The setting

Cedar and Salt is an online kitchen and home goods store with about 400 SKUs and one busy storefront. You are the e-commerce manager, owning the catalog, merchandising, pricing, and promotions.

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. catalog auditstarter

Audit the Cedar and Salt catalog before the spring refresh

The spring catalog refresh starts next week and leadership will not sign off until the product data is clean. The export has grown messy: prices that do not make sense, a product with no category, and at least one SKU that looks duplicated. Bad data here becomes wrong prices on the live store.

You deliver: An audit report listing every catalog data issue by SKU with a severity and a fix.

Scored on: Catches pricing errors, Catches the duplicate, Data completeness, Fix and severity.

Working from: catalog_master.csv.

2. merchandisingstarter

Rebuild the Cookware category page for Cedar and Salt

The Cookware category page lists products alphabetically, which buries the items that actually sell and pushes a couple of duds to the top. You have a month of performance data and a mandate to reorder the page so it earns more per visitor.

You deliver: A merchandising plan reordering the Cookware page with a stated logic, features, demotions, and stock flags.

Scored on: Promotes hidden gems, Addresses low conversion, Features the bestseller, Ordering logic.

Working from: cookware_performance.csv.

3. pricing analysiscore

Find the pricing leaks in Cedar and Salt's knife category

Margins in the knife category slipped last quarter and nobody is sure why. You suspect a mix of problems: an item priced below its minimum advertised price, one priced so high it barely sells, and one whose margin has quietly gone to almost nothing.

You deliver: A pricing analysis with a per-SKU recommendation, margin math, and the specific pricing violations flagged.

Scored on: Catches the MAP violation, Catches the thin margin, Competitive read, Correct math.

Working from: knife_pricing.csv, competitor_prices.csv.

4. promo plancore

Design the Mother's Day promo that protects margin

Merchandising wants a Mother's Day sale, but last year's blanket 20 percent off everything drew a crowd and sold the low-margin categories at almost no profit. Finance has drawn a line: this year the event has to clear at least 30 percent blended gross margin.

You deliver: A margin-safe Mother's Day promo plan with targeted discounts, guardrails, and a numbers-based forecast.

Scored on: Margin-aware targeting, Avoids last year's loss, Guardrails, Forecast.

Working from: promo_history.csv, margin_by_category.md.

5. conversion reportstretch

Explain why Cedar and Salt's checkout conversion dropped in Q1

Sales fell through Q1 even though traffic held up, and leadership wants the why and the fixes, not a shrug. Something changed in the funnel partway through the quarter, and the upstream numbers look fine.

You deliver: A conversion report locating the funnel drop, its root causes, the revenue lost, and three ranked fixes.

Scored on: Locates the drop, Ties to the changes, Quantifies the loss, Three ranked fixes.

Working from: funnel_weekly.csv, checkout_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 e-commerce operations" something an employer can check.