A Novel Delivery Model to Expand E-commerce Customers Based on Telecom Data Mining

9 pages•Published: March 13, 2019

Giridhar Maji, Sharmistha Mandal, Narayan Debnath and Soumya Sen

Abstract

A very important issue with the e-commerce delivery service in most of the emerging economies including India is the last mile connectivity. Delivering products, booked online to the remote tier-2 and tier-3 cities remained “costly”. It is observed from firsthand experience with some well-known e-commerce brands in India that their delivery service partners tend to cancel orders that are far away from their tier-2 logistics hubs with the reason shown as “address out of delivery range”. Due to low order density in the far flanges of tier-2 and tier-3 cities arranging vehicles and delivery personnel become costly. In this paper, we propose an innovative delivery model to serve the remote areas by opening edge-hubs at selected places and employing local daily commuters for last mile delivery. Identifying the edge-hubs for opening distribution centers is a costly business if done using traditional field surveys. Here we propose the use of telecom call detail record (CDR) location data as an alternate way of identifying the hubs in real time with much less cost and time.

Keyphrases: cdr, delivery model, e commerce, telecom data mining

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 336-344.

Links:	https://easychair.org/publications/paper/v42W
	https://doi.org/10.29007/vn4r

BibTeX entry

@inproceedings{CATA2019:Novel_Delivery_Model_Expand,
  author    = {Giridhar Maji and Sharmistha Mandal and Narayan Debnath and Soumya Sen},
  title     = {A Novel Delivery Model to Expand E-commerce Customers Based on Telecom Data Mining},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/v42W},
  doi       = {10.29007/vn4r},
  pages     = {336-344},
  year      = {2019}}

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