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Analyze trends in spreadsheet data

Autor: AILesson7 min de preparaciónProbado con:ChatGPTRevisado el: 2026-08-28

Respuesta rápida

Separate sustained change, seasonality, and one-off noise using explicit time periods. Proporciona: Metric and decision, Time fields and sample data, Known events and limitations. Resultado esperado: A reproducible trend table, chart plan, and evidence-bounded interpretation.

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Design and interpret a spreadsheet trend analysis.

Metric definition and decision:
[metric]

Time fields, frequency, segments, and representative data:
[data]

Known events and limitations:
[context]

Validate time coverage and metric consistency before calculating change. Return period aggregation rules, formulas or PivotTable layout, absolute and percentage change, an appropriate moving average when justified, segment comparisons, and a chart recommendation. Distinguish sustained trend, seasonal pattern, level shift, and isolated spike. Treat links to known events as hypotheses unless the data establishes causality. Include checks for missing periods, changing denominators, partial current periods, and revisions.
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De la entrada al resultado

Un ejemplo completo

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Entrada real

Metric and decision
Weekly online conversion rate = completed orders / checkout starts. Use it to decide whether a recent decline needs investigation.
Time fields and sample data
One row per week from 5 Jan to 29 Jun 2026, with WeekStart, CheckoutStarts, CompletedOrders, Device. Overall rates for the last six complete weeks: 4.8%, 4.7%, 4.6%, 4.1%, 4.0%, 3.9%. Mobile checkout volume is roughly 70%.
Known events and limitations
The latest week is partial and must be excluded. Tracking definitions changed on 11 May. A mobile checkout release occurred on 1 June. No comparable prior-year data is available.

Ejemplo de salida

Exclude the partial week and split interpretation at the 11 May definition change; pre/post values are not automatically comparable. Calculate each rate from summed orders divided by summed starts, not by averaging row percentages. Add absolute percentage-point change and relative change.

The six complete weeks show a monotonic decline from 4.8% to 3.9%: −0.9 percentage points, or −18.75% relative. The larger step from 4.6% to 4.1% suggests a possible level shift rather than random week-to-week movement, followed by two smaller declines. This is evidence for investigation, not proof that the 1 June mobile release caused it.

Build a line chart with raw weekly rate by Device and overall rate; add a three-week moving average only as a secondary line. Mark 11 May and 1 June as annotations. Compare Mobile and non-Mobile using weighted rates and show weekly denominators. If the decline is concentrated on Mobile after 1 June and definitions are stable within that window, the release becomes a stronger hypothesis. Check missing weeks, denominator drops, delayed order completion, and later revisions before deciding.

Por qué funciona

  1. 1

    A fixed metric and period definition prevents apparent trends caused by changing denominators.

  2. 2

    Separating pattern types produces more cautious and actionable interpretations.

Comprueba el resultado

  • Are all compared periods complete and defined consistently?

  • Does the chart show raw values as well as any smoothing?

  • Are event explanations labeled as hypotheses rather than causes?

Úsalo con confianza

Preguntas frecuentes

Respuestas prácticas sobre cuándo usar esta receta, qué debes proporcionar y en qué casos la revisión humana sigue siendo importante

What should I prepare before using “Analyze trends in spreadsheet data”?

For “Analyze trends in spreadsheet data,” prepare Metric and decision, Time fields and sample data, and Known events and limitations. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Analyze trends in spreadsheet data” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A reproducible trend table, chart plan, and evidence-bounded interpretation—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Analyze trends in spreadsheet data”?

The published test record for “Analyze trends in spreadsheet data” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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