Milestone map
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
Resources
Foundationstart here
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
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
Resources
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
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
Resources
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