Prepare
Validated dates, channel labels, numeric order values, fulfillment hours, and binary return flags.
Credentials and applied proof
Retail Operations Analysis
A transparent analysis of a small synthetic order dataset, connecting data preparation and descriptive statistics to fulfillment and return decisions.
- Evidence
- Independent applied exercise
- Supports
- Data Analytics Level III
- Data
- Synthetic and privacy-safe
- Focus
- CSV · Data preparation · Descriptive statistics
- Orders reviewed
- 24
- Gross order value
- ₱57,830
- Average order value
- ₱2,410
- Return rate
- 16.7%
Small synthetic sample across four sales channels.
Sum of order value before refunds, fees, or costs.
Gross order value divided by 24 orders.
Four returned orders in the sample.
01
Scenario
A bounded problem with transparent evidence.
A fictional online retailer needs a quick operational view of order value, fulfillment time, channel mix, and returns before deciding where to investigate process friction.
- Inspect and normalize a synthetic 24-order dataset
- Summarize revenue, order value, fulfillment time, and returns
- Compare channels and product categories
- Translate observations into bounded operational recommendations
02
Method
How the exercise was approached.
Summarize
Calculated totals and simple averages, then grouped records by channel and category.
Interpret
Separated what the sample directly shows from questions that would require a larger dataset.
Observed
Findings from the available evidence.
- Marketplace and social channels account for half of the sample orders, so channel-specific fulfillment checks deserve attention.
- Returned orders appear across multiple categories; the sample does not justify attributing returns to one product group.
- Long fulfillment times occur on more than one channel, suggesting the next analysis should include stock status and fulfillment-stage timestamps.
Recommended
Safe next actions.
- Track order-created, packed, handed-to-carrier, and delivered timestamps separately.
- Add return reasons before treating the return rate as a product-quality signal.
- Compare gross order value with discounts, fees, refunds, and cost data before making profitability claims.
Inspectable artifact
Download the synthetic CSV
The downloadable file contains fictional records created for portfolio demonstration. It contains no customer or employer data.
Evidence and privacy boundary
The public summary omits the learner identifier, certificate and training numbers, QR code, signatures, and exact training location.