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Extract purchasing patterns for a focal product category using sales data: The case of skincare products, Astrolavos, Scientific Journal of New Technologies (Hellenic Mathematical Society), 25, 3-18, (in Greek).
Abstract
Customer satisfaction is the key of the success of any retail store or business and has an effect on their profitability. Companies have identified the importance to gain insights on their customer’s behavior, so as to better satisfy their needs. Both retailers and suppliers have
understood the need to cooperate in order to combine their data, gain knowledge and improve the offered services and customer satisfaction. Additionally, they have realized the potential mutual benefits if they combine their individual knowledge and expertise. Nowadays, the advent of business analytics, aids this cooperation. Thus, data mining techniques could be utilized to analyze the vast amount of data both retailers and suppliers have, to extract knowledge and support data-driven decision-making. However, not enough research has been
conducted to analyze such data in order to investigate consumers’ behavior regarding a specific product category. This study presents an effort to fill this gap by introducing a Data Mining-based framework, which could be used to discover sales affinities in customers' visits
in a supermarket related to a specific product category, and extract behavioral insights. The utility of this framework has been evaluated by applying it in real data of two representative supermarket stores of a Greek retailer. The proposed approach is useful for both an academic
and business perspective. It gives retailers the opportunity to extract how shoppers buy specific categories when they visit their stores, while it also enriches the suppliers’ knowledge about their shoppers. This knowledge can be used to support the decision-making process for all stakeholders in the retail domain, and improve their relationships with shoppers.
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Satisfying customers is one of the main objectives of every business. Businesses recognize that keeping current customers is more profitable than having to win new ones to replace those lost. Management and marketing theorists underscore the importance of customer satisfaction for a business's success. Customer satisfaction is the key factor in knowing the success of any retail store or business, therefore it is very important to measure it and to find the factors which affect the customer satisfaction. Customers are most likely to appreciate the goods and services that they buy if they are made to feel special. This occurs when they feel that the goods and services that they buy have been specially produced for them or for people like them. It should be always keep measuring in order to get feedback for the product and service in order to develop it further with wide customization. Customer satisfaction refers to the extent to which customers are happy with the products and services provided by a business. Customer satisfaction levels can be measured using survey techniques and questionnaires. Gaining high levels of customer satisfaction is very important to a business because satisfied customers are most likely to be loyal and to make repeat orders and to use a wide range of services offered by a business. Customer satisfaction is an asset that should be monitored and managed just like any physical asset. This is true for both service-oriented and product-oriented organizations.
The success of retail business is influenced by its fast response and its ability in understanding consumers’ behaviors. Analysis of transaction data is the key for taking advantage of these new opportunities, which enables supermarkets to understand and predict customer behavior, has become a crucial technique for effective decision-making and strategy formation. We propose a methodological framework for the use of the knowledge discovery process and its visualization to improve store layout. This study examines the layout strategy in relation to supermarket retail stores and assists managers in developing better layout for supermarkets. We use the buying association measure to create a category correlation matrix and we apply the multidimensional scale technique to display the set of products in the store space. This is a new approach to supermarket layout from industrial categories to consumption universes that is consumer-oriented store layout approach through a data mining approach. This framework is useful for both academia and retail industry. For industry professionals, it may be used to guide development of successful layout. Retailers can utilize the proposed model to dynamically improve their in-store conversion rate. As the empirical study, a practical application proceeded for Migros Turk, a leading Turkish retailing company.
Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data science programs, and publications are touting data science as a hot -- even "sexy" -- career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this paper we argue that there are good reasons why it has been hard to pin down exactly what data science is. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of Data Science precisely right now is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii) we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this paper we present a perspective that addresses all these things. We close by offering as examples a partial list of fundamental principles underlying data science.
To stay competitive, supermarkets need to develop a viable customer retention strategy. Since a key to the successful development of such a strategy rests with customer relationship management, supermarkets should identify the most profitable ways to build and maintain a loyal customer relationship. In an effort to help supermarkets understand their customers' shopping/patronage behaviour and the ways to retain valued customers, we propose data mining techniques. Using the examples of franchised supermarkets in the south-eastern United States, this paper illustrates the usefulness of the proposed data mining techniques for examining customer grocery shopping behaviour and developing the profiles of loyal patrons.
Two paradigms characterize much of the research in the Information
Systems discipline: behavioral science and design science. The behavioral-science
paradigm seeks to develop and verify theories that explain or predict
human or organizational behavior. The design-science paradigm seeks
to extend the boundaries of human and organizational capabilities
by creating new and innovative artifacts. Both paradigms are foundational
to the IS discipline, positioned as it is at the confluence of people,
organizations, and technology. Our objective is to describe the performance
of design-science research in Information Systems via a concise conceptual
framework and clear guidelines for understanding, executing, and
evaluating the research. In the design-science paradigm, knowledge
and understanding of a problem domain and its solution are achieved
in the building and application of the designed artifact. Three recent
exemplars in the research literature are used to demonstrate the
application of these guidelines. We conclude with an analysis of
the challenges of performing high-quality design-science research
in the context of the broader IS community.
This article was originally published following peer-review in Business Process Management Journal, published by and copyright Emerald. A number of Customer Relationship Management (CRM) implementations has grown dramatically in recent years. However, few academic studies of the issues associated with the implementation of the concept are available. In this paper, the author offers a modest contribution through the analysis of a case study of a CRM implementation at a United Kingdom based manufacturing company. The case study illustrates that CRM is a complex and holistic concept, organised around business processes and the integration of information technologies. The study also highlights that implementing CRM requires effective leadership, sourcing, targeting and evaluation strategies.
Grocery retailers have been informed that, to remain competitive, they must reduce the number of stockkeeping units (SKUs) offered, in line with consumer demand, or, in other words, adopt “Efficient Assortment.” Retailers have resisted this principle on the basis of a fear that eliminating items would lower consumer assortment perceptions and decrease the likelihood of store choice. In two studies, the authors examine how consumers form assortment perceptions in the face of SKU reduction with a particular emphasis on two heuristic cues: the availability of a favorite product and the amount of shelf space devoted to the category. Results indicate that retailers might be able to make substantive reductions in the number of items carried without negatively affecting assortment perceptions and store choice, as long as only low-preference items are eliminated and category space is held constant. Thus, the potential risk inherent in item reduction might be more limited than initially thought. The authors then discuss the implications of these findings for retailers, as well as additional measurement considerations.
In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.
Much academic research has been conducted about the process of association rule mining. More effort is now required for practical application of association rules in various commercial fields. A potential application of association rule mining is the problem of product assignment in retail. The product assignment problem involves how to most effectively assign items to sites in retail stores to grow sales. Effective product assignment facilitates cross-selling and convenient shopping for customers to promote maximum sales for retailers. However, little practical research has been done to address the issue. The current study approaches the product assignment problem using association rule mining for retail environments. There are some barriers to overcome in applying association rule mining to the product assignment problem for retail. This study conducts some generalizing to overcome drawbacks caused by the short lifecycles of current products. As a measure of cross-selling, lift is used to compare the effectiveness of various assignments for products. The proposed algorithm consists of three processes, which include mining associations among items, nearest neighbor assignments, and updating assignments. The algorithm was tested on synthetic databases. The results show very effective product assignment in terms of the potential for cross-selling to drive maximum sales for retailers.
The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. Data analysis, organization, retrieval, and modeling are other foundational challenges. Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. Finally, presentation of the results and its interpretation by non-technical domain experts is crucial to extracting actionable knowledge.During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led, during the last 35 years, to a multi-billion dollar industry. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today. The many novel challenges and opportunities associated with Big Data necessitate rethinking many aspects of these data management platforms, while retaining other desirable aspects. We believe that appropriate investment in Big Data will lead to a new wave of fundamental technological advances that will be embodied in the next generations of Big Data management and analysis platforms, products, and systems.We believe that these research problems are not only timely, but also have the potential to create huge economic value in the US economy for years to come. However, they are also hard, requiring us to rethink data analysis systems in fundamental ways. A major investment in Big Data, properly directed, can result not only in major scientific advances, but also lay the foundation for the next generation of advances in science, medicine, and business.
The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement customer relationship management (CRM) in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify CRM strategies used by retail companies. A keyword search within business databases using CRM and CRM identified publications with CRM content. Content analysis was used on articles (N=149) drawn from Stores, Chain Store Age, Harvard Business Review, and Retail Forward over a 5 year period (2000–2005). Selected articles were stored as text files in QDA Miner, a computerized qualitative analysis tool. Key organization/infrastructure needs emerged focusing on data structure, organizational systems, technology structure, and data accessibility. Retailers goals/objectives and strategies focused on marketing, customer service, understanding customers through data analysis and increasing acquisition and retention through customer loyalty programs. Data mining tools identified supported marketing and customer analysis efforts. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.
As more retailers evolve into customer-centric and segment-based business, business intelligence (BI) and customer relationship management (CRM) systems are playing a key role in achieving and maintaining competitive advantage. For the past ten years, the authors have had the rare opportunity of observing and interviewing employees and managers of three different management teams at three separate Fingerhut companies as they experimented with various ITs for their companies. When the first Fingerhut company peaked in 1998, as many as 200 analysts and 40 statisticians mined the database for insights that helped predict consumer shopping patterns and credit behaviour. Data mining and BI helped Fingerhut spot shopping patterns, bring product offerings to the right customers, and nurture customer relationships. By 1998, Fingerhut was the second largest catalogue retailer in the U.S. with revenues nearing $2 billion. However, after Federated acquired Fingerhut in 1999 and made it a subsidiary, Fingerhut Net, it suffered great losses and was eventually liquidated. Finally, a new company, Fingerhut Direct Marketing, was resurrected in 2002 under a new management team, and it once again became successful. What went right? What went wrong? The paper concludes with CRM and BI systems success factors and a discussion of lessons learned.
Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000–2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.
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