python Case study
WooCommerce Order Analytics
WooCommerce reporting answers questions about revenue. It does not answer the questions this shop asked every month, which were about product mix, gateway split and where orders were stalling. Those needed the raw export and a repeatable analysis over it.
The business problem
The reporting that mattered was being rebuilt by hand in a spreadsheet every month, which made it slow, inconsistent between months, and impossible to check. The built-in WooCommerce analytics could not answer questions that cut across order status, payment gateway and product category at the same time.
What I delivered
- A Python workflow that ingests WooCommerce order exports and normalizes them into a consistent shape before any analysis runs.
- Repeatable reporting over sales patterns, product mix and operational gaps, so the same questions produce comparable answers month to month.
- Handling for the mess that is specific to this data: Persian numerals, Jalali dates, and gateway names that drift across the export history.
- Output in a format the people who asked for it can actually open, rather than requiring them to run anything.
Technical approach
- I keep normalization separate from analysis. Most of the difficulty is in the input format, and mixing the two makes both harder to change.
- The workflow is deterministic over a given export, so a number can be re-derived and checked rather than taken on trust.
- I kept the scope to the questions actually being asked each month, rather than building a general analytics platform that would need its own maintenance.
Result and evidence
Monthly reporting became a repeatable run over an export instead of a manual rebuild. For the first time, the figures were comparable across months.
Commercial value
The value is consistency more than speed. A number derived the same way every month is one you can make decisions from.
Readable implementation brief
implementation_brief {
project: "WooCommerce Order Analytics"
stack: "Python over WooCommerce order exports"
shape: "normalize first, analyse second, as separate steps"
handles: "Persian numerals, Jalali dates, inconsistent
gateway naming across export history"
properties: "deterministic over a given export, re-derivable"
scope: "the questions asked monthly, not a platform"
output: "a file the requesters can open unaided"
}What this project shows
The judgement here was scoping down. A general analytics platform was the tempting version and would have been the wrong deliverable for a shop that needed four questions answered reliably.
Separating normalization from analysis is what made it survivable when the export format changed.