Peer-to-Peer Crowdshipping as an Omnichannel Retail Strategy

Crowdshipping is an emerging operations model proposed to tackle the last-mile delivery problem in urban markets that major retailers such as Amazon and Walmart, as well as start-ups such as Instacart and DoorDash, have adopted or experimented with. This paper investigates a novel peer-to-peer (P2P) mode of crowdshipping, that is, using in-store customers to help deliver online orders, and its strategic implications on the marketing-operations interface: on the one hand, it offers the opportunity to improve last-mile delivery efficiency (the efficiency effect); on the other hand, it provides an additional tool for the retailer to price discriminate between the online and in-store channels (the pricing effect). We model an omnichannel retail network’s pricing decision for its online and in-store channels and consumers’ decisions on purchase, channel choice, and participation in P2P crowdshipping. As the exact analysis of the game is confounded by heterogeneity of spatial characteristics within a store’s catchment area, we adopt a continuous approximation approach to yield a tractable model. Contrasting two remuneration modes for P2P crowdshipping, that is, remunerating shopper-deliverers their incurred delivery costs or providing an additional premium as a (cross-channel) subsidy, against a store-operated (business-to-consumer (B2C)) delivery model, our analysis reveals that the effect of P2P crowdshipping depends upon the interactions between the aforementioned efficiency and pricing effects. A win-win outcome can arise if the retailer employs the cost-based remuneration model when there is little correlation between consumers’ travel costs and product valuation, or the cross-subsidy model when such correlation is strong. We further show that P2P crowdshipping has the potential to help reduce the carbon footprint of sales by inducing a larger proportion of online sales and fulfilling them with efficient crowdshipping. Finally, we find that P2P crowdshipping, if mass adopted in the long run, could enable the retailer to serve the market with a sparser store network.