GENERATING CONSUMER INSIGHTS FROM BIG DATA CLICKSTREAM INFORMATION AND THE LINK WITH TRANSACTION-RELATED SHOPPING BEHAVIOR

E-Commerce firms collect enormous amounts of information in their databases. Yet, only a fraction is used to improve business processes and decision-making, while many useful sources often remain underexplored. Therefore, we propose a new and interdisciplinary method to identify goals of consumers and develop an online shopping typology. We use k-means clustering and non-parametric analysis of variance tests to categorize search patterns as Buying, Searching, Browsing or Bouncing. Adding to purchase decision-making theory we propose that the use of off-site clickstream data—the sequence of consumers’ advertising channel clicks to a firm’s website—can significantly enhance the understand-ing of shopping motivation and transaction-related behavior, even before entering the website. To run our consumer data analytics we use a unique and extensive dataset from a large European apparel company with over 80 million clicks covering 11 online advertising channels. Our results show that consumers with higher goal-direction have significantly higher purchase propensities, and against our expectations - consumers with higher levels of shopping involvement show higher return rates. Our conceptual approach and insights contribute to theory and practice alike such that it may help to improve real-time decision-making in marketing analytics to substantially enhance the customer experience online

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

UNDERSTANDING CONSUMERS' ONLINE INFORMATION RETRIEVAL AND SEARCH: IMPLICATIONS FOR FIRM STRATEGIES

The growth of the Internet and other digitization technologies has enabled the unbundling of the physical and information components of the value chain and has led to an explosion of information made available to consumers. Understanding the implications of this new informational landscape for theory and practice is one of the key objectives of my research. My dissertation seeks to understand how firms can use their knowledge of online consumer search and information seeking behaviors to design optimal information provision strategies. The main premise is that consumers' online search behaviors are key to understanding consumers' underlying information needs and preferences. In my first essay I specifically focus on big-ticket, high-involvement goods for which firms essentially have sparse information on their potential buyers - making information reflected in consumers' online search very valuable to online retailers. I use a new and rich source of clickstream data obtained from a leading clicks-and-mortar retailer to model consumers' purchase outcomes as a function of the product and price information provided by the retailer, and find interesting differences for sessions belonging to customers classified as browsers, directed shoppers and deliberating researchers. Since consumers typically straddle online as well as traditional channels, the second essay in my dissertation examines how online information acquired by consumers affects their choices in offline used-good markets. Secondary markets characterized by information asymmetries have typically resorted to quality-signaling mechanisms such as certification to help reduce the associated frictions. However, the value of traditional quality signals to consumers depends crucially on the extent of the asymmetries in these markets. The online information available to consumers today may help bridge such asymmetries. Drawing upon a unique and extensive dataset of over 12,000 consumers who purchased used vehicles, I examine the impact of their information acquisition from online intermediaries on their choice of (reliance on) one such quality signal - certification, as well as the price paid. These findings will help firms to better understand how the provision of different types of online information impacts consumers' choices and outcomes, and therefore help them in designing better and targeted strategies to interact with consumers

Paths on a website: research on customers’ online search and purchase behaviours using clickstream data

This dissertation is comprised of three chapters studying customers’ online search and purchase behaviours using clickstream data collected from the context of air travel. The first chapter explores how choice overload effect influences customers’ purchase decisions under different time pressure conditions. Two studies yield two findings. The first finding is that larger choice sets result in purchase deferral, which is consistent with choice overload effects. Time pressure significantly moderates this effect, because more deferrals occur when the purchase deadline is further away. The second finding is that it is not the real, physical time passing per se that creates a sense of time pressure; instead, time pressure appears to be defined by customers’ perception of time limit, which moderates the choice overload effect by shifting customers’ regulatory focus. The second chapter develops the modelling approaches of using path data to predict purchases. We develop the concepts of two types of sequence of browsing behaviours: the sequence of search strategies and the sequence of viewing behaviours. We find that viewing behaviour is a better indicator of purchase tendencies. We develop the modelling approaches of predicting the next viewing behaviour and using this predicted viewing behaviour to predict the purchase probability. Our approach improves current methods of predicting purchases by overcoming two disadvantages: inflexibility in adaption to different websites and missing detailed information of customers’ behaviours. We provide managerial insights on customising information shown to customers according to predicted viewing behaviours in order to improve purchase conversion rates. The third chapter reveals the relationship between customers’ online search strategies and decision strategies. Our first finding is that the search strategy of filtering can be viewed as a decision strategy characterised by Elimination by aspects (EBA) strategy, while flexible searches can be viewed as a decision strategy featured by Satisficing strategy. Our second finding is that the goal-related variable has the predominate effect on choice of decision strategies in the studied context. Customers choose the decision strategy that can enable them to fulfil the goal of finding a good price, instead of the strategy that simplify the choice task.Open Acces

Real-Time Purchase Prediction Using Retail Video Analytics

The proliferation of video data in retail marketing brings opportunities for researchers to study customer behavior using rich video information. Our study demonstrates how to understand customer behavior of multiple dimensions using video analytics on a scalable basis. We obtained a unique video footage data collected from in-store cameras, resulting in approximately 20,000 customers involved and over 6,000 payments recorded. We extracted features on the demographics, appearance, emotion, and contextual dimensions of customer behavior from the video with state-of-the-art computer vision techniques and proposed a novel framework using machine learning and deep learning models to predict consumer purchase decision. Results showed that our framework makes accurate predictions which indicate the importance of incorporating emotional response into prediction. Our findings reveal multi-dimensional drivers of purchase decision and provide an implementable video analytics tool for marketers. It shows possibility of involving personalized recommendations that would potentially integrate our framework into omnichannel landscape

Comparison of Owned, Earned and Paid Website Visitors: a Case Study

This thesis explores website traffic and visitors by analysing website customer behaviour. The thesis expands the current research on web analytics to consider the rising categorization of media into owned, earned and paid media types. The research is first of its kind to further explore if there is significant difference between owned, earned and paid website visitors measured by web metrics. In addition to academic contributions, it is desired that the research helps marketers and publishers to invest their resources between generating each type of traffic in order to reach their individual goals and maximize the return-on-investment. In this paper, a framework for measuring owned, earned and paid website visitors is created. The research framework is tested in a case study where owned, earned and paid traffic is driven from Facebook to a fashion magazine’s online articles. Data on visitor-level website behavior of 2739 visitors is collected from the case website using Piwik analytics. The data was analyzed using two quantitative methods: chi-square test of homogeneity and one-way analysis of variance. These methods were used in order to determine whether statistically significant differences in website between owned, earned and paid visitor groups exists. Further, the case study demonstrates how to use the framework and appropriate techniques to effectively collect, extract, and analyze website visitor’s web behavior and the differences between owned, earned and paid website visitors. The empirical research reveals that significant differences between different types of website visitors exists. The chi-square test of homogeneity indicated a statistical significant difference of binomial proportions of ‘new / return user rate’, ‘bounce-rate’ and ‘mobile / desktop rate’ variables. One-way ANOVA indicated a statistical significant difference between the means of owned, earned and paid visitors of “visit count” and “actions”, but also a non-significant difference of “visit duration”. Thus also the usability of the research framework is confirmed. This thesis expands the research on clickstream data into social networking and earned media in media and journalism, and so contributes to the existing research on web analytics. This thesis also contributes to the existing literature on owned, earned and paid media and web analytics by adding owned and earned social media exposure to clickstream research and comparing them to paid social media exposure it in assessing user’s behavioral response in a cross-site context. Thus the thesis also combines social marketing with web analytics and expands the use ‘owned’, ‘paid’ and ‘earned’ jointly in a digital environment. This study is also first one to apply ‘heart rate monitoring’ measurement, redefined visit duration and bounce-rate metrics. The thesis provides useful technical and methodological information about website visitor tracking and web metrics for both academics and businesses seeking benefits from web analytics and online channels

Taking Stock of the Digital Revolution: A Critical Analysis and Agenda for Digital, Social Media, and Mobile Marketing Research

Marketing has been revolutionized due to the rise of digital media and new forms of electronic communication. In response, academic researchers have attempted to explain consumer- and firm-related phenomena related to digital, social media, and mobile marketing (DSMM). This paper presents a critical historical analysis of, and forward-looking agenda for, this work. First, we assess marketing’s contribution to understanding DSMM since 2000. Extant research falls under three eras, and a fourth era currently underway. Era 1 focused on digital tools and platforms as consumer and marketer decision aids. Era 2 studied online communications channels (e.g., online forums) as word of mouth marketing “laboratories,” capturing the potential of DSMM for social information transmission. Era 3 embraced the notion of “connected consumers” by considering various antecedents and consequences of socially interconnected consumers in marketplaces. Era 4, currently starting, considers mobile marketing and brings psychological and social theories to bear on emergent DSMM issues. Second, we critique the DSMM literature and advance a series of recommendations for future research. While we find much to applaud, we argue that several problems limit the relevance of this research moving forward and suggest ways to alleviate these concerns moving forward