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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.