Demand Modeling & Market Estimation
Our market estimation methodology combines the robustness of top-down and bottom-up approaches, rigorously cross-validated through multi-level data triangulation, to arrive at precise and dependable market figures.
Top-Down Approach: This method initiates with a comprehensive analysis of the total addressable market (TAM) for overall logistics or specific industrial sectors (e.g., chemicals, oil & gas, pharmaceuticals) at global or regional levels. It then segments down to estimate the share attributable to dangerous goods transportation services, leveraging macro-economic indicators, industry-specific growth trajectories, and the impact of regulatory changes.
Bottom-Up Approach: This granular approach involves building market size estimates by aggregating data from individual market segments. It focuses on summing up the potential revenue or volume generated by distinct applications, transport modes, and regional markets, based on primary and secondary data.
Specific metrics and variables utilized for the bottom-up market sizing include:
- Total volume (e.g., tonnage, cubic meters, TEUs) of specific dangerous goods classes (e.g., flammable liquids, corrosive substances, gases) transported, segmented by air, sea, rail, and road.
- Average freight rates (per ton-mile/km or per shipment) for various dangerous goods categories, adjusted for hazard class, mode of transport, regional variations, and specialized handling requirements.
- Number of dangerous goods shipments/consignments by end-user industry and transport mode, multiplied by average service cost per shipment.
- Revenue generated by certified dangerous goods logistics providers, carriers, and specialized service operators, disaggregated by service type (e.g., packaging, documentation, warehousing, transport) and geographical region.
Multi-level Data Triangulation: This critical process involves cross-referencing and validating data points from diverse sources – primary interviews, secondary research, and our proprietary internal statistical models. This iterative validation ensures consistency, resolves discrepancies, and enhances the overall accuracy and reliability of the final market estimates.