Initial Situation
- A global automotive OEM planned for a specific vehicle category to pivot their existing business model to address the hyper-fast last mile logistics arena which includes same day and instant (<3 hour) delivery
- The client was formulating its go to market strategy, complementary to product development phase
- To gauge the market attractiveness, the client needed to have complete insights with regards to current market size, along with future estimates
Project Objectives
- Market sizing of current parcel volumes commanded by the same day and instant delivery markets as well as a future (5 year) projection across three geographies: Asia, N. America, W. Europe
- Compilation of important market volume-affecting factors, and estimation of their degree of effect
- Identification and comparative relevance of key product categories (e.g. groceries, electronics, etc) for same day and instant delivery
challenges we faced
- Limited availability of reliable secondary research data for parcel volumes and growth rate for delivery models, across geographies, and product categories
- Secondary research data was heterogeneous in nature because of different definitions of key base factors such as parcel size and instant delivery time
Research
Comprehensive secondary research was first conducted to identify potential sources of market data including professional databases, press releases, references, company websites, association publications, nat. statistics & annual reports
Market model
Using proven Frenus’ methodologies and experience, a customized top-down market model was then built up, based on available and attainable data
Demand and supply analysis
Consumer preferences and current solutions from eCommerce & logistics players
Validate the market model data
Additionally, to address the aforementioned challenges, Frenus developed an approach which involved the use of in total 45 expert interviews with decision makers within leading regional CEP & eCommerce players as well as relevant national bodies in order to validate the real-world authenticity of market model data