LinkedIn / case study

From daily exports to a reviewed HQ report.

An anonymised retail reporting workflow that turns sales and ticket exports into a consistent, quality-controlled output.

Illustrative context / original source data and company details are not shown.

Outcome59 stores · 9 products · 45 → 3 min

Evidence levelMeasuredBefore/after comparison documented; review remains human.

StackPython · pandas · Excel · Outlook · QA

01 / Context

The question behind the work.

The process started with two daily exports, inconsistent store names and a recurring need to prepare a clear report for commercial and HQ stakeholders.

02 / Built

Structure before decoration.

Automated discovery, cleaning, product filtering, store normalization, ticket reconciliation, branded Excel output and Outlook email preparation. The process stops when mappings are incomplete instead of silently passing bad data forward.

03 / Result

What changed.

The measured processing comparison fell from approximately 45 minutes to 3 minutes before human review. The workflow covers 59 mapped points of sale and 9 products; the email remains deliberately reviewable and unsent until a person approves it.

This case study is intentionally anonymised. It describes the shape, scale and outcome of the work without exposing internal data, client systems or confidential screenshots.

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