Modeling of fuzzy-based voice of customer for business decision analytics

Aguwa, Celestine; Olya, Mohammad Hessam; Monplaisir, Leslie

doi:10.1016/j.knosys.2017.03.019

Cited by 55 publications

(28 citation statements)

References 44 publications

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“…The trials we completed on Yelp assessments gave promising impacts. [10] The take a look at result suggests that for companies within the banking sector so one can increase their patron retention, they must focus at the customers‗ pride and feel of belongingness to the agency. The internal inspection / audit of the bank will address attributes on customer service and 0. complaints manageme6fvt7by8uiok,pl.…”

Section: Related Workmentioning

confidence: 99%

A Design on Bank Customer Complaints Analysis using Natural Language Processing

2019

IJITEE

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The banking sector has undergone a major revolution with the advent of digital transformation. The entry of Fintech and tech giants such as Google, Amazon, and Facebook have introduced convenient banking that is easy to understand and use. In this focused condition, banks are understanding the significance of client care and fulfillment and need to give close consideration to the Voice of Customer to improve client experience. By dissecting and getting bits of knowledge from client input, banks will have better data to settle on key choices. In their quest to better understand their customers, banks are seeking artificial intelligence (AI) solutions in the form the of sentiment analysis. What is sentiment analysis? In simple words, sentiment analysis is the process of detecting a customer’s reaction to a product, brand, situation or event through texts, posts, reviews, and other digital content. Using sentiment analysis, business leaders can gain deep insight into how their customers think and feel. The analysis can help in tracking customer opinions over a period of time, determine customer segmentation, plan product improvements, prioritize customer service issues, and many more business use cases

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Section: Related Workmentioning

confidence: 99%

A Design on Bank Customer Complaints Analysis using Natural Language Processing

2019

IJITEE

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“…A probabilistic Naïve Bayes-based classifier has been proposed to automatically identify customer requirements by developing preferred features of a product's feature [9]. Aguwa et al [10] used text mining to extract significant attributes of a product and to develop a real-time system to monitor customer feedback by learning association rules; the system includes a unique model that uses fuzzy logic to identify negative and positive feedback. Liang et al [11] used the topic model of latent Dirichlet allocation (LDA) to identify product features that customers frequently mentioned, then identified product problems by exploring the relationship of product features using association rule mining.…”

Section: Text Mining For Market-oriented Product Developmentmentioning

confidence: 99%

“…Prior studies [5][6][7][8][9][10][11][12][13][14] identified customer needs on the basis of product features by analysing unstructured text data provided by customers but this approach is insufficient to represent customers' true requirements. When a customer uses a product, several steps are involved, such as ordering, installing, using, monitoring and arranging [29].…”

Section: Text Mining For Market-oriented Product Developmentmentioning

confidence: 99%

“…Many studies [5][6][7][8][9][10][11][12][13][14] have analysed unstructured text data such as customer suggestions, customer complaints and customer feedback to identify customer needs for the product development. However, the previous research used keyword extraction, regression analysis, clustering method, association rule mining, classifier, latent Dirichlet allocation and the Kano model; they only identified a limited set of customer needs based on features of a product.…”

Section: Introductionmentioning

confidence: 99%

“…Needs statements must contain a metric (time, likelihood, number) so performance can be measured. 10. Examples added to the end of a statement for purposes of clarification must be similarly and consistently formatted.…”

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Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development

Joung

Jung

Ko³

et al. 2018

Sustainability

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The rapid increase in the quantity of customer data has promoted the necessity to analyse these data. Recent progress in text mining has enabled analysis of unstructured text data such as customer suggestions, customer complaints and customer feedback. Much research has been attempted to use insights gained from text mining to identify customer needs to guide development of market-oriented products. However, the previous research has a drawback that identifies limited customer needs based on product features. To overcome the limitation, this paper presents application of text mining analysis of customer complaints to identify customers' true needs by using the Outcome-Driven Innovation (ODI) method. This paper provides a method to analyse customer complaints by using the concept of job. The ODI-based analysis contributes to identification of customer latent needs during the pre-execution and post-execution steps of product use by customers that previous methods cannot discover. To explain how the proposed method can identify customer requirements, we present a case study of stand-type air conditioners. The analysis identified two needs that experts had not identified but regarded as important. This research helps to identify requirements of all the points at which customers want to obtain help from the product.

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Rough DT²R²ML for renewable energy supplier selection in the presence of big data

Fazlollahtabar

2023

Concurrency and Computation

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SummarySupplier selection is a substantial problem in supply chain management due to concurrent decision on key performance indicators on multi‐dimension data. Recently, more studies investigated the supplier section problem with respect to variety of criteria within the contexts of applied cases. The problem is more significant when industries with high amount of investment explore appropriate suppliers such as renewable energy. This article concerns with developing a new method encompass all indices effective on supplier selection. The proposed algorithm first group by decision tree (DT) indices to criteria and sub‐criteria; then, to include large amount of uncertain data, rough comparisons and weighing are fulfilled using a machine learning (RML). Further, transformation (T) to crisp value and ranking (R) of suppliers are delivered. The new method is implemented for a renewable energy supplier selection problem as a case study. The outputs show the effectiveness of the proposed method in practice since it can handle big data through a machine learning technique. Managerial implications as decision supports are discussed.

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Modeling of fuzzy-based voice of customer for business decision analytics

Cited by 55 publications

References 44 publications

A Design on Bank Customer Complaints Analysis using Natural Language Processing

A Design on Bank Customer Complaints Analysis using Natural Language Processing

Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development

Rough DT²R²ML for renewable energy supplier selection in the presence of big data

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Modeling of fuzzy-based voice of customer for business decision analytics

Cited by 55 publications

References 44 publications

A Design on Bank Customer Complaints Analysis using Natural Language Processing

A Design on Bank Customer Complaints Analysis using Natural Language Processing

Customer Complaints Analysis Using Text Mining and Outcome-Driven Innovation Method for Market-Oriented Product Development

Rough DT2R2ML for renewable energy supplier selection in the presence of big data

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Product

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Rough DT²R²ML for renewable energy supplier selection in the presence of big data