Influence of the Contact Center Systems Development on Key Performance Indicators

Płaza, Mirosław; Pawlik, Lukasz

doi:10.1109/access.2021.3066801

Cited by 15 publications

(9 citation statements)

References 31 publications

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“…In the initial phase, the signal recorded during the conversation between a client and an agent undergoes preprocessing tasks (1). These tasks are performed before the data is sent for further analysis by the embedded intelligent ASR systems (2). The selected ASR systems create transcripts of the conversation, which then go to the block that performs postprocessing algorithms (3).…”

Section: Transcription Methods For CC Systemsmentioning

confidence: 99%

“…e-mail, chat, and social media), traditional phone calls continue to be one of the dominant channels. The technological development of this area in the context of automation is particularly important because it has a significant impact on the quality of customer service and the costs incurred [2]. In recent years, intelligent virtual assistants taking the form of chat bots and voice bots have become very popular in CCs [3].…”

Section: Introductionmentioning

confidence: 99%

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Call Transcription Methodology for Contact Center Systems

Płaza

Pawlik²,

Deniziak

2021

IEEE Access

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Nowadays, one of the key areas of research on contact centre systems is their automation. The main element that influences the possibility of automation of contact centre processes is the call transcription methods implemented by automatic speech recognition (ASR) systems. Such systems enable developing intention recognition methods and, consequently, chat bots and voice bots. The current solutions used in ASR systems for many less popular languages do not guarantee a fully satisfactory transcription quality for hotline voice calls. This is due to the unique characteristics of the sound signal generated there, whose quality parameters differ significantly from those of studio recordings. The paper presents a comparative study of selected speech recognition systems that were additionally supplemented with elements of preprocessing of sound recordings and postprocessing of originally produced transcriptions. As for preprocessing, the following methods were tested: separation of the client and agent channels into two independent signals, training of ASR systems, and audio signal correction. With regards to postprocessing, on the other hand, tests were performed for inarticulate sounds, normalization of standard phrases (e.g. numbers, dates, times, etc.), and identification of close-sounding phrases and foreign language phrases, and lemmatization. Based on the research conducted and the analyses performed, a new method of call transcription intended specifically for contact center systems was proposed. The research conducted for this paper was based on the Polish language model, for which major problems are observed with the quality of automatic contact center call transcriptions.

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Section: Transcription Methods For CC Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Call Transcription Methodology for Contact Center Systems

Płaza

Pawlik²,

Deniziak

2021

IEEE Access

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“…Text mining has been used to analyze sentiment words from customer reviews for measuring the level of customer satisfaction [9] and [10]. Additionally, speech recognition techniques have been adopted to measure customer satisfaction [11]- [14]. Sun et al [13] used information fusion to analyze customer satisfaction.…”

Section: A Satisfactionmentioning

confidence: 99%

“…Park [10] employed text mining and statistical analysis to calculate sentiment scores for online customer reviews in order to measure customer satisfaction. Automatic Speech Recognition (ASR) is a technique that is primarily used for transcribing speech into spoken words, and the texts corresponding to these spoken words can then be analyzed through various analytical models to measure customer satisfaction [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Confirming Customer Satisfaction With Tones of Speech

Ko¹,

Hsu²,

Liu³

et al. 2022

IEEE Access

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As customer satisfaction explicitly leads to repurchase behavior, the level of customer satisfaction affects both sales performance and enterprise growth. Traditionally, measuring satisfaction requires customers spending extra time to fill out a post-purchase questionnaire survey. Recently, ASR (Automatic Speech Recognition) is utilized to extract spoken words from conversation to measure customer satisfaction. However, as oriental people tend to use vague words to express emotion, the approach has its limitation. To solve the problem, this study strived to complete following tasks: devising a process to collect customer voice expressing satisfaction and corresponding verifiable ground truth; a data bank of 150 customer voices speaking in Mandarin was collected; the features of the voice data were extracted with MFCC; as the size of data bank was limited, Auto Encoder was utilized to further reduce the features of voices; models based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) and Support Vector Machine (SVM) were constructed to predict satisfaction. With nested cross validation, the average accuracy of LSTM and SVM could reach 71.95% and 73.97%, respectively.

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“…For CC systems, limiting procedures to only identifying positive and negative affect significantly limits the ability to improve customer service. In contrast, accurately identifying customer emotion types can contribute to a significant reduction in service costs, as measured by selected KPIs (key performance indicators) [ 63 ]. KPIs that can be improved as a result of implementing a new emotion recognition procedure include, for example, improved C-SAT (customer satisfaction) or FCR (first call resolution), which measure the number of cases resolved during the customer’s first contact with the CC.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications

Płaza

Trusz

Kęczkowska

et al. 2022

Sensors

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Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).

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Influence of the Contact Center Systems Development on Key Performance Indicators

Cited by 15 publications

References 31 publications

Call Transcription Methodology for Contact Center Systems

Call Transcription Methodology for Contact Center Systems

Confirming Customer Satisfaction With Tones of Speech

Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications

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