SPEEDY: Framework for sharpening promise time estimates in sub-same day delivery

In today's fast-paced world, customers increasingly value quick and reliable delivery services, with many prioritizing speed as a decisive factor in their purchasing decisions. E-commerce stores serve customers through specialized programs ensuring delivery within same day. Facilitated by strategically placed delivery networks, this provides an ultra-fast delivery experience to the end customers enabling them to receive their orders within the same day in a chosen fixed size window. While intra-day deliveries conclusively improve the customer experience, in the age of quick commerce, customers want the orders faster, and many scenarios (for e.g. periods of the day when customers would be traveling to school or work) make long static windows inconvenient for customers. However, faster deliveries increase the cost of shipping. In this work, we leverage the observation that in many instances orders reach ahead of time because of the geographical proximity of the shipping address and the order density in the neighborhood. This presents an opportunity to improve the delivery experience of customers without incurring any additional costs for customer or the seller. We present a framework to recommend dynamic, faster delivery time slots to customers. We create multiple heterogeneous views of order-to-delivery data capturing the spatial and spatio-temporal aspects of the data, and leverage a novel deep view interaction network which computes the higher order interactions among the views. The proposed model outperforms multiple representative baselines and allows us to predict narrower slots for 60%+ eligible orders for the locale under experimentation. During a 21 day online A/B test, the treatment recorded a significant gain of +17 bps in units, +21 bps for views and +19 bps increase in search interactions, establishing the efficacy of the framework.