Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts

When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternative products for the one that was out of stock. Here we develop a Bayesian hierarchical model for inferring the underlying customer arrival rate and choice model from sales transaction data and the corresponding stock levels. The model uses a nonhomogeneous Poisson process to allow the arrival rate to vary throughout the day, and allows for a variety of choice models. Model parameters are inferred using a stochastic gradient MCMC algorithm that can scale to large transaction databases. We fit the model to data from a local bakery and show that it is able to make accurate out-of-sample predictions, and to provide actionable insight into lost cookie sales

DATA MINING: A SEGMENTATION ANALYSIS OF U.S. GROCERY SHOPPERS

Consumers make choices about where to shop based on their preferences for a shopping environment and experience as well as the selection of products at a particular store. This study illustrates how retail firms and marketing analysts can utilize data mining techniques to better understand customer profiles and behavior. Among the key areas where data mining can produce new knowledge is the segmentation of customer data bases according to demographics, buying patterns, geographics, attitudes, and other variables. This paper builds profiles of grocery shoppers based on their preferences for 33 retail grocery store characteristics. The data are from a representative, nationwide sample of 900 supermarket shoppers collected in 1999. Six customer profiles are found to exist, including (1) "Time Pressed Meat Eaters", (2) "Back to Nature Shoppers", (3) "Discriminating Leisure Shoppers", (4) "No Nonsense Shoppers", (5) "The One Stop Socialites", and (6) "Middle of the Road Shoppers". Each of the customer profiles is described with respect to the underlying demographics and income. Consumer shopping segments cut across most demographic groups but are somewhat correlated with income. Hierarchical lists of preferences reveal that low price is not among the top five most important store characteristics. Experience and preferences for internet shopping shows that of the 44% who have access to the internet, only 3% had used it to order food.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Forecasting modeling and analytics of economic processes

The book will be useful for economists, finance and valuation professionals, market researchers, public policy analysts, data analysts, teachers or students in graduate-level classes. The book is aimed at students and beginners who are interested in forecasting modeling and analytics of economic processes and want to get an idea of its implementation