Evolutionary computing applied to customer relationship management: A survey

Krishna, Gutha Jaya; Ravi, Vadlamani

doi:10.1016/j.engappai.2016.08.012

Cited by 49 publications

(18 citation statements)

References 103 publications

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“…The customer-focused businesses are better equipped to deliver superior and added value to their identified key customer. This is being done through personalized products and/or services (Vandermerwe, 2004) which is aimed to improve the customer experiences (Ata & Toker, 2012; Krishna & Ravi, 2016). The ‘personalization’ has been included as a separate dimension of CRM for the purposes of the undertaken study.…”

Section: Crm Dimensions/constructs: Sin Et Al (2005)mentioning

confidence: 99%

Customer Relationship Management as Tool to Enhance Competitive Effectiveness: Model Revisited

Sofi

Hakim

2018

FIIB Business Review

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Maintaining collaborative relationships of a business firm with the customer is very important in the contemporary competitive business scenario, besides the service rendered by it. Against this backdrop, the study was undertaken which aims to analyse the customer relationship management (CRM) practices and dimensions in the business. The past research studies were reviewed and the CRM frameworks given by the various authors, in particular of Sin, Tse, and Yim (2005, European Journal of Marketing, 11(12), 1264–1290), were critically examined. The research article based on the study revisited the four behavioural dimensions of CRM. A total of 225 respondents were conveniently contacted from banks and hotel establishments. In view of the challenges faced by the business organizations in the contemporary scenario, in particular in the service sector, this article identified and discusses ‘customer prospecting’ and ‘personalization’ as additional practices/dimensions that need to be incorporated to design a robust CRM framework which has been presented as revisited model. Structural equation modeling (SEM) had been used to validate the revisited model. These dimensions, though need to be ratified in the future research.

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Section: Crm Dimensions/constructs: Sin Et Al (2005)mentioning

confidence: 99%

Customer Relationship Management as Tool to Enhance Competitive Effectiveness: Model Revisited

Sofi

Hakim

2018

FIIB Business Review

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“…Yang et al (2016) used bacterial foraging optimization and made a comparative analysis with other methods to prove the superiority of their approach. A review of articles focusing on application of evolutionary computation and various data mining techniques for effective customer relationship management was conducted by Krishna and Ravi (2016). However, the primitive versions of these algorithms face the problem of immature convergence.…”

Section: Evolutionary Algorithms and Their Variants Have Been Deployementioning

confidence: 99%

Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

Maiyar

Cho

Tiwari

et al. 2018

International Journal of Production Research

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Bowing to the burgeoning needs of online consumers, exploitation of social media content for extrapolating buyer-centric information is gaining increasing attention of researchers and practitioners from service science, data analytics, machine learning and associated domains. The current paper aims to identify the structural relationship between product attributes and subsequently prioritize customer preferences with respect to these attributes while exploiting textual social media data derived from fashion blogs in Germany. A Bayesian Network Structure Learning (BNSL) model with K2-score maximization objective is formulated and solved. A selftailored metaheuristic approach that combines Self-Learning Particle Swarm Optimization (SLPSO) with K2 algorithm (SLPSOK2) is employed to decipher the highest scored structures. The proposed approach is implemented on small, medium and large size instances consisting of nine fashion attributes and 18 problem sets. The results obtained by SLPSOK2 are compared with Particle Swarm Optimization/K2 score (PSOK2), Genetic Algorithm/K2 score (GAK2), and Ant Colony Optimization/K2 score (ACOK2). Results verify that SLPSOK2 outperforms its hybrid counterparts for the tested cases in terms of computational time and solution quality. Furthermore, the study reveals that psychological satisfaction, historical revival, seasonal information and facts and figure based reviews are major components of information in fashion blogs that influence the customers.

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“…Customer Relationship Management (CRM) is a customer-centric business strategy which a company employs to improve customer experience and satisfaction by customizing products and services to their customers’ needs [ 1 ]. CRM systems offer companies the possibility of obtaining competitive advantages through better knowledge while maintaining a close relationship with their customers [ 2 , 3 ].…”

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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Evolutionary computing applied to customer relationship management: A survey

Cited by 49 publications

References 103 publications

Customer Relationship Management as Tool to Enhance Competitive Effectiveness: Model Revisited

Customer Relationship Management as Tool to Enhance Competitive Effectiveness: Model Revisited

Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

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

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