Shortening Delivery Times by Predicting Customers’ Online Purchases: A Case Study in the Fashion Industry

Abstract

Online retailers still struggle with the disadvantage of delivery times compared to traditional brick and mortar stores. With the emergence of big data analytics, it has become possible to extract meaningful knowledge from the volume of data that online retailers collect on their website. Nevertheless, limited research exists that investigates how this data can be used to optimize delivery times for customers. The goal of this paper is to develop a prediction model for anticipatory shipping, which predicts customers' online purchases with the aim of shipping products in advance, and subsequently minimize delivery times. Different forecasting methods in combination with k-means clustering are applied to test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The proposed prediction model can easily be implemented and used to predict purchases, which can also be leveraged for other areas of application besides anticipatory shipping.

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

“…Polyvore [78,126] PolyvoreMayland [166] retailshop [33,156,[185][186][187]195,196,198,199,202,207,208,211,[214][215][216]229,232,246,254] RetailshopfromtheInternet [218,237] RetailshopsfromtheInternet [71,99,105,109,111,144,182,189,…”