Transforming data into strategic business solutions.
Unlock Data Insights for Growth
Transforming data into strategic business solutions.
Transforming data into strategic business solutions.
Transforming data into strategic business solutions.
At Mind Mongers l specialize in data-driven decisions by using Power BI dashboards and Python-based forecasting. The focus areas include Procurement analytics, Total Cost of Ownership analysis, Retail insights, and Education (including LMS data). l help clients transform raw data into strategic insights that drive measurable value.
We empower organizations to thrive by delivering data driven, business focused insights that drive real results ; built on a foundation of integrity, collaboration, and clarity.
With over 15 years’ consultancy experience across procurement, education, infrastructure asset management, and broadcasting/telecoms, I’ve since integrated this multidisciplinary background with data science toolkits to establish Mind Mongers. Driven by a passion for data and a commitment to uncovering meaningful insights. l help clients turn complex information into actionable intelligence, reveal the true drivers of performance, and identify the next opportunity for growth.
Mind Mongers is currently a founder-led and independently operated consultancy. With no layers of bureaucracy, clients benefit from direct access, rapid turnarounds, and fully tailored, customer focused solutions combining deep industry expertise with modern data science tools to deliver meaningful outcomes and focused intelligence.
An online retail client lacked insight into customer behavior. K-means clustering was applied to segment users by spend, frequency, and recency. The analysis revealed clear customer types, enabling targeted strategies. Segmentation insights, CLV & RFV analysis visualized in Power BI led to a 12% rise in campaign engagement and more efficient resource allocation.
An ABC analysis was conducted to classify stock value and turnover by manufactured model. The insights highlighted excess stock in low-priority items and coverage inefficiencies. This helped refocus procurement on high-impact parts, reduce days inventory outstanding, and improve overall inventory turnover.
A TCO analysis was conducted for a manufacturer operating injection blow molding machines, evaluating multiple bearing vendors. Using Power BI and Python, the model combined repair costs, machine downtime, Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and potential revenue impact. A weighted scoring model was applied to support data driven vendor recommendations resulting in optimized vendor selection, improved machine uptime, and more accurate cost forecasting.
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