Back to all cases
CASE STUDY / Data Analytics
Cancellation Rate Driver Analysis for an On-Demand Logistics Platform
O2O On-Demand Logistics PlatformsDecomposed cancellation drivers across the order lifecycle and both sides of the marketplace to identify high-risk markets, scenarios, and journey stages for operational improvement.
Delivery Details
This analysis addressed cancellation performance for a global on-demand logistics platform by first standardizing order states, cancelling party, reason codes, and timing definitions, then building a complete funnel from booking and matching through acceptance, arrival, and completion. SQL-based analysis segmented cancellation rates by market, time, location, order type, supply-demand conditions, customer cohort, and service-provider journey, separating demand-side, supply-side, and system or operational causes. Issues were prioritized by impact, frequency, and actionability to create a repeatable cancellation diagnostic framework and a set of operational recommendations.
Industry Context
On-demand logistics platforms must match customers and service providers within a narrow time window. A cancellation is not only a lost order: it also weakens marketplace liquidity, participant trust, and operational efficiency, while cross-market differences in density, supply structure, and behavior require consistent definitions and comparable diagnostics.
Business Problem
The headline cancellation rate showed the outcome but not who cancelled, when it happened, where it concentrated, or why.
What I Built
Standardized cancellation definitions and built an order funnel, using multidimensional segmentation, contribution analysis, and root-cause decomposition.
Business Impact
Enabled operations teams to move from one aggregate rate to actionable market, cohort, and journey problems with consistent ongoing monitoring.
For career conversations, advisory work, or a concrete Data & AI project, feel free to get in touch.