Currently, I manage a charity shop where every transaction directly supports vital humanitarian causes. While retail management is often seen as a purely operational role, I recognised an immense opportunity to leverage the data we generate daily to maximise our social impact.
In the fast-paced world of the third sector, decisions are frequently made based on intuition. However, intuition alone cannot answer critical questions:
Which donation categories (apparel, media, or home goods) yield the highest return per square foot?
How do seasonal trends affect the volume and quality of donations?
What is the optimal pricing strategy to balance fast stock turnover with maximum revenue for the charity?
As part of my Data Analyst Apprenticeship with Corndel and Imperial College London, I began applying structured methodologies to our shop’s data:
Data Harvesting: I streamlined the way we track sales through our POS system and categorised incoming donations to identify high-value streams.
Trend Analysis & Visualisation: Using Excel and Tableau, I developed dashboards to visualise performance. This allowed us to identify "dead zones" in the shop floor and peak times for donor footfall.
Actionable Insights: My analysis revealed that donor behaviour shifted significantly on weekends, allowing us to optimise volunteer scheduling to handle the influx of high-quality items more efficiently.
By shifting from a reactive to a data-informed strategy, we achieved:
Reduced Waste: More accurate pricing led to fewer items remaining unsold.
Operational Efficiency: Optimised volunteer hours based on predicted donor activity.
Increased Revenue: Ultimately, more funds were directed toward the charity’s core mission thanks to improved stock management.
This experience has reinforced my belief that Data Analytics is a universal language. It is not just for tech giants; it is a transformative tool for the non-profit sector, ensuring that every donation goes as far as possible in helping those in need.