A methodology to map customer complaints and measure customer satisfaction and loyalty

Faed, Alireza; Hussain, Omar Khadeer; Chang, Elizabeth

doi:10.1007/s11761-013-0142-6

Cited by 23 publications

(14 citation statements)

References 36 publications

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“…For this, we have leveraged on the predictive power of machine learning models and the use of real historic data from a bike sharing system. The module employs artificial neural networks, which have been extensively employed in other prediction tasks [26][27][28]. Particularly, in our system, the predictions of this module are employed to feed the persuasive system, which will recommend trips to users based on the predicted state for the network of stations and the users' preferences.…”

Section: Station Status Forecastingmentioning

confidence: 99%

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

et al. 2019

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This work proposes a persuasion model based on argumentation theory and users’ characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.

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Section: Station Status Forecastingmentioning

confidence: 99%

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

et al. 2019

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“…Paper [15] proposed an interesting method based on nonlinear modeling and application of a fuzzy system to han-dle customer complaints, namely, the approach of an adaptive neuro-fuzzy output system (ANFIS). Authors figured out the relationship between customer satisfaction and the main relevant variables in transport and logistics companies in ports.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…An overview of the most common models and methods for measurement of the consumer loyalty [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] showed that these models do not take into account different directions of its formation; and they are too generalized in most cases. In addition, their basis is various traditional statistical methods of analysis, such as factor analysis, regression analysis, least squares, structural equations, and others, that is, they describe the existing level of information uncertainty inadequately.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Using the elements from a fuzzy sets theory in the process of diagnosing the loyalty of consumers of motor transport services

Fedotova¹,

Kryvoruchko²,

Shynkarenko³

et al. 2019

EEJET

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“…Applies the DEA technique for the location of a remanufacturing plant in a network with reverse logistics, thus minimizing costs [34]. And, it uses the DEA technique to identify and capture the key customers of a company, and identify and measure strengths and weaknesses in customer service [12].…”

Section: Relative_efficiency = Weihted_average_of_outputs (2) Weihtedmentioning

confidence: 99%

Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics

Gamboa

Riveros

2018

INGE CUC

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Introduction− Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia. Objective− The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics. Methodology− A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI's for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system are analyzed to find options for improving the system. Conclusions− Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network. Para citar este artículo: C. Ardila Gamboa y F. Ballesteros Riveros, "Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with reverse logistics"

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A methodology to map customer complaints and measure customer satisfaction and loyalty

Cited by 23 publications

References 36 publications

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

Using the elements from a fuzzy sets theory in the process of diagnosing the loyalty of consumers of motor transport services

Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics

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