review Q&A notes — confirm at least two specific data interpretation challenges are documented
Check that at least one update or caveat is documented based on the session

You'll sign in first, then come straight back here.

Part of

Communication & Media Studies →

Related outcomes

Milestone map

3 milestones

Collect and analyse a real digital audience dataset

4 weeks

Proof required

What gets checked

Dataset source and collection method are documented — not 'I got this data from social media'
Three distinct behaviour patterns are identified from the data with supporting statistics
Charts or tables are included that support (not just illustrate) the stated patterns

Common mistakes

Describing audience size without engaging with behaviour — follower counts are not an audience analysis; engagement patterns are
Not documenting the data collection method — without method documentation the analysis cannot be evaluated for bias
Identifying patterns without supporting data — patterns must be demonstrable from the dataset, not inferred from general knowledge

Powstik Guide

A 600-word analysis of a real audience dataset.

Steps

Document the dataset and how it was collected.
Report descriptive statistics.
Note sampling limits.
State one question for the next step.

Template

Dataset:        Collection method:
Descriptives:
Limits:
Next question:

What gets sent back

Source missing.
Under 600 words.
Limits ignored.

Resources

Foundationstart here

Depthgo deeper

What a verifier looks for

Confirm the dataset source is real and documented — not described vaguely
Review the identified patterns — confirm each has supporting statistics
Check charts/tables — confirm they support the stated patterns, not just decorate the document

You'll sign in first, then come straight back here.

Develop and justify a content strategy recommendation

3 weeks

Proof required

What gets checked

Every recommendation is tied to a specific finding from the M1 dataset — not general social media best practice
Success metrics are specific and measurable — not 'increase engagement'
30-day pilot plan has specific week-by-week actions, not a high-level description

Common mistakes

Writing generic social media strategy advice that is not grounded in the specific M1 audience findings — a strategy that works for any audience is not a strategy for this audience
Setting unmeasurable success metrics — 'improve brand awareness' cannot be evaluated after 30 days
Not connecting recommendations to the audience data — recommendations without evidence are opinions, not strategy

Powstik Guide

A content strategy built on your audience data.

Steps

List the three strongest findings from your M1 data.
Tie each recommendation to one finding.
Set metrics you can check in 30 days.
Plan a 30-day pilot with weekly checkpoints.

Template

| M1 finding | Recommendation | Metric | 30-day target |
|------------|----------------|--------|---------------|
Pilot plan
Week 1:
Week 2:
Week 3:
Week 4:

What gets sent back

Generic social media advice.
Metrics like 'brand awareness'.
Recommendations with no data link.

Resources

Foundationstart here

Depthgo deeper

What a verifier looks for

Review recommendations — confirm each is tied to a specific M1 dataset finding, not generic best practice
Check success metrics — confirm they are specific and measurable
Review the 30-day plan — confirm it has week-by-week specifics

You'll sign in first, then come straight back here.

Present strategy and defend audience insights under media practitioner challenge

1 week

Proof required

What gets checked

Reviewer is an experienced digital media practitioner or media studies academic
At least two specific challenges to data interpretation or strategy are documented
At least one analysis update or caveat is documented based on the session

Common mistakes

Presenting to a social media user rather than a media practitioner — the reviewer must have enough expertise to challenge interpretations of digital audience data
Not addressing the algorithm artefact concern when raised — distinguishing real audience preference from algorithmic amplification is a core challenge in digital media analysis
Not updating any recommendations based on the challenge — a Q&A that leaves all recommendations unchanged was not genuinely adversarial

Powstik Guide

Your strategy, challenged by a media practitioner.

Steps

Note where a pattern might be an algorithm effect.
Book a digital media practitioner or academic.
Ask for challenges to your data reading.
Log each challenge and your answer.

Template

Challenge session log
Date:            Duration:
Reviewer name:
Reviewer role and credentials:

Challenge 1 (data interpretation):
  What they asked:
  My answer:
  Did it hold?
Challenge 2:
  What they asked:
  My answer:
  Did it hold?
Challenge 3:
  What they asked:
  My answer:
  Did it hold?

Update I am committing to:

What gets sent back

A casual social media user as reviewer.
The algorithm question left unanswered.
No update after the session.

Resources

Foundationstart here

Depthgo deeper

What a verifier looks for

Confirm the reviewer is a digital media practitioner or media studies academic with relevant experience
Review Q&A notes — confirm at least two specific data interpretation challenges are documented
Check that at least one update or caveat is documented based on the session

You'll sign in first, then come straight back here.