Modeling Shopping Cart Decisions

The most recent consumer propensity study by SAP indicates that online shopping cart abandonment is high and the associated reasons are complex. In order to examine this phenomenon, we construct online SCA decision as a discrete choice model (DCM) and capture consumer segments by a latent class model (LCM) in this research-in-progress (RIP) paper, grounded on the theories of product involvement, word of mouth, and consumer heterogeneity. We will apply the clickstream dataset from 78,746 consumers at a large Chinese online platform to verify the proposed models in future study. The objective of this research project is to scrutinize the heterogeneous impacts of product involvement and online reviews on shopping cart decision-making in view of individual-level sequential behavior and the associated products in the form of stock-keeping-unit items. We conclude this RIP paper with the discussion of potential theoretical contributions and managerial implications

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

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

DESIGN AND DELIVERY OF ELECTRONIC SERVICES: IMPLICATIONS FOR CUSTOMER VALUE IN ELECTRONIC FOOD RETAILING

Electronic food retailers can satisfy their customers more effectively if they understand how this particular market works. As in other service segments, the emergence of electronic business-to-customer services in the retail food industry poses questions for managers about the design of new food retailing services and the redesign of existing services for delivery through electronic channels. Important topics include characteristics of electronic service offerings, the typical operational configurations used to deliver electronic services, and the ways in which they relate to the effectiveness of electronic service delivery. We address this issue by developing a product-process matrix for understanding and analyzing electronic retailing services in general. We tailor the matrix to food retailing in particular. The product-process matrix allows electronic food retailers to determine in advance what features they need in a web site to serve their chosen market effectively.Consumer/Household Economics, Marketing, Research and Development/Tech Change/Emerging Technologies,

Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure

Clicks to conversion: the value of product information and price incentives

Journal ArticleThis study uses clickstream data obtained from a large online durable goods retailer to examine how different types of information - product-related and price-related information provided by retailers - impact purchase-related outcomes for consumers. Using mixture-modeling techniques to analyze latent differences among customers, we find that consumers fall under three distinct categories - directed shoppers, deliberating researchers and browsers. In examining the impacts of information on purchase outcomes, we find that product and price-related information impacts consumers in these three shopping states differently. While product information highlighting features of product alternatives in a category has the strongest impact on deliberating researchers, specific price incentives related to category-level discounts increases the likelihood of purchase for both directed shoppers as well as browsers. Price incentives relating to site-wide free shipping have a positive impact on purchase for all consumers. Surprisingly, category-level discounts have a negative impact on deliberating researchers, while rich product information hampers the purchase process of directed shoppers. We discuss the managerial implications of our findings and the role of clickstream analytics in designing dynamic targeting and information provisioning strategies for online retailers

Developing a theoretical framework of consumer logistics from a comprehensive literature review

Paper delivered at the 21st Logistics Research Network annual conference 2016, 7th-9th September 2016, Hull. Abstract Purpose: Logistics as a business discipline entered academic consciousness in the mid-1960s when work by marketing academics discussed the integration between marketing and logistics. However, the link with consumers in the point-of-origin to point-of-consumption typology was not explored until Granzin and Bahn’s conceptualisation and model of consumer logistics (CL) in 1989. Since then few contributions have followed and neglecting this aspect of logistics research is difficult to understand. Firstly, the consumer represents a productive resource as an important downstream supply chain member carrying out logistics activities and tasks. Secondly, logistics activities directed towards the consumer also act along a marketing axis, i.e. satisfaction and loyalty for an overall shopping experience both from transaction-specific and cumulative levels are influenced by product quality elements and service-related dimensions. This paper presents a theoretical framework for deeper research into the topic of CL. Research approach: A literature review was conducted first following philosophical or field conceptualization principles as a first step towards theory building. Data bases of major logistics and SCM journals were searched however the publication timeframe was not limited as the concept of CL is relatively new. Selection criteria and Boolean searches were conducted and keywords used within article abstracts and title fields of search. Due to a relative scarcity of contributions obtained by that approach and in-line with the principle of methodological triangulation, additional search strategies were applied using Google/ Google Scholar searches. The majority of the cited contributions were also cross-referenced and included in the analysis if appropriate. Findings and originality: The literature search yielded a mother population of 46 documents of which 24 have been considered relevant for further consideration. The document harvest was analysed using Granzin and Bahn’s original CL issues and additional features in order to explore, structure, articulate, orient, hierarchize and delimit the field of CL in the 21st century. Research impact: This paper updates Granzin and Bahn’s work to outline new and distinctive features of CL given the obvious changes in the retail landscape since their work 27 years ago, such as the Internet and omni-channel retailing. More broadly, conceptualizing CL in a holistic manner enhances SCM theory building by questioning traditional notions of time and space ranges, isolated marketing-merchandizing/logistics considerations, traditional understandings of sites /locations, and equipment (e.g. shopping cart or basket)/ infrastructure/ layout and buying stages that are in-line with external evolutions on organizational, technological and societal levels. Practical impact: Understanding and improving CL contributes to supply chain competitiveness via increased consumer satisfaction and loyalty, better order fulfilment via cost reductions and efficiency increases, and enhanced differentiation targeting consumers receptive for sustainability/ ethics/ mobility/ lifestyle/ life quality issues. A dedicated approach to CL also enhances management of repercussions and interactions with upstream/ B2B logistics, visible through retail stores being both a destination and a source for inventory, the rise of drop-ship vendor relationships and new fulfilment options and related infrastructure