Green vs. Non-Green Customer Behavior: A Self-Organizing Time Map over Greenness

Holmbom, Annika H.; Rönnqvist, Samuel; Sarlin, Peter; Eklund, Tomas; Back, Barbro

doi:10.1109/icdmw.2013.103

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…A set of 9 studies out of 23 papers presented in the literature are covering the use of VA systems in the plan process activities of the SC. These visualizations that mainly concern the planning of SC operations have been employed in various use cases, the most significant of which are sales and demand analysis [24], [41]- [43].…”

Section: ) Plan Processesmentioning

confidence: 99%

“…Identifying customer behaviors is another VA task that supports decision making in the plan business processes. In the study presented in [43], the quantification of the tendency of customers towards buying green products is discussed. This objective can be helpful both for the production and marketing planning, as well as for sustainability decision making.…”

Section: ) Plan Processesmentioning

confidence: 99%

“…In general, clustering of data is a method that helps identifying hidden patterns in data. However, the authors of the study presented in [43], decided to use a Self-Organizing Time map (SOTM) to identify the different degrees of the so called customers greenness, instead of general partitioning of customers into groups of green and non-green. The SOTM algorithm indeed, is another method to analyze the multidimensional data by viewing the data from a single variable point-of-view.…”

Section: ) Plan Processesmentioning

confidence: 99%

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Visual Analytics for Decision Support: A Supply Chain Perspective

2021

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Supply chain (SC) activities generate huge amount of data that can be used in decision making processes. However, proper data analytics techniques are required to combine, organize, and analyze data from different sources and produce required insights available for decision makers. These techniques promote analytical reasoning of the events and patterns hidden in the data using visualizations, so-called Visual Analytics (VA). Although there is a large number of VA systems to facilitate the process of analysis and decision making, there is a lack of an adequate overview of what already exists in this area for SC management. To address that need, we conducted a systematic literature review to analyze the state of the art in SC VA systems. Particularly, we focus on use cases, the type of the decisions that a VA system intended to support, the type of visualizations employed, the type of analytics used, and the data that has been used for analysis. The goal of this study is to provide SC and VA researchers with an overview of the works carried out in the field of SC VA, helping them to observe latest trends and to recognize existing gaps that need further investigation. Consequently, a mapping between decisions of various SC business processes and their reciprocal visualization techniques and tactics have been provided. Adding to that, VA applications and use cases in SC are identified based on the SC Operation Reference (SCOR) model and underlying decision areas are recognized.

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Section: ) Plan Processesmentioning

confidence: 99%

Section: ) Plan Processesmentioning

confidence: 99%

Section: ) Plan Processesmentioning

confidence: 99%

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Visual Analytics for Decision Support: A Supply Chain Perspective

2021

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“…Among the four CRM dimensions, customer development (19 out of 51 articles, 37.3 %) is the most common dimension for which data analytics is used to support decision making. [18], [27], [40], [46] , [47] , [50], [55], [67] Customer Attraction 16 31 % [19], [20], [29], [34], [37], [44], [45], [49], [52], [53], [57], [59], [61], [65], [66], [68] Customer Retention 7 14 % [17], [21], [24], [26], [28], [35], [64] Customer Development 19 37 % [3], [22], [23], [25], [30], [31], [32], [33], [36], [38], [42], [43], [48], [51], [56], …”

Section: Classification Of the Articlesmentioning

confidence: 99%

“…Among the seven data mining techniques, clustering (7 out of 51 articles, 14 %) is the most common data mining technique for which data analytics is used to support decision making. [20], [22], [23], [32], [36], [38] [35], [42], [51], [55], [59] Full list of reviewed publications with classification is available at https://drive.google.com/open?id=0Bwp9RlyV--pwcFg1dC1kSzlMNG8 VI. CONCLUSION Application of data analytics in CRM is an emerging trend in the industry.…”

Section: Classification Of the Articlesmentioning

confidence: 99%

Data Analytics in CRM Processes: A Literature Review

Gončarovs

2017

Information Technology and Management Science

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-Nowadays, the data scarcity problem has been supplanted by the data deluge problem. Marketers and Customer Relationship Management (CRM) specialists have access to rich data on consumer behaviour. The current challenge is effective utilisation of these data in CRM processes and selection of appropriate data analytics techniques. Data analytics techniques help find hidden patterns in data. The present paper explores the characteristics of data analytics as the integrated tool in CRM for sales managers. The paper aims at analysing some of the different analytics methods and tools which can be used for continuous improvement of CRM processes. A systematic literature has been conducted to achieve this goal. The results of the review highlight the most frequently considered CRM processes in the context of data analytics.

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Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization

Bourennani

2019

International Journal of Applied Metaheuristic Computing

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Nowadays, we have access to unprecedented quantities of data composed of heterogeneous data types (HDT). Heterogeneous data mining (HDM) is a new research area that focuses on the processing of HDT. Usually, input data is transformed into an algebraic model before data processing. However, how to combine the representations of HDT into a single model for a unified processing of big data is an open question. In this article, the authors attempt to find answers to this question by solving a data integration (DI) problem which involves the processing of seven HDT. They propose to solve the DI problem by combining multi-objective optimization and self-organizing maps to find optimal parameters settings for most accurate HDM results. The preliminary results are promising, and a post processing algorithm is proposed which makes the DI operations much simpler and more accurate.

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Green vs. Non-Green Customer Behavior: A Self-Organizing Time Map over Greenness

Cited by 3 publications

References 20 publications

Visual Analytics for Decision Support: A Supply Chain Perspective

Visual Analytics for Decision Support: A Supply Chain Perspective

Data Analytics in CRM Processes: A Literature Review

Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization

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