Characteristics Description of Potential User Segments on the E-Commerce Website oriented to Precision Marketing

In the increasingly competitive environment between e-commerce companies, for more accurate implementation of marketing strategies, e-commerce websites often choose to subdivide the consumer market of the enterprise to identify site users’ characteristics to find their needs. In this paper, we subdivide consumer market from the four dimensions of behavior, geography, demography and psychology and propose a model to describe the characteristics of potential user market segments. Based on the web log data and user transaction data, a classification algorithm is used to analyze user behavior data in Web log to find the potential user segments and the user\u27s descriptive characteristics in user transaction data are clustered to obtain the distribution of consumer characteristics under various product categories, then we use the product categories in e-commerce website as an intermediary to give every single potential user in potential user market segments the descriptive characteristics, which can provide data support for the realization of precision marketing. The proposed model is applied to the actual data of a certain insurance e-commerce platform, and based on the results, we gain some implications for marketing of the e-commerce website

Marketing management of a successful e-business

Marketing management occupies an increasingly important position in the business world, as well as in the sphere of electronic commerce. Some participants, however, underestimate the importance of this marketing support, which may be one of the major causes of the failure and inability of some companies operating on the Internet to grow. The aim of this paper is to develop an effective marketing management process model, which can significantly contribute to the increased competitiveness of companies operating on the Internet. The validity of this model is then applied on a Czech e-shop, which has long been one of the leaders of the Czech Internet market. To achieve the objective of this paper the current situation will be analysed, and synthesis of the findings from research literature as well as modelling using the methods of abstraction and specification will be performed. This article is focused on Czech Internet market. Results of the survey (case study) will be used for further research in the field of e-business

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

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Promotion and Marketing Communications

This edited Promotion and Marketing Communications book is an original volume that presents a collection of chapters authored by various researchers and edited by marketing communication professionals. To survive in the competitive world, companies feel an urge to achieve a competitive advantage by applying accurate marketing communication tactics. Understanding marketing communication is an essential aspect for any field and any country. Hence, in this volume there is the latest research about marketing communication under which marketing strategies are delicately discussed. This book does not only contribute to the marketing and marketing communication intellectuals but also serves different sector company managerial positions and provides a guideline for people who want to attain a career in this field, giving them a chance to acquire the knowledge regarding consumer behavior, public relations, and digital marketing themes

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

ANALYSIS OF BEST PRACTICE OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION IN DIGITAL MARKETING ACTIVITIES

Rapid development of artificial intelligence is transforming the world we live in. Advancement in technology and consumer\u27s needs creates the urge for rapid adaptation by companies operating in a volatile and uncertain marketing environment in order to adequately shape their marketing decisions and achieve the best results on the market. The availability of information to consumers is greater than ever before causing an increase in the needs and demands they expect when buying and consuming a product or a service which results in higher efforts of personalization and individualization while creating marketing messages. This is precisely what innovative and disruptive technologies, such as intelligent self-learning systems based on artificial intelligence, allow companies to gain a better insight into the consumer\u27s needs and create marketing content that will result in higher engagement and conversion rates. This study investigates and analyses set of examples of best practices of artificial intelligence implementation and the benefits of its usage in marketing activities and campaigns in automotive, retail and hospitality industry through predicting, testing and optimizing. Study shows the way artificial intelligence systems make an exceptional contribution to the optimization of marketing activities and overall marketing performance efficiency. The paper ends with the conclusions and recommendations how to implement some of the presented AI solutions into the Croatian business practice