MODULE 05 — WORKED SOLUTIONS
Read metric_dictionary.txt for populations and units.

1. Start with the reader, decision and metric contract
Element | Worked choice | 
Reader / decision | Operations lead; investigate evidence before expansion | 
Default view | May 2026 / All channels / Completed orders | 
Cards | Completed value; completed orders; AOV; delivery measurement coverage | 
Diagnostic views | Monthly completed value; channel AOV with group counts | 
Missing measures | Profit needs cost/margin data; conversion needs a defined eligible visitor/customer population | 
The May baseline is €3,420 across 20 completed orders, AOV €171, and measured-delivery coverage 19/20 = 95%. April’s equivalent values are €2,900, 20, €145 and 14/20 = 70%. Both channel AOVs fall despite the aggregate increase. The brief therefore asks for channel context and sparse-group warnings, not an automatic expansion recommendation.
Comparison percentage is (selected May value − April value) / April value × 100, using the same channel selection. Preserve a separate unavailable state for April or the combined window. Label this fixture a static fictional teaching extract. No authenticated access, payment or live connection is required to inspect it.


2. Build a clear reading order before styling
A clear sequence is: decision and fixture identity → filters and scope → value/count/AOV/coverage → monthly value and channel AOV → explanation and next investigation → underlying orders and definitions. On a narrow screen, keep the four metrics in their existing order across two columns and stack the diagnostic panels. This preserves meaning while reducing width.
Place the sparse April store baseline warning beside the explanation of the aggregate change. It materially affects the comparison, so it should not appear only in hidden metadata. Use a visible heading and plain text; the lavender background supports grouping rather than carrying the warning by itself.
The starter is an honest scaffold. In your own implementation, replace its prompts with calculations linked to the verified source. Use dashboard_solution.html as a worked reference after attempting the layout. Its labels, scope and evidence sections illustrate the intended reading order; they do not require you to use the same software or reproduce its code.


3. Choose charts that preserve the comparison
View | Completed value | Orders | Actual AOV | 
April / All | €2,900 | 20 | €145 | 
May / All | €3,420 | 20 | €171 | 
May / Web | €180 | 2 | €90 | 
May / Store | €3,240 | 18 | €180 | 
For May/All, the channel-AOV values are Web €90 (n=2) and Store €180 (n=18). For April/All they are €100 (n=18) and €550 (n=2). If Web alone is selected, both charts and all cards must use Web; the prior-month completed-value comparison becomes €180 versus €1,800.
Replace “May average order value is €135” with “May actual AOV is €171, weighted by 2 web and 18 store orders; €135 is a hypothetical equal-channel average.” Keep the hypothetical calculation separate. The downloadable example uses exact text labels, a zero bar baseline and an explicit note that its displayed scale adjusts with the view.


4. Make filters, comparisons and empty states consistent
State | Value / count / AOV | Coverage | Prior-value change | 
May / All | €3,420 / 20 / €171 | 19/20 = 95% | +17.9% | 
April / All | €2,900 / 20 / €145 | 14/20 = 70% | Unavailable: March absent | 
Combined / All | €6,320 / 40 / €158 | 33/40 = 82.5% | Unavailable: prior window absent | 
May / Web | €180 / 2 / €90 | 1/2 = 50% | −90.0% | 
May / Store | €3,240 / 18 / €180 | 18/18 = 100% | +194.5% | 
Empty practice selection | €0 / 0 / unavailable | Unavailable | Unavailable: zero prior selection | 
The wrong May/Web comparison uses (€180 − €2,900) / €2,900 and mixes channel scopes. The correct comparison uses (€180 − €1,800) / €1,800 = −90%. The selected-period table, charts and summary must reflect the same Web filter. Reset restores May/All and its original 20 underlying completed rows.
Empty-state count zero is meaningful within that supplied selection. AOV and coverage have zero eligible denominators and remain unavailable. April’s missing March baseline is a separate absence-of-source case, not a zero baseline. Both require explicit reasons, so users can distinguish a reporting limitation from genuine zero activity.


5. Write explanations that match the selected evidence
Worked May/All explanation
In the supplied extract, May completed-order value is €3,420, up 17.9% from April’s €2,900, with 20 completed orders in each month. Overall AOV rises from €145 to €171, but both channel AOVs fall and store’s order share changes from 10% to 90%. April store has only two orders, including verified A020 at €1,000, making that comparison sensitive to a large order. Observed delivery means cover 14/20 April and 19/20 May completed orders, so they do not establish an all-order service improvement. Investigate the channel mix, collect comparable periods and obtain costs before recommending expansion. This extract does not establish profit or causation.
Worked May/Web explanation
May/Web contains two completed orders totalling €180, with AOV €90. Completed value is down 90% versus April/Web’s €1,800 from 18 orders. Only one of the two May delivery durations is measured. Check the change in channel volume and collection coverage before drawing a broader business conclusion.
Replace the unsupported claim with the actual completed-order value change and observed delivery coverage. “Profit” needs costs; “the initiative increased” requires causal evidence; “delivery improved” overstates a small observed difference with unequal coverage. Filter-dependent amounts, comparisons and coverage must change together.


6. Review calculations, accessibility and refresh claims
A strong test record includes May/All = €3,420, 20 orders, €171 AOV, 95% coverage; April/All = €2,900, 20, €145, 70%, with no March comparison; combined/All = €6,320, 40, €158, 82.5%, with no prior two-month comparison. May/Web and May/Store must match the selected-channel baselines. Empty selection shows zero count/value but unavailable AOV, coverage and relative change.
For a changed-input test, increase a verified completed order’s value in a disposable copy. Its period/channel value and AOV should update while order count stays unchanged. Changing a missing delivery measurement to a known zero should increase measured count, not missing count. Keep the original fixture intact. The worked example is generated from embedded data; changing a separate CSV beside it does not automatically refresh that HTML.
The handover states: “Static fictional fixture edition 2026-10-15.5. No live ingestion or automatic refresh. Period/channel controls recalculate the embedded data. Source changes require rebuilding or replacing that embedded dataset and repeating reconciliation and interaction checks.” Record limited keyboard/responsive checks as such rather than asserting complete standard conformance.


7. Deliver a dashboard and a concise stakeholder walkthrough
Worked walkthrough outline
Question and scope: review the supplied completed-order extract, default May/All, EUR; not a live feed.
Observation: €3,420 versus €2,900, +17.9%, 20 completed orders each month; AOV €171 versus €145.
Context: both channel AOVs fall; store share changes 10% → 90%; April store n=2 includes verified A020 at €1,000.
Interaction: select Web; May value is €180 from two orders, −90% versus April/Web. Reset restores the default.
Limits and next action: unequal delivery coverage, no costs or causal design; investigate mix, comparable periods and costs before expansion.
The complete worked dashboard is available as dashboard_solution.html. It runs locally using embedded fictional data, supports the stated filters and includes numeric chart labels, an underlying-order table, empty-state handling and definitions. The learner scaffold is deliberately incomplete; it is a layout starting point.
A complete evidence pack can receive two points in each category. A dashboard that uses €135 as May actual AOV fails metric correctness even if its layout is attractive. A Web filter that leaves an All-channel success headline unchanged fails interaction and communication. A source labelled “live” without a live connection fails the handover. Use the rubric to identify concrete revisions rather than treating a visual match to the worked example as proof of correctness.