Fourteen years across multi-site retail in New Zealand and Asia: category P&L, demand forecasting, and leading teams of up to 120. At Samsung Electronics I grew a television category from $8.9M to a $16.8M peak against a shrinking core segment. Then I rebuilt here, with a First Class Honours Master's in Business Analytics and a Duty Manager's view of a supermarket floor. I build the tools as well as the analysis: the work below runs.
Fifteen inner-city catchments have 51.9% of households without a motor vehicle, and eight of those have no convenience-format store within a walkable kilometre while a competitor sits closer. The more useful finding came from the co-operative's published accounts: at a 3.17% margin the recapture opportunity is worth about $0.46m, while allocating $254m of annual capital 5% better is worth around $12.7m. The catchment gaps are not the prize; they are the evidence that the method finds things.
Grew a television category from $8.9M to a $16.8M peak against a contracting core segment.
Category P&L, demand forecasting, and direct leadership of teams of up to 120.
Alongside an MBA (International Business) and an Executive Master of Project Management.
Licensed Duty Manager: the operator's view of the trade the data describes.
Bringing the operator's view, the commercial record and working tools to an analytics team.
Every figure traces to a named script and a named source file. The catchment pipeline (scripts, intermediate data, outputs, logs and the written study) re-runs end to end and is available on request. Shelly is public on GitHub and runs from a clean clone.
Data boundary. I work in a Foodstuffs banner store. No employer data of any kind appears in this work, in raw, aggregated or anonymised form. The catchment study uses public sources only; Shelly ships with synthetic demo data.
Customer analytics, category and commercial analysis, forecasting, or building the tool your team keeps asking for.