52 research outputs found
Customer experience management: Expanding our understanding of the drivers and consequences of the customer experience
The present doctoral dissertation aims to analyze thenew business landscape that suggests the importance of customer experience Âż its drivers and consequences from a dynamic perspective. The drivers of customer experience provide firms with crucial knowledge about the experience expectations and desires of the customers, thereby enabling firms to identify the key determinants which significantly shape customer perceptions toward the experience with the firm. This is very important for firms, since the effort dedicated by firms to improve customer experience is not always equally perceived and/or valued by customers. Likewise, integrating the consequences of customer experience allows firms to translate their investment in customer experience into specific opportunities and enhanced performance outcomes (financial, behavioral, and relational). This is specifically critical, considering that a customer experience perceived as favorable by customers might not have a positive impact on firm outcomes. Customer experience is not static but evolve over time. By taking into account the dynamic nature of customer experience, firms may capture the occurred changes in customers and adjust the factors under their controls immediately, thereby ensuring the alignment between customer experience expectations and firmsÂż offerings. In this way, through a dynamic lens, we establish the linkage across what firms do, what customers think, what customers do, and finally what firms get. The thesis is consisted of three studies. Study 1 investigates the impact of firmsÂż investments in three key strategic levers (i.e., value, the brand, and the relationship) on the customer experience as well as the direct and moderating role played by social influence. We integrate research in customer relationship management (i.e., customer equity framework) (Rust, Lemon, & Zeithaml, 2004) and customer experience management (Lemon & Verhoef, 2016; Verhoef et al., 2009) and offer a unifying framework to understand the linkages between the three equity drivers (i.e., value equity, brand equity, relationship equity), social influence, the customer experience, and its ultimate impact on profitability. Study 2 focuses on the separate and joint effects of customer experience and lock-in on customer retention. Building barriers to lock customers and improving the customer experience are two key strategies employed by firms to enhance customer retention. Although pursuing the same goal, these strategies work differently: the former relies more on a calculative, costÂżbenefit approach to the exchange, while the latter promotes the affective aspects of the relationship. Finally, study 3 investigates how different dimensions of customer experience (recency effect, peak effect, trend effect, and fluctuation effect) and different relationship marketing (RM) actions (i.e., advertising communication, product innovation, and conflict) impact customer relationship expansion from a dynamic perspective, and distinguishes their short-term and long-term effects. Self-determination theory posits that motivation for pursuing activities are consisted of intrinsic (the ones originating from the self and oneÂżs desire) and extrinsic factors (originating from external demands).<br /
Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?
This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation
Essays in Applied Bayesian Analysis
With continuing rapid developments in computational power, Bayesian statistical methods, because of their user-friendliness and estimation capabilities, have become increasingly popular in a considerable variety of application fields. In this thesis, applied Bayesian methodological topics and empirical examples focusing on nonhomogeneous hidden Markov models (NHMMs) and measurement error models are explored in three chapters. In the first chapter, a subsequence-based variational Bayesian inference framework for NHMMs is proposed in order to address the computational problems encountered when analyzing datasets containing long sequences. The second chapter concentrates on measurement error models, where a Bayesian estimation procedure is proposed for the partial potential impact fraction (pPIF) with the presence of measurement error. The third chapter focuses on an empirical application in marketing, where a coupled nonhomogeneous hidden Markov model (CNHMM) is introduced to provide a novel framework for customer relationship management
Strategic business models of platform providers in the video gaming industry - cloud gaming customer segmentation analysis
This work project examines the evolution and variation of business models within the
online video gaming platform industry. In the present paper, an in-depth analysis of the recent
business model of cloud gaming is provided. With large gaming audiences that differ in needs and
preferences, segmentation should occur based on gamer type. The analysis reveals a large potential,
especially within the non-gamer and casual gamer segments. Further, advantages and weaknesses
of the business model are outlined, and overriding recommendations are presented to strategically
optimize it. To conclude, key insights and limitations as well as future research are discussed
Modelling customer behaviour in contractual settings
The objective of this dissertation is to develop models of customer behavior that provide insights for firms working in contractual settings. A contractual setting is a business situation in which the time when a customer becomes inactive is perfectly observable. This is in contrast to noncontractual settings (e.g., Reinartz and Kumar 2000), where a company does not observe whether or not a customer is still active. In the first chapter we elaborate on this definition, providing a classification of contractual businesses depending on the nature of the transactions. In the second chapter we develop a joint model to forecast renewal and usage behaviors simultaneously. The models previously proposed for contractual settings primarily focus on predicting churn, while they are silent about how to predict usage behavior. In order to fill this gap, we develop a joint model of churn and usage behavior under the assumption that both are driven by the same underlying process (e.g., commitment). This enables the model to predict usage (thus contribution) and retention simultaneously and accurately. Besides its methodological contribution, this study has important managerial implications for customer base analysis, and lifetime value calculations. In chapter 3 we focus our attention on understanding customers’ behavior when subscribed to multi-priced contracts. A growing body of work about two- and three-part tariffs has recently emerged in the literature. However, even though three-part tariff contracts are now being offered in many business settings, there is no research investigating the effect of these pricing practices on customer switching and usage behavior. We explore this issue in Chapter 3. Given a unique data set from a mobile telephony operator, we investigate customer switching between two- and three-part tariffs as well as customers’ usage behavior under the two types of tariffs. We measure how an individual’s usage behavior is affected by changes in the tariff mechanism, and how the launch of the new tariffs impacts the firm’s revenue. The results of this analysis have important managerial implications for pricing and tariff design. Finally, we look at a customer relationship setting unstudied in the marketing literature: prepaid contracts. Prepaid is a big phenomenon in the market place. For many people, daily activities/services are prepaid; from commodities (transit commute, mobile phone, gas, etc.) to leisure products (Starbucks, Borders, etc.). Despite the popularity of this practice, we find that there is basically no work in the marketing and related literatures that explores this issue. Moreover, once we start exploring the notion of prepaid, we see references to gift cards, prepaid credit cards, transit cards, and various other forms of prepaid, though it is not clear how all these prepaid services are related. In Chapter 4 we review the various prepaid services and provide a framework to classify all these business settings. In particular, we uniquely characterize the prepaid mobile phone setting and identify several business issues that practitioners face in this market. Finally, we discuss the modeling challenges that researchers will face when trying to solve these business issues
A Comprehensive Survey on Rare Event Prediction
Rare event prediction involves identifying and forecasting events with a low
probability using machine learning and data analysis. Due to the imbalanced
data distributions, where the frequency of common events vastly outweighs that
of rare events, it requires using specialized methods within each step of the
machine learning pipeline, i.e., from data processing to algorithms to
evaluation protocols. Predicting the occurrences of rare events is important
for real-world applications, such as Industry 4.0, and is an active research
area in statistical and machine learning. This paper comprehensively reviews
the current approaches for rare event prediction along four dimensions: rare
event data, data processing, algorithmic approaches, and evaluation approaches.
Specifically, we consider 73 datasets from different modalities (i.e.,
numerical, image, text, and audio), four major categories of data processing,
five major algorithmic groupings, and two broader evaluation approaches. This
paper aims to identify gaps in the current literature and highlight the
challenges of predicting rare events. It also suggests potential research
directions, which can help guide practitioners and researchers.Comment: 44 page
Radial Basis Function Neural Network in Identifying The Types of Mangoes
Mango (Mangifera Indica L) is part of a fruit
plant species that have different color and texture
characteristics to indicate its type. The identification of the
types of mangoes uses the manual method through direct visual
observation of mangoes to be classified. At the same time, the
more subjective way humans work causes differences in their
determination. Therefore in the use of information technology,
it is possible to classify mangoes based on their texture using a
computerized system. In its completion, the acquisition process
is using the camera as an image processing instrument of the
recorded images. To determine the pattern of mango data
taken from several samples of texture features using Gabor
filters from various types of mangoes and the value of the
feature extraction results through artificial neural networks
(ANN). Using the Radial Base Function method, which
produces weight values, is then used as a process for classifying
types of mangoes. The accuracy of the test results obtained
from the use of extraction methods and existing learning
methods is 100%
Deep Learning Detected Nutrient Deficiency in Chili Plant
Chili is a staple commodity that also affects the Indonesian economy due to high market demand.
Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One
factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning
Technology in agriculture to help farmers be able to diagnose their plants, so that their plants
are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270
datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency,
Indonesia. The chili we use are curly chili. The results of this study are computers that can
recognize nutrient deficiencies in chili plants based on image input received with the greatest
testing accuracy of 82.61% and has the best mAP value of 15.57%
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