A systematic review for the determination and classification of the CRM critical success factors supporting with their metrics

Salah, Mona; Abed, Amira Hassan; Ellatif, Mahmoud Abd

doi:10.1016/j.fcij.2018.11.003

Cited by 35 publications

(35 citation statements)

References 37 publications

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“…Customer Relationship Management (CRM) emerged in the 1990s, as an extension of relationship marketing [34,35]. However, it has been noted that CRM exceeds the boundaries of marketing to include human resource management, sales, customer service, and other functions dealing directly with the customer [36][37][38].…”

Section: Crmmentioning

confidence: 99%

“…From a strategic perspective, CRM is about creating and maintaining profitable customer relationships in the long-term [37] and attempts to understand the needs and desires of consumers through the integration of strategy, people, technology, and business processes [38]. The main concept of CRM involves the differentiation of individual customers, in order to provide personalized treatment and deliver customized value based on their specific needs [35]. Advantages of CRM for both companies and customers include higher profits, customer satisfaction improvement and loyalty, and maximized customers' lifetime and costs efficiency [35,36].…”

Section: Crmmentioning

confidence: 99%

“…The main concept of CRM involves the differentiation of individual customers, in order to provide personalized treatment and deliver customized value based on their specific needs [35]. Advantages of CRM for both companies and customers include higher profits, customer satisfaction improvement and loyalty, and maximized customers' lifetime and costs efficiency [35,36]. Furthermore, from a technical perspective, CRM involves the collection and storage of customer data, the analysis of consumers' profit sustainability, and a lifetime value approach to managing customers [39].…”

Section: Crmmentioning

confidence: 99%

See 2 more Smart Citations

Transformation of CRM Activities into e-CRM: The Generating e-Loyalty and Open Innovation

Farmania

Elsyah

Tuori

2021

Journal of Open Innovation: Technology, Market, and Complexity

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Technological developments have digitized consumer behavior all over Indonesia. This has become the basis for the emergence of e-commerce, a digital business system that is widely used by the public in making purchases through apps or websites. For this reason, an e-commerce platform should create e-loyalty, to make it the best option among many stores. This is the first study investigating ten factors that contribute to e-Customer Relationship Management (e-CRM) value on e-loyalty in Indonesia. The study involved 767 active users of the number one e-commerce company in Indonesia. An empirical investigation was carried out, to validate the instrument items regarding the e-CRM factors and e-loyalty indicators, using Explanatory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The analysis utilizes Structural Equation Modeling (SEM) to test the hypotheses. The finding suggested that the variables of customization, care, cultivation, choice, online community, convenience, site security, personalization values, rewards, and interactivity positively contribute to e-CRM value on e-loyalty.

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Section: Crmmentioning

confidence: 99%

Section: Crmmentioning

confidence: 99%

Section: Crmmentioning

confidence: 99%

See 1 more Smart Citation

Transformation of CRM Activities into e-CRM: The Generating e-Loyalty and Open Innovation

Farmania

Elsyah

Tuori

2021

Journal of Open Innovation: Technology, Market, and Complexity

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“…In this article we relied on research of such domestic economists as Rybakova and Orlov (2017), Spitsyn, and Fedorets (2017) and others. From the western authors it should be noted works of Cruz-Jesus, Pinheiro and Oliveira (2019), Haddara and Constantini (2017), Kunath and Winkler (2019), Marwa, Amira, and Mahmoud (2018), Soltani and Navimipour (2016) and others.…”

Section: Problem Statementmentioning

confidence: 96%

Application Of Crm Systems In Enterprises In The Digital Economy

Efremova¹,

Petryanina²

2020

European Proceedings of Social and Behavioural Sciences

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“…As previously discussed, CRM is an important framework for managing an organisation’s interactions with its current and future customers [ 50 , 51 ]. Identification of duplicated customers in the CRM is also receiving increasing attention in the literature [ 52 , 53 , 54 , 55 ]. When there are many customers in the CRM database with similar names, or names with similar spellings to those of the customer to be identified, recognition becomes difficult.…”

Section: Introductionmentioning

confidence: 99%

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Serrano

Ballestero

Muñoz-Romero

et al. 2019

Entropy

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Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.

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A systematic review for the determination and classification of the CRM critical success factors supporting with their metrics

Cited by 35 publications

References 37 publications

Transformation of CRM Activities into e-CRM: The Generating e-Loyalty and Open Innovation

Transformation of CRM Activities into e-CRM: The Generating e-Loyalty and Open Innovation

Application Of Crm Systems In Enterprises In The Digital Economy

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

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