A guided analysis of completed-order value, channel composition and measurement coverage in a fictional April–May extract.
Question: what should the operations lead investigate before recommending expansion?
This is a complete worked learning example, not a learner submission or real client engagement. Recreate the method and identify your own extension before presenting portfolio work.
Compare completed-order value in the supplied April–May 2026 extract and identify what the aggregate hides. The decision is what evidence to gather before expansion, rather than a claim that the initiative caused profitable growth.
Unchanged Module 4 fictional data: 46 raw records, 44 verified unique orders, 40 completed orders. One row represents an order. Values are EUR major units. Returned and cancelled orders are outside the headline value/count/AOV.
Observed order dates end on the 22nd in each month. The supplied fixture is the exercise population; it does not prove complete real-world trading coverage.
No customer exposure, advertising spend, margins, experiment assignment or longer seasonal history. The analysis cannot establish customer conversion, profit, ROI or a causal effect.
Delivery blanks are unknown measurements, not zero durations. Report observed means with measured/eligible counts.
A004 is an exact repetition. Y007 has conflicting channels; keep the verified store record rather than choosing file order. Correct A005 formatting and the five reference-backed field issues at A008, A009, Y003, Y005 and Y006. Preserve all genuine delivery blanks.
P1/P2 inspect raw source. P3/P4 produce and reconcile monthly/channel metrics. P5 names status denominators; P6 compares previous observed periods. P7 retains zero-count distribution bins; P8 separates baseline and the named A020-exclusion scenario.
Run analysis_setup.sql in a new empty local SQLite database. It loads raw text and the separate verified reference; it is not an automatic cleaning pipeline. Run your own SELECT queries and compare columns/values with expected_outputs.json. P6 needs SQLite window-function support. Use a local practice database, not the hosting application database.
EUR · Zero baseline · Exact values shown. €520 increase; +17.93% relative to April.
Overall AOV rises €145 → €171. Web falls €100 → €90; store falls €550 → €180. Store’s order share changes 10% → 90%.
April store has only two orders, including verified A020 at €1,000. Retain that order in baseline results and disclose its influence.
| Month / channel | Orders | Value EUR | AOV EUR | Order share |
|---|---|---|---|---|
| April / web | 18 | 1,800 | 100 | 90% |
| April / store | 2 | 1,100 | 550 | 10% |
| May / web | 2 | 180 | 90 | 10% |
| May / store | 18 | 3,240 | 180 | 90% |
Combined baseline: 40 completed orders, €6,320, mean €158, median €110. Excluding verified A020 as a named scenario: 39 orders, €5,320, mean €136.41. The baseline is unchanged.
A hypothetical equal-channel mix gives AOV €325 → €135. These are scenario values, not observed AOV.
April observed mean: 51/14 = 3.64 days, measured for 14/20 completed orders. May: 67/19 = 3.53 days, measured for 19/20.
Different coverage and unknown durations prevent an all-order service-improvement claim. Source is static; no live feed or automatic refresh is implied.
In the supplied April–May 2026 extract, completed-order value rises from €2,900 to €3,420, or 17.9%, with 20 completed orders in each month. Overall AOV increases from €145 to €171, but web AOV falls from €100 to €90 and store AOV from €550 to €180. Store’s order share changes from 10% to 90%. April store has only two orders, including verified A020 at €1,000, so the comparison is sensitive to composition and a large order. Delivery means cover 14/20 April and 19/20 May completed orders; they do not establish an all-order service improvement. Retain the verified high order in baseline results and label any exclusion separately. Investigate the mix shift, collect comparable periods and obtain costs before recommending expansion. This fictional guided extract establishes neither profitability nor a causal effect of the initiative.
Traceability: monthly value/count/AOV and coverage P3; channels/weights P4; relative value change P6; baseline/exclusion P8.
| Measured / eligible | April | May |
|---|---|---|
| Web | 12/18 = 66.7% | 1/2 = 50% |
| Store | 2/2 = 100% | 18/18 = 100% |
| All channels | 14/20 = 70% | 19/20 = 95% |
Missing measurements are concentrated in web orders. Overall coverage is affected by the channel mix. Investigate the collection process before comparing all-order service outcomes; these counts do not establish the cause of missingness or channel delivery speed.
Extract the complete practice ZIP with its relative filenames to keep these local links together. The worked case study is static. The separate dashboard reference embeds its own unchanged Module 5 fixture.
This is a supplied worked example. Data, reference queries, dashboard and this narrative are provided for learning. A learner should identify which steps they implemented, what they reused and which extension they independently designed. No real-client work, achieved savings, job outcome or qualification is claimed.