The setting

Aster and Oak sells sustainable camping and hiking gear direct to consumers online. You are the digital marketer, the only full-time marketing hire, reporting to the founder Nora Vance.

Every mission on this path happens at the same company, so context carries over the way it does in a real job: the data you cleaned in mission two is the data the finance lead questions in mission four.

The missions

1. audience briefstarter

Build the audience brief for Aster and Oak's spring tent launch

Aster and Oak is about to launch the Summit 4 family tent, its biggest product of the year. Nora is convinced the core buyer is a young thru-hiker who counts every gram, and she wants the launch copy aimed there. You are not sure the data agrees, and getting this wrong wastes the whole launch budget.

You deliver: An audience brief naming the primary and secondary segments for the Summit 4 launch, backed by the survey data.

Scored on: Segments grounded in the survey, Spend analysis, Segment motivations, Clear recommendation.

Working from: customer_survey.csv, product_notes.md.

2. content planstarter

Plan April's content calendar around what families actually search

Organic traffic is flat and the blog has not published in six weeks. With the Summit 4 launch coming, April is the month to publish content that pulls in the family campers who spend the most. You have a keyword research pull and a list of what already exists.

You deliver: An April content calendar of eight posts with titles, target keywords, and angles, plus a short note on which keywords were rejected and why.

Scored on: Keyword selection, Rejects the traps, Eight dated posts, Angles serve the audience.

Working from: keyword_ideas.csv, content_gaps.md.

3. SEO checkcore

Audit Aster and Oak's top landing pages before the launch push

Before paid traffic starts hitting the site, the important pages need to be technically clean, or the launch spend leaks into slow, duplicate, or unindexable pages. Nora asked for a plain-language audit she can hand to the developer.

You deliver: An SEO audit that lists each problem page, the specific defect, the standard broken, and a prioritized fix list for the developer.

Scored on: Finds the duplicate title, Meta and heading defects, Speed and canonical, Prioritized to-do.

Working from: page_audit.csv, seo_checklist.md.

4. campaign testcore

Decide which paid channel to cut before summer budget locks

Summer budget locks Friday and one channel is quietly burning money. Nora has been judging channels on simple return on ad spend, but that ignores agency fees, fulfillment, returns, and the repeat purchases that some channels seed. You need to find the real loser and defend anything that only looks bad on the surface.

You deliver: A channel test recommendation with true cost-per-order math, the channel to cut, the channel to protect, and one proposed budget shift or test.

Scored on: True cost per order, Names the real loser, Protects the funnel channel, Concrete next step.

Working from: channel_performance.csv, cac_notes.md.

5. analytics readoutstretch

Write the monthly analytics readout for Nora at Aster and Oak

The founder wants one honest page every month: what happened, why, and what to do next. This month is tricky because traffic kept climbing from the launch push while something changed in checkout, and the raw revenue line hides it. Nora needs the real story, not a victory lap.

You deliver: A one-page monthly analytics readout covering the conversion story, its cost, the traffic mix shift, and two or three data-backed recommendations.

Scored on: Conversion story, Quantifies the dip, Traffic mix insight, Data-backed recommendations.

Working from: weekly_metrics.csv, context_notes.md.

How the scoring works

Each deliverable is graded against the rubric written for that mission. Separately, every mission on every path is graded on how you used AI, against the same four criteria:

  • Understood the task. The learner framed the goal for the assistant clearly instead of pasting the brief and hoping.
  • Grounded in the material. The learner directed the assistant into the provided files and based the work on them, not on invented facts.
  • Verified the output. The learner checked claims, numbers, or coverage against the source material before submitting.
  • Iterated with judgment. The learner refined weak parts of the draft with specific follow-ups rather than accepting the first answer.

Both scores, with the work behind them, go on your proof profile. That is what makes a claim like "I can use AI for digital marketing" something an employer can check.