Retail managers’ preparedness to capture customers’ emotions: a new synergistic framework to exploit unstructured data with new analytics

Although emotions have been investigated within strategic management literature from an internal perspective, managers’ ability and willingness to understand consumers’ emotions, with emphasis on the retail sector, is still a scarcely explored theme in management research. The aim of this paper is to explore the match between the supply of new analytical tools and retail managers’ attitudes towards new tools to capture customers’ emotions. To this end, Study 1 uses machine learning algorithms to develop a new system to analytically detect emotional responses from customers’ static images (considering the exemplar emotions of happiness and sadness), whilst Study 2 consults management decision-makers to explore the practical utility of such emotion recognition systems, finding a likely demand for a number of applications, albeit tempered by concern for ethical issues. While contributing to the retail management literature with regard to customers’ emotions and big data analytics, the findings also provide a new framework to support retail managers in using new analytics to survive and thrive in difficult times

More than a feeling? Toward a theory of customer delight

PurposeResponding to an increasing call for a more comprehensive conceptualization of customer delight, the purpose of this paper is to expand the theory of customer delight and to examine the implications of such an expanded view for service theory and practice.Design/methodology/approachThis paper presents the results of three qualitative studies. The first study explores customer delight through self-reported consumption experiences in customer-selected contexts, followed by one-on-one in-depth interviews. The second involves focus groups and the third examines self-reported incidents of delightful customer experiences.FindingsThis research finds that customer delight goes beyond extreme satisfaction and joy and surprise to include six properties that—individually or in combination—characterize customer delight. An expanded conceptualization of how customer delight can be defined is proposed in which customer delight is associated with various combinations of six properties – the customer experiencing positive emotions, interacting with others, successful problem-solving, engaging customer’s senses, timing of the events and sense of control that characterizes the customer's encounter.Research limitations/implicationsIt is clear from the findings of this research that there is no single property that is associated with delight. Through the facilitation of multiple properties, managers have the potential to create a multitude of routes to delight. It is recommended that future research (1) identify and explicate these alternative routes for engendering delight using the six properties identified, and (2) develop a general typology based on service context and characteristics, customer segment, etc. that further stimulates scholarship on delight, and offers more industry-specific insights for managers.Practical implicationsInsights from this investigation will encourage managers and service designers to think more broadly and creatively about delight. Doing so will open up new opportunities for achieving customer delight, beyond merely focusing on extreme satisfaction or surprise and joy strategies currently dominating discussions of customer delight.Originality/valueThis paper makes several contributions to the service literature. First, it extends current conceptualizations of customer delight and offers an expanded definition. Next, it demonstrates how this new understanding extends the existing literature on delight. Finally, it proposes an agenda for future delight research and discusses managerial implications, opening up new opportunities for firms to design delightful customer experiences.</jats:sec

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

The use of intellectual capital information by sell-side analysts in company valuation

This paper investigates the role of intellectual capital information (ICI) in sell-side analysts’ fundamental analysis and valuation of companies. Using in-depth semi-structured interviews, it penetrates the black box of analysts’ valuation decision-making by identifying and conceptualising the mechanisms and rationales by which ICI is integrated within their valuation decision processes. We find that capital market participants are not ambivalent to ICI, and ICI is used: (1) to form analysts’ perceptions of the overall quality, strengths and future prospects of companies; (2) in deriving valuation model inputs; (3) in setting price targets and making investment recommendations; and (4) as an important and integral element in analyst–client communications. We show that: there is a ‘pecking order’ of mechanisms for incorporating ICI in valuations, based on quantifiability; IC valuation is grounded in valuation theory; there are designated entry points in the valuation process for ICI; and a number of factors affect analysts’ ICI use in valuation. We also identify a need to redefine ‘value-relevant’ ICI to include non-price-sensitive information; acknowledge the boundedness and contextuality of analysts’ rationality and motives of their ICI use; and the important role of analyst–client meetings for ICI communication

A real-time assessment of customer experience

We have now entered into the age of the customer, a time where competing through traditional methods is no longer valid; it is the customer experience that is crucial to gaining a competitive advantage. Despite the recognition of the importance of customer experience, there is a considerable dearth of knowledge among both academics and practitioners on aspects relating to customer experience including the drivers, measurement and value it provides to firms. In an attempt to move forward the body of knowledge on customer experience, the researcher set out to answer the following research question: what is the impact of customer experience on customer intentions and actual behaviour in multichannel retail and service settings? The research comprised two successive studies. Study 1 was conducted to identify the elements that encompass customer experience touch points. The study was based on a qualitative research approach, using a sequential incident technique to guide the data collection. A total of 28 customer experience narratives provided by 22 informants was collected through semi-structured interviews. An inductive thematic analysis of the semi-structure interview transcripts was employed to identify distinct elements of customer experience touch points; elements to be used to develop a holistic model of customer experience in Study 2. Study 2 was conducted to investigate empirically the real-time impact of customer experience on customer intentions and actual behaviour in multichannel retail and service settings

Speech and natural language processing for the assessment of customer satisfaction and neuro-degenerative diseases

ABSTRACT: Nowadays, the interest in the automatic analysis of speech and text in different scenarios have been increasing. Currently, acoustic analysis is frequently used to extract non-verbal information related to para-linguistic aspects such as articulation and prosody. The linguistic analysis focuses on capturing verbal information from written sources, which can be suitable to evaluate customer satisfaction, or in health-care applications to assess the state of patients under depression or other cognitive states. In the case of call-centers many of the speech recordings collected are related to the opinion of the customers in different industry sectors. Only a small proportion of these calls are evaluated, whereby these processes can be automated using acoustic and linguistic analysis. In the assessment of neuro-degenerative diseases such as Alzheimer's Disease (AD) and Parkinson's Disease (PD), the symptoms are progressive, directly linked to dementia, cognitive decline, and motor impairments. This implies a continuous evaluation of the neurological state since the patients become dependent and need intensive care, showing a decrease of the ability from individual activities of daily life. This thesis proposes methodologies for acoustic and linguistic analyses in different scenarios related to customer satisfaction, cognitive disorders in AD, and depression in PD. The experiments include the evaluation of customer satisfaction, the assessment of genetic AD, linguistic analysis to discriminate PD, depression assessment in PD, and user state modeling based on the arousal-plane for the evaluation of customer satisfaction, AD, and depression in PD. The acoustic features are mainly focused on articulation and prosody analyses, while linguistic features are based on natural language processing techniques. Deep learning approaches based on convolutional and recurrent neural networks are also considered in this thesis

The Role of Customer Emotions in a Call Center Setting

This thesis investigates the role of customer emotions in a call center setting. Three empirical studies are presented: 1. An exploratory qualitative study shows that emotions play an important role in a call center encounter. A measurement scale is developed using the negative emotions reported: frustration, anger, powerlessness, discouragement and stress, and positive emotions: happiness and relief. 2. A survey of 1440 customers of a French call center and subsequent Structural Equation Modeling shows that emotions have a significant effect on satisfaction and loyalty. 3. A follow-up qualitative study indicates that emotions change during the process of a call, and are accompanied by appraisals of reduced certainty and control. Frustration is shown to be a major customer emotional experience due to the set-up of call center services which generate reduced certainty and control, attributions of employee or company responsibility and appraisals of goal-blocking. The main theoretical contributions come from a) the finding that emotions have a stronger effect on loyalty than does satisfaction, b) empirical testing of a widely acknowledged (but not explicitly tested) notion that negative emotions decrease loyalty more than positive emotions increase it. In a call center context callers begin the call with some negative emotional ‘baggage’ which may enhance the effect on loyalty as compared to positive emotions, c) empirical support for Barrett et al. (2007) and Elfenbein (2007) who suggest that an emotional process characterizes emotional experience better than emotions alone. Managerial implications are a) that the call center industry needs to move away from its traditional performance-metrics basis for measuring customer satisfaction and take customer emotions into account, and b) that to ensure loyalty call center managers should focus more strongly on strategies for avoiding negative customer emotional states than for promoting positive emotions