A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153965/1/A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems.pd

A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality

Analyzing user reviews of messaging Apps for competitive analysis

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe rise of various messaging apps has resulted in intensively fierce competition, and the era of Web 2.0 enables business managers to gain competitive intelligence from user-generated content (UGC). Text-mining UGC for competitive intelligence has been drawing great interest of researchers. However, relevant studies mostly focus on industries such as hospitality and products, and few studies applied such techniques to effectively perform competitive analysis for messaging apps. Here, we conducted a competitive analysis based on topic modeling and sentiment analysis by text-mining 27,479 user reviews of four iOS messaging apps, namely Messenger, WhatsApp, Signal and Telegram. The results show that the performance of topic modeling and sentiment analysis is encouraging, and that a combination of the extracted app aspect-based topics and the adjusted sentiment scores can effectively reveal meaningful competitive insights into user concerns, competitive strengths and weaknesses as well as changes of user sentiments over time. We anticipate that this study will not only advance the existing literature on competitive analysis using text mining techniques for messaging apps but also help existing players and new entrants in the market to sharpen their competitive edge by better understanding their user needs and the industry trends

The most frequently used vocabulary in the minutes of the monetary policy committee of the Central Bank of Brazil : a text mining approach

Transparent and clear communication from the Central Bank of Brazil (BCB) is important to ensure that monetary policy is applied more effectively. Economic Agents keenly await the Minutes released by the BCB's Monetary Policy Committee (COPOM), because these anchor their expectations and enable them to take decisions based on their interpretation of the terms used by the monetary authority. The text of COPOM's Minutes is unstructured data that can be analyzed using Text Mining. This method can reveal new knowledge through the automated computerized extraction of information from a corpus of documents. The aim of this study is to identify the most frequently used vocabulary in BCB’s communications in Portuguese using Text Mining. For this, 190 of COPOM's Minutes were collected and made available on the BCB website in HTML format. A Topic Modeling algorithm based on the Latent Dirichlet Allocation (LDA) statistical model was applied to this set of documents and used to construct a vocabulary and discover unobservable topics. There were more than 2.5 million alphanumeric terms, repeated or otherwise, and from this a vocabulary with 10,281 words was identified. Only one topic best fit the LDA model. The most common words found in this single topic were related to inflation and economic activity, which are important aspects for decision-making on the Selic Rate. This work is of practical importance, as there is no record of any other dictionary of this type in Portuguese, which makes it unique, but not definitive.A transparência e clareza na comunicação do Banco Central do Brasil (BCB) é importante para uma maior eficiência na execução da política monetária. As Atas divulgadas pelo Comitê de Política Monetária (COPOM) do BCB são bastante aguardadas pelos agentes econômicos, uma vez que eles podem ancorar suas expectativas e tomar decisões a partir da interpretação dos termos empregados pela autoridade monetária. Os textos contidos nas Atas do COPOM são dados não estruturados que podem ser analisados através da técnica de Text Mining. Este método revela novos conhecimentos através da extração automática computadorizada de informações de um corpus de documentos. O objetivo deste estudo é identificar o vocabulário mais frequente na comunicação do BCB em português utilizando a abordagem de Text Mining. Para isso, foram coletadas 190 Atas do COPOM disponibilizadas no site do BCB no formato HTML. Neste conjunto de documentos, foi aplicado um algoritmo de Topic Modelling baseado no modelo estatístico Latent Dirichlet Allocation (LDA) para construção do vocabulário e descoberta dos tópicos não observáveis. Em um universo de mais de 2.5 milhões de termos alfanuméricos, repetidos ou não, foi encontrado um vocabulário com 10.281 palavras. O número de tópicos que melhor se ajusta ao modelo LDA é igual a 1. As palavras mais frequentes encontradas neste único tópico estão relacionadas com a inflação e atividade econômica, aspectos importantes para a tomada de decisão referente à Taxa Selic. Este trabalho possui importância prática, uma vez que não há registros de um outro dicionário deste tipo em língua portuguesa, o que o torna único, porém não definitivo

A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector embedding-based keyword extraction, and clustering. The elements of our model have been integrated and further developed to meet better the requirements of efficient information extraction, topic modeling of the extracted information, and user needs. Furthermore, our system can achieve better results than this task's existing topic modeling and keyword extraction solutions. Our approach is validated and compared with other state-of-the-art methods using publicly available datasets for benchmarking

Business Value of Making Managerial Responses: A Literature Review and Agenda for Future Research

Along with the prevalence of online customer reviews, a growing body of academic research has investigated the business value of adopting managerial response (MR) service, considering its capability to engage customers proactively. However, conflicting findings have been widely reported on the effect of MR usage in past studies. By synthesizing extant research on the topic, this literature review explicated the reported mechanism of how MR affect business performance and deciphered the causes of contradicting results reported in the extant literature, aiming at offering an agenda for future research. As a result, the study facilitates a more complete understanding on the state-of-art in MR research, which presents the key issues in current and emerging literature and offers a useful reference for the future advance in this field

Using Text Analytics to Derive Customer Service Management Benefits from Unstructured Data

Deriving value from structured data is now commonplace. The value of unstructured textual data, however, remains mostly untapped and often unrecognized. This article describes the text analytics journeys of three organizations in the customer service management area. Based on their experiences, we provide four lessons that can guide other organizations as they embark on their text analytics journeys.Click here for podcast summary (mp3)Click here for free 2-page executive summary (pdf)Click here for free presentation slides (pptx

Parenting, Vaccines, and COVID-19: A Machine-Learning Approach

COVID-19 is currently at the forefront of both out-of-school time program providers’ and parents’ minds, with additional policies and procedures added existing operating standards to protect the health of participants, staff, and parents (Environmental Health & Engineering, 2020). A failure to adequately prepare and react to different parenting styles may have both operational and financial implications for out-of-school time programs. These implications are only further exacerbated in the additional context of a global pandemic. While the COVID-19 vaccine is a hope to many that the end of the pandemic is near, parental vaccine hesitancy or refusal may pose a significant hurdle to the safe operation of out-of-school time programs. By exploring the topics of vaccine hesitancy, children, and parents in an online environment, this study offers a closer look into a digital leisure space. In order to better explore the conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19, web-scraping technologies were employed to aid in a more robust data collection. Due to the nature of web-scraped data as large in size and unruly, a machine learning method was used to analyze the data: Latent Dirichlet Allocation (i.e., LDA), a specific form of topic modelling. After establishing model parameters for the LDA, 25 latent topics were identified from the cleaned dataset (N = 31,925). These 25 topics were subsequently sorted into seven categories: Government, Feelings, School, Public Health, Christmas, Risk & Safety, and Parents & Families. Interpretation of the 25 latent topics was aided by a visualization of the top words most relevant to individual topics, in context to the overall dataset. Representative tweets from each category further identified the range of conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19. Challenges with research at the cusp of innovation for leisure sciences, as well as implications of practice for out-of-school-time professionals, are also discussed

The impact of online reputation on hotel profitability

Purpose: The purpose of this study is to quantify the impact of online customer reputation on financial profitability. Design/methodology/approach: Online reputation is captured by extracting the most recurring textual themes associated with customer satisfaction and dissatisfaction, expressed within positive vs negative online guest reviews on Booking.com. Latent semantic analysis is used for textual analysis. Proxies of overall financial performance are manually constructed for the sample hotels, using financial data from the Financial Analysis Made Easy (FAME) database. Ordinary least squares is used to gauge the effect of online customer reputation on financial profitability. Findings: Empirical findings indicate that recurring textual themes from positive online reviews (in contrast to negative reviews) exhibit a higher degree of homogeneity and consensus. The themes repeated in positive, but not in negative reviews, are found to significantly associate with hotel financial performance. Results contribute to the discussion about the measurable effect of online reputation on financial performance. Originality/value: Contemporary quantitative methods are used to extract online reputation for a sample of UK hotels and associate this reputation with bottom-line financial profitability. The relationship between online reputation, as manifested within hotel guest reviews, and the financial performance of hotels is examined. Financial profitability is the result of revenues, reduced by the costs incurred in order to be able to offer a given level of service. Previous studies have mainly focused on basic measures of performance, i.e. revenue generation, rather than bottom-line profitability. By combining online guest reviews from travel websites (Booking.com) with financial measures of enterprise performance (FAME), this study makes a meaningful contribution to the strategic management of hotel businesses

Job Satisfaction and Employee Turnover Determinants in Fortune 50 Companies: Insights from Employee Reviews from Indeed.com

We explored 682176 employee reviews of Fortune 50 companies from Indeed.com using topic discovery techniques like Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) to identify salient aspects in employee reviews and automatically infer latent topics that tend to drive employee satisfaction. We also studied how various satisfaction factors could be related to employee turnover. We discovered important topics in the reviews, including Management and Leadership, Advancement Opportunity, Pay and Benefits, Work-Life Balance, and Culture, which we compare to the five Job Descriptive Index (JDI) facets. Both LDA and STM discovered well-separated and distinguishable topics. We also incorporated a “Job Status” covariate in STM, which helped distinguish between what topics were talked about most by “Former” vs “Current” employees, and consequently helped us analyze the factors that could have caused employee turnover. We found that Leadership and Management and Overwork and Stressful Environment were the dominant factors contrasting between former and current employees, suggesting that they might be a leading cause of employee turnover. Furthermore, we post-processed the topic probability result from the STM model and analyzed it to determine sector-wise topic contribution for each topic, and also analyzed the company-wise topic contribution in each sector. We found that Retail sectors talked the most about Pay and Benefits and Length of Breaks, whereas the Technology sector’s employees were more concerned about the Work-Life Balance issue. Our results are directly usable to support company behavioral management decision makers to conceive and evaluate initiatives intended to enhance employee satisfaction. Furthermore, our techniques, including a novel visualization of topic composition and quality, are generalizable to any setting that uses topic discovery from unstructured text, and especially those comparing topics across entities