Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences

A primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers’ needs. A customer’s purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer’s probability of a next purchase and target the customers most prone to purchasing a particular product. The bias– variance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias–variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions

Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile

Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model

Objectives of financial statements: Report of the Study Group on the Objectives of Financial Statements

Committee members are: Cyert, Richard M.; Davidson, Sidney;Edwards, James Don; Gellein, Oscar S.;Parker, C. Reed;Reinhart, Andrew J.; Trueblood, Robert M.;Wagner, Howard O.;Weston, Frank T.

Market power in an exhaustible resource market: The case of storable pollution permits

Motivated by the structure of existing pollution permit markets, we study the equilibrium path that results from allocating an initial stock of storable permits to a large polluting agent and a competitive fringe. A large agent selling permits in the market exercises market power no differently than a large supplier of an exhaustible resource. However, whenever the large agent’s endowment falls short of its efficient endowment –allocation profile that would exactly cover its emissions along the perfectly competitive path– the market power problem disappears, much like in a durable-good monopoly. We illustrate our theory with two applications: the carbon market that may eventually develop under the Kyoto Protocol and beyond and the US sulfur market.Exhaustible resources, market power, pollution markets, durable-good monopoly

Essays on Monetary Policy

Central banks use a series of relatively small interest rate changes in adjusting their monetary policy stance. This persistence in interest rate changes is well documented by empirical monetary policy reaction functions that feature a large estimated coefficient for the lagged interest rate. The two hypotheses that explain the size of this large estimated coefficient are monetary policy inertia and serially correlated macro shocks. In the first part of my dissertation, I show that the effect of inertia on the Federal Reserve’s monthly funds rate adjustment is only moderate, and smaller than suggested by previous studies. In the second part, I present evidence that the temporal aggregation of interest rates puts an upward bias on the size of the estimated coefficient for the lagged interest rate. The third part of my dissertation is inspired by recent developments in the housing market and the resulting effect on the overall economy. In this third essay, we show that high loan-to-value mortgage borrowing reduces the effectiveness of monetary policy

The impact of flooding on the value of residential property in the UK

Submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy (PhD)Flooding of residential property is a real and growing phenomenon in the UK causing short and long-term detriment of various kinds to its victims. The issue of potential decrease in value of those properties which are located on the floodplain, though much discussed in the media, has received scant attention in the UK research literature. An extensive literature survey has revealed a need for methodological innovation in the field of temporal impact of flooding and the inadequacy of the current paradigms for inclusion of insurance into flood modelling. A wide-ranging review of data sources, including discussion with industry experts, has identified the requirement to generate primary data on the availability and cost of flood insurance. A novel framework has been developed for this research. This framework is an extension of the recent research in flood modelling and incorporates ideas from the wider house price analysis literature. Data collected via a questionnaire survey of householders has been combined with secondary data on property prices and flood designation in order to attribute any loss in property value to the correct vector of underlying flood status. The output from this study makes a contribution to the understanding of the impact of flooding on house prices, allowing for better valuation advice. Empirical findings are that the understandable concerns of residential property owners at risk of flooding regarding long term loss of property value are largely unfounded. Price discounts are observed for some recently flooded areas but they are temporary Improved appreciation of the impact of claims and flood risk on the cost of insurance has also emerged. The insurance market was not found to be instrumental in reducing the price of property. The output from the study also makes a methodological contribution in extending concepts relating to the relationship between flooding, insurance and house prices. This development is anticipated to facilitate refinement and updating of the empirical findings with reduced effort in the light of future events

Objectives of financial statements: Selected papers

Committee members are: Cyert, Richard M.; Davidson, Sidney;Edwards, James Don; Gellein, Oscar S.;Parker, C. Reed;Reinhart, Andrew J.; Trueblood, Robert M.;Wagner, Howard O.;Weston, Frank T.