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Audience Creation for Consumables -- Simple and Scalable Precision Merchandising for a Growing Marketplace
arXiv - CS - Computers and Society Pub Date : 2020-11-17 , DOI: arxiv-2011.08575 Shreyas S, Harsh Maheshwari, Avijit Saha, Samik Datta, Shashank Jain, Disha Makhija, Anuj Nagpal, Sneha Shukla, Suyash S
arXiv - CS - Computers and Society Pub Date : 2020-11-17 , DOI: arxiv-2011.08575 Shreyas S, Harsh Maheshwari, Avijit Saha, Samik Datta, Shashank Jain, Disha Makhija, Anuj Nagpal, Sneha Shukla, Suyash S
Consumable categories, such as grocery and fast-moving consumer goods, are
quintessential to the growth of e-commerce marketplaces in developing
countries. In this work, we present the design and implementation of a
precision merchandising system, which creates audience sets from over 10
million consumers and is deployed at Flipkart Supermart, one of the largest
online grocery stores in India. We employ temporal point process to model the
latent periodicity and mutual-excitation in the purchase dynamics of
consumables. Further, we develop a likelihood-free estimation procedure that is
robust against data sparsity, censure and noise typical of a growing
marketplace. Lastly, we scale the inference by quantizing the triggering
kernels and exploiting sparse matrix-vector multiplication primitive available
on a commercial distributed linear algebra backend. In operation spanning more
than a year, we have witnessed a consistent increase in click-through rate in
the range of 25-70% for banner-based merchandising in the storefront, and in
the range of 12-26% for push notification-based campaigns.
更新日期：2020-11-18
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