Analytics Project README Template

# [Project name]

[One sentence: the decision this project supports and the result it produces.]

## Business question

- **Decision owner:** [Who uses this work?]
- **Question:** [What decision or recurring workflow does this analysis support?]
- **Output grain:** [What does one row in the final output represent?]
- **Reporting period:** [Coverage dates and timezone]

## Executive summary

[Write 3–5 sentences. State the checked result, the most important limitation, and the recommended next action. Do not describe a synthetic pattern as a real business benchmark.]

## Data

| Source | Grain | Coverage | Important limitations |
|---|---|---|---|
| [source.table] | [one row per…] | [date range] | [missing history, synthetic data, sampling, etc.] |

### Relationships

[Add a small lineage or relationship diagram. Label one-to-many relationships and the key used on each side.]

## Metric definitions

### [Metric name]

- **Business meaning:** [Plain-language definition]
- **Population:** [Included entities/events]
- **Formula:** [Numerator / denominator or aggregation]
- **Date rule:** [Which timestamp assigns the reporting period?]
- **Exclusions:** [Statuses, test records, cancellations, etc.]
- **Units:** [USD, customers, percentage points, days, etc.]

## Model and approach

1. **Staging:** [Renames, types, source cleanup]
2. **Intermediate:** [Reusable joins or event logic]
3. **Facts and dimensions:** [Declared grain and keys]
4. **Serving output:** [Final mart, report, notebook, or dashboard]

### Defended decision

[Explain one trade-off. What alternatives did you consider, what evidence changed the decision, and what consequence remains?]

## Validation

- [ ] Final key is unique at the declared grain.
- [ ] Required keys and fields are not null.
- [ ] Relationships have no unexpected orphan records.
- [ ] Important totals reconcile to a simpler source query.
- [ ] Row counts were checked before and after one-to-many joins.
- [ ] A small example was calculated independently.
- [ ] Ties, zero-activity entities, late records, and cancellations were reviewed where relevant.

## Result

[Show the smallest useful table or chart. Include units, filters, coverage dates, and the dataset/model version.]

## Limitations

- [What can this result not establish?]
- [Which source limitation could change the interpretation?]
- [What additional evidence would you request?]

## Repository map

```text
.
├── README.md
├── models/          # or sql/
├── analyses/        # checks and one-off validation
├── tests/
├── docs/
└── outputs/         # reproducible tables/charts, not private raw data
```

## Reproduce the work

### Requirements

- [Tool and tested version]
- [Required packages]
- [Safe sample data or documented source access]

### Run

```bash
[exact setup command]
[exact build/test command]
[exact output command]
```

## Changes and ownership

- **Owner:** [Name or team]
- **Last checked:** [YYYY-MM-DD]
- **Version:** [dataset/model/release version]
- **Change process:** [How definitions and breaking changes are reviewed]

# Ecommerce revenue investigation

Reproduce the August-to-September 2025 change in net payment value, identify measurable contributors, and state what evidence would be needed before assigning a cause.

## Business question

- **Decision owner:** Fictional finance lead
- **Question:** Why was September 2025 net payment value lower than August?
- **Output grain:** One row per order month in the comparison; separate supporting cuts at order or item grain
- **Reporting period:** August and September 2025, UTC

## Executive summary

The checked payment-based measure declined from $1,734,506.51 in August to $1,624,955.07 in September, a decrease of $109,551.44 (6.32%). The analysis reconciles successful payments and refunds at order grain before comparing customer, product, and channel cuts. These synthetic associations do not establish a cause. The next step is to review the largest measured contributors against operational or campaign evidence not present in this release.

## Data

| Source | Grain | Coverage | Important limitations |
|---|---|---|---|
| Ecommerce Sales & Returns v1.0.0 | Orders, payments, refunds, items, products, and customers at documented grains | 2023-01 through 2025-12 | Synthetic; no campaign exposure, inventory availability, or site-behavior data |

## Metric definition

### Net payment value

- **Population:** Successful payment amounts and all linked refunds
- **Formula:** successful payments minus refunds
- **Date rule:** Month of the original order
- **Units:** USD
- **Limitation:** This is not automatically merchandise sales or recognized revenue

## Model and approach

1. Aggregate successful payment attempts to one row per order.
2. Aggregate refund events to one row per order.
3. Join both aggregates to orders and assign the order month.
4. Reconcile monthly totals to the published checked output.
5. Analyze candidate contributors at compatible grains.

## Validation

- [x] Payment and refund inputs are reduced to order grain before joining.
- [x] August and September totals match the versioned checked output.
- [x] Orders without refunds remain in the result.
- [x] Item-level cuts are calculated separately from order-level measures.

## Result

| Order month | Net payment value |
|---|---:|
| 2025-08 | $1,734,506.51 |
| 2025-09 | $1,624,955.07 |

## Limitations

- The data can measure associations, not prove why customer behavior changed.
- Refunds are assigned to the original order month under the supplied contract.
- A different date or revenue definition will produce a different answer.

## Reproduce the work

1. Download the SQLite distribution for Ecommerce Sales & Returns v1.0.0.
2. Run the checked payment-and-refund query from the dataset page.
3. Save the comparison and validation outputs with the dataset version.

Related guidance.

• Portfolio project guide→
• Analytics engineering portfolio projects→