Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway

The development of Smart Grid in Norway in specific and Europe/US in general will shortly lead to the availability of massive amount of fine-grained spatio-temporal consumption data from domestic households. This enables the application of data mining techniques for traditional problems in power system. Clustering customers into appropriate groups is extremely useful for operators or retailers to address each group differently through dedicated tariffs or customer-tailored services. Currently, the task is done based on demographic data collected through questionnaire, which is error-prone. In this paper, we used three different clustering techniques (together with their variants) to automatically segment electricity consumers based on their consumption patterns. We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes. We found that the combination of Self Organizing Map (SOM) and k-means algorithms produce the most insightful and useful grouping. We also discovered that grouping quality cannot be measured effectively by automatic indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure

To boardrooms and sustainability: the changing nature of segmentation

Market segmentation is the process by which customers in markets with some heterogeneity are grouped into smaller homogeneous segments of more ‘similar’ customers. A market segment is a group of individuals, groups or organisations sharing similar characteristics and buying behaviour that cause them to have relatively similar needs and purchasing behaviour. Segmentation is not a new concept: for six decades marketers have, in various guises, sought to break-down a market into sub-groups of users, each sharing common needs, buying behavior and marketing requirements. However, this approach to target market strategy development has been rejuvenated in the past few years. Various reasons account for this upsurge in the usage of segmentation, examination of which forms the focus of this white paper. Ready access to data enables faster creation of a segmentation and the testing of propositions to take to market. ‘Big data’ has made the re-thinking of target market segments and value propositions inevitable, desirable, faster and more flexible. The resulting information has presented companies with more topical and consumer-generated insights than ever before. However, many marketers, analytics directors and leadership teams feel over-whelmed by the sheer quantity and immediacy of such data. Analytical prowess in consultants and inside client organisations has benefited from a stepchange, using new heuristics and faster computing power, more topical data and stronger market insights. The approach to segmentation today is much smarter and has stretched well away from the days of limited data explored only with cluster analysis. The coverage and wealth of the solutions are unimaginable when compared to the practices of a few years ago. Then, typically between only six to ten segments were forced into segmentation solutions, so that an organisation could cater for these macro segments operationally as well as understand them intellectually. Now there is the advent of what is commonly recognised as micro segmentation, where the complexity of business operations and customer management requires highly granular thinking. In support of this development, traditional agency/consultancy roles have transitioned into in-house business teams led by data, campaign and business change planners. The challenge has shifted from developing a granular segmentation solution that describes all customers and prospects, into one of enabling an organisation to react to the granularity of the solution, deploying its resources to permit controlled and consistent one-to-one interaction within segments. So whilst the cost of delivering and maintaining the solution has reduced with technology advances, a new set of systems, costs and skills in channel and execution management is required to deliver on this promise. These new capabilities range from rich feature creative and content management solutions, tailored copy design and deployment tools, through to instant messaging middleware solutions that initiate multi-streams of activity in a variety of analytical engines and operational systems. Companies have recruited analytics and insight teams, often headed by senior personnel, such as an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such personnel out-weigh posts for brand and marketing managers. Far more companies possess the in-house expertise necessary to help with segmentation analysis. Some organisations are also seeking to monetise one of the most regularly under-used latent business assets… data. Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a Chief Data Officer. This emerging role has also driven the professionalism of data exploration, using more varied and sophisticated statistical techniques. CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of value propositions and the need to look after core customers; CFOs because segmentation leads to better and more prudent allocation of resources – especially NPD and marketing – around the most important sub-sets of a market; COOs because they need to better look after key customers and improve their satisfaction in service delivery. More and more it is recognised that with a new segmentation comes organisational realignment and change, so most business functions now have an interest in a segmentation project, not only the marketers. Largely as a result of the digital era and the growth of analytics, directors and company leadership teams are becoming used to receiving more extensive market intelligence and quickly updated customer insight, so leading to faster responses to market changes, customer issues, competitor moves and their own performance. This refreshing of insight and a leadership team’s reaction to this intelligence often result in there being more frequent modification of a target market strategy and segmentation decisions. So many projects set up to consider multi-channel strategy and offerings; digital marketing; customer relationship management; brand strategies; new product and service development; the re-thinking of value propositions, and so forth, now routinely commence with a segmentation piece in order to frame the ongoing work. Most organisations have deployed CRM systems and harnessed associated customer data. CRM first requires clarity in segment priorities. The insights from a CRM system help inform the segmentation agenda and steer how they engage with their important customers or prospects. The growth of CRM and its ensuing data have assisted the ongoing deployment of segmentation. One of the biggest changes for segmentation is the extent to which it is now deployed by practitioners in the public and not-for-profit sectors, who are harnessing what is termed social marketing, in order to develop and to execute more shrewdly their targeting, campaigns and messaging. For Marketing per se, the interest in the marketing toolkit from non-profit organisations, has been big news in recent years. At the very heart of the concept of social marketing is the market segmentation process. The extreme rise in the threat to security from global unrest, terrorism and crime has focused the minds of governments, security chiefs and their advisors. As a result, significant resources, intellectual capability, computing and data management have been brought to bear on the problem. The core of this work is the importance of identifying and profiling threats and so mitigating risk. In practice, much of this security and surveillance work harnesses the tools developed for market segmentation and the profiling of different consumer behaviours. This white paper presents the findings from interviews with leading exponents of segmentation and also the insights from a recent study of marketing practitioners relating to their current imperatives and foci. More extensive views of some of these ‘leading lights’ have been sought and are included here in order to showcase the latest developments and to help explain both the ongoing surge of segmentation and the issues under-pinning its practice. The principal trends and developments are thereby presented and discussed in this paper

A typology categorization of millennials in their technology behavior

Hay un interés creciente por los millennials; y sin embargo, hasta la fecha hay escasas segmentaciones de los millennials en cuanto a su comportamiento en relación a la tecnología. En este contexto, este estudio trata las siguientes cuestiones:”¿Son los millennials monolíticos o hay diferentes segmentos en esta generación en cuanto a su comportamiento tecnológico?”. Y si este fuera el caso: “¿Existen diferencias importantes en cuanto a la forma en que los millennials usan la tecnología?”. Nuestro objetivo consiste en examinar los potenciales perfiles de los millennials en relación a su comportamiento y uso de la tecnología. Los datos obtenidos de una muestra de 707 millennials se analizaron mediante un análisis de componentes principales y análisis clúster. A continuación, los segmentos se caracterizaron mediante un análisis MANOVA. Nuestros resultados revelan la existencia de cinco segmentos o tipologías de millennials en cuanto a su comportamiento tecnológico: los “devotos de la tecnología”, los “espectadores”, los “prudentes”, los “adversos” y los “productivos”. Este estudio contribuye de forma detallada al conocimiento sobre cómo las diferentes categorías de millennials usan la tecnología.There is an increasing interest for millennials; however, to date millennials’ segmentations regarding their technology behavior are scarce. In this context, this study addresses the following questions: “Are millennials monolithic, or are there segments within this generation group regarding the technology behavior?”. And if so: “Are there important variances in the way that millennial segments use technology?”. Our purpose is to examine the potential profiles of millennials regarding their technology use and behavior. Data from a sample of 707 millennials was gathered and analyzed through principal component analysis and cluster analysis. Then, millennials’ segments were profiled using a MANOVA analysis. Our findings revealed five different segments or typologies of millennials regarding their technology behavior: technology devotees, technology spectators, circumspects, technology adverse users and productivity enhancers. This study contributes with a detailed perspective of how different millennial segments use technology

ANALYZING CUSTOMER VALUE USING CONJOINT ANALYSIS: THE EXAMPLE OF A PACKAGING COMPANY

The fulfillment of customers’ wishes in a profitable way requires that companies understand which aspects of their product and service are most valued by the customer. Conjoint analysis is considered to be one of the best methods for achieving this purpose. Conjoint analysis consists of generating and conducting specific experiments among customers with the purpose of modeling their purchasing decision. This article will give an overview of the method and apply it to an Estonian packaging company. As a result of the empirical study the author is able to estimate the value creation models of 34 respondents (customers) both on a group and individual basis.customer value, conjoint analysis, market research methods

Consumers’ Trade-Off Between Relationship, Service Package, And Price: An Empirical Study In The Car Industry

The prime objective of our study is to assess whether consumer segments based on relational aspects, service aspects, or price aspects have different preferences concerning these three key decision making variables when buying a car. In addition, we assessed consumer segments resulting from simultaneously incorporating relationships, service package, and price. We investigated a large sample of Mitsubishi drivers in the Netherlands emphasizing consumers’ trade-off between dealer relationship, service package and price. Conjoint analysis showed that dealer relationships (as opposed to price) represent a very important decision making variable when buying a car and consumer preferences concerning relationships provide a useful instrument for segmenting markets. Cluster analyses on the basis of three aspects simultaneously revealed that some consumers do value relationships, while others emphasize the service package in their purchase, both opposed to the third segment that is most probably not inclined to be loyal to a car dealer at all.Our study clearly indicates that different consumer segments can be distinguished on the basis of preferences for relationships and service packages rather than on the basis of price. This knowledge enables car dealers to use their resources more effectively.marketing ;

Consumers pnline: Intentions, orientations and segmentation

Purpose – This paper examines the purchase intentions of online retail consumers, segmented by their purchase orientation. Design/methodology/approach – An e-mail/web survey was addressed to a consumer panel concerning their online shopping experiences and motivations, n = 396. Findings – It is empirically shown that consumer purchase orientations have no significant effect on their propensity to shop online. This contradicts the pervasive view that Internet consumers are principally motivated by convenience. It was found that aspects that do have a significant effect on purchase intention are prior purchase and gender. Research limitations/implications – There are two limitations. First, the sample contained only UK Internet users, thus generalisations about the entire population of Internet users may be questionable. Second, in our measurement of purchase intentions, we did not measure purchase intent per se. Practical implications – These findings indicate that consumer purchase orientations in both the traditional world and on the Internet are largely similar. Therefore, both academics and businesses are advised to treat the Internet as an extension to existing traditional activities brought about by advances in technology, i.e. the multi-channel approach. Originality/value – The paper adds to the understanding of the purchase orientations of different clusters of e-consumer

Deriving Supply-side Variables to Extend Geodemographic Classification

The traditional proprietary geodemographic information systems that are on the market today use well-established methodologies. Demographic indicators are selected as a proxy for affluence and are then often linked to customer databases to derive a measure of the level of consumption expected from the different area typologies. However, these systems ignore fundamental relationships in the retail market by focusing upon demand characteristics in a ‘vacuum’ and ignore the supply side and consumer-supplier interaction. This paper argues that there may be considerable advantages to including supply-side indicators within geodemographic systems. Whilst the term ‘supply’ in this context might imply the number of consumer services already in an area, equally important for understanding demand are variables such as the supply of jobs and houses. We suggest that profiling an area in terms of its labour market characteristics gives a better insight into the income chain while the supply of houses could be argued to be a crucial factor in household formation that in turn will impact upon demographic structure. Using the regional example of Yorkshire and Humberside in northern England, we indicate how a suite of supply-side variables relating to the labour market can be assembled and used alongside a suite of demand variables to generate a new area classification. Spatial interaction models are calibrated to derive some of the variables that take into account zonal self-containment and catchment size

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