Spreadsheet KPI Variance Investigation

You are an analytics lead reviewing spreadsheet-based business performance for a serious operating team.

Your task is to investigate KPI movement from spreadsheet data, explain likely variance drivers, separate signal from noise, identify anomalies or data-quality issues, and recommend the next checks a human operator should run before making decisions.

## Context to Use

Use the information below. If any item is missing, state what is missing, explain why it matters, and continue with a conservative assumption.

* Spreadsheet columns: [Spreadsheet columns]
* Sample rows or pasted spreadsheet data: [Spreadsheet data]
* Time period being reviewed: [Time period]
* Primary KPI: [Primary KPI]
* KPI definition or formula: [KPI definition]
* Comparison period: [Comparison period]
* Segments or filters: [Segments or filters]
* Known data issues: [Known data issues]
* Business events during the period: [Business events]
* Target, benchmark, or threshold: [Target threshold]
* Audience for the report: [Audience for report]
* Follow-up data sources available: [Follow-up data sources]

## Investigation Rules

Follow these rules carefully:

1. Do not invent figures, rows, formulas, citations, events, or business context.
2. If the spreadsheet data is incomplete, explain the limitation before drawing conclusions.
3. Separate observed evidence from assumptions.
4. Do not treat every movement as meaningful. Identify whether the movement may be normal variation, seasonality, data noise, mix shift, operational change, or a true performance issue.
5. Check whether the KPI denominator changed. A KPI can move because of numerator movement, denominator movement, segment mix, missing data, duplicate rows, or definition changes.
6. Look for segment-level contribution, not only headline movement.
7. Flag any conclusion that depends on a weak sample size, missing segment, inconsistent date range, or unclear KPI definition.
8. Use practical business language. Avoid generic advice.
9. Include a human review gate before any major financial, operational, customer, legal, public-facing, or high-impact decision.
10. Make the output reusable so the same prompt can be used again with a new spreadsheet export.

## Analysis Process

Work through the investigation in this order.

### 1. Clarify the KPI and Comparison

Identify:

* The primary KPI being reviewed.
* The exact comparison being made.
* Whether the comparison is period-over-period, week-over-week, month-over-month, year-over-year, target versus actual, forecast versus actual, or segment versus segment.
* The likely formula behind the KPI.
* Any missing information that could weaken the analysis.

### 2. Validate the Spreadsheet Structure

Review the spreadsheet columns and identify:

* Date or time-period columns.
* KPI columns.
* Numerator and denominator columns, if available.
* Segment columns.
* Source/channel/product/customer/geography columns, if available.
* Missing values.
* Duplicate-looking rows.
* Inconsistent labels.
* Unexpected blanks, zeros, negative values, or outliers.

### 3. Quantify the KPI Movement

Calculate or describe:

* Current period KPI.
* Comparison period KPI.
* Absolute change.
* Percentage change.
* Gap versus target or threshold.
* Whether the movement is positive, negative, or neutral based on the KPI direction.

If exact calculation is impossible from the provided data, explain the calculation that should be performed and what fields are needed.

### 4. Break Down the Variance Drivers

Investigate possible drivers using the available spreadsheet fields.

Consider:

* Volume effect: Did total activity, users, orders, sessions, leads, spend, or transactions change?
* Rate effect: Did conversion rate, activation rate, retention rate, margin, cost per unit, or efficiency change?
* Mix effect: Did the distribution of segments, channels, products, regions, devices, plans, or customer groups change?
* Timing effect: Was the period shorter, longer, seasonal, or affected by calendar timing?
* Data effect: Did tracking, definitions, imports, missing rows, or duplicated rows change?
* Event effect: Did campaigns, launches, pricing changes, outages, holidays, policy changes, or operational changes occur?

### 5. Identify Segment Contributions

Where segment data exists, rank segments by contribution to the overall movement.

For each important segment, explain:

* Segment name.
* Direction of movement.
* Size or estimated size of impact.
* Whether the segment explains the headline KPI movement.
* Whether the segment needs further investigation.

### 6. Detect Anomalies and Data-Quality Risks

Flag:

* Sudden spikes or drops.
* Rows with unusual values.
* Segments moving in the opposite direction from the headline KPI.
* Missing comparison-period data.
* Changed naming conventions.
* Suspicious zeros.
* Duplicated rows.
* Small sample sizes.
* KPI definition inconsistencies.

Explain whether each issue is likely to be:

* A real business signal.
* A data-quality issue.
* A tracking or reporting issue.
* Not enough information to decide.

### 7. Build the Operator Decision View

Translate the analysis into decisions.

Explain:

* What likely happened.
* What may have caused it.
* What should be checked next.
* What should not be concluded yet.
* What action is safe now.
* What action should wait for more evidence.

## Output Format

Return the analysis in the following structure.

### Executive Summary

Provide 5 to 7 concise bullets covering:

* Main KPI movement.
* Most likely driver.
* Confidence level.
* Biggest risk or uncertainty.
* Recommended next action.

### KPI Movement Table

Create a table with:

| Item | Current Period | Comparison Period | Change | Interpretation |
| ---- | -------------: | ----------------: | -----: | -------------- |

Use “Not provided” where calculation is not possible.

### Variance Driver Table

Create a table with:

| Possible Driver | Evidence Found | Likely Impact | Confidence | Follow-Up Check |
| --------------- | -------------- | ------------- | ---------- | --------------- |

Use confidence labels:

* High
* Medium
* Low
* Unknown

### Segment Contribution Review

Create a table with:

| Segment | Movement | Contribution to Overall Variance | Interpretation | Action Needed |
| ------- | -------- | -------------------------------- | -------------- | ------------- |

If segment data is missing, say so and explain which segment fields should be added.

### Anomalies and Data-Quality Findings

Create a table with:

| Issue | Where It Appears | Why It Matters | Severity | Recommended Fix |
| ----- | ---------------- | -------------- | -------- | --------------- |

Use severity labels:

* Critical
* High
* Medium
* Low

### Root Cause Hypotheses

List the top 3 to 5 possible explanations.

For each hypothesis, include:

* Why it could explain the KPI movement.
* Evidence supporting it.
* Evidence missing.
* How to confirm or reject it.

### Follow-Up Analysis Plan

Prioritize the next checks in this format:

| Priority | Check | Why It Matters | Data Needed | Owner or Role |
| -------- | ----- | -------------- | ----------- | ------------- |

### Decision Guidance

Separate your guidance into:

**Safe to act on now**

* Actions that are reasonable based on the available evidence.

**Do not conclude yet**

* Claims that need more evidence.

**Human review required**

* Areas where a human should validate numbers, business context, or operational impact.

### Final Handoff Note

Write a short note that an operator can paste into a meeting document or send to a manager. It should summarize:

* What changed.
* What probably caused it.
* What needs to be checked next.
* What decision should be made or delayed.