Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns

Ramsey, Gregory; Bapna, Sanjay

doi:10.4018/ijban.2016040102

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…Among other capabilities, text analytics analyzes a document's linguistic structures to extract a wide range of content, including dates, places, companies, concepts, or topics (Valacich & Schneider, 2012). By isolating critical pieces of information, text analytics can determine the general meaning of the text which can be used to assist decision making in various application domains (Ramsey & Bapna, 2016). Specific areas of application include information extraction, topic models, question answering, and opinion mining (Chen, Chiang, & Storey, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Huizinga

Ayanso

Smoor

et al. 2017

International Journal of Business Analytics

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This study explores twitter data about insurance and natural disasters to gain business insights using text analytics. The program R was used to obtain tweets that included the word ‘insurance' in combination with other natural disaster words (e.g., snow, ice, flood, etc.). Tweets related to six top Canadian insurance companies as well as the top five insurance companies from the rest of the world, including the new entrant Google Insurance, was collected for this study. A total of 11,495 natural disaster tweets and 19,318 insurance company tweets were analyzed using association rule mining. The authors' analysis identified several strong rules that have implications for insurance products and services. These findings show the potential text mining applications offer for insurance companies in designing their products and services.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Huizinga

Ayanso

Smoor

et al. 2017

International Journal of Business Analytics

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show abstract

“…DDE executes primarily as a background process. Gregory et al [9] they have reported on the results of extracting useful information from text notes captured within a Customer Relationship Management (CRM) system to segment and thus target groups of customers likely to respond to cross-selling campaigns. These notes often contain text that is indicative of customer intentions.…”

Section: Related Workmentioning

confidence: 99%

An Efficient String Matching Technique for Desktop Search to Detect Duplicate Files

Vijayarani¹,

Muthulakshmi²

2017

IJITCS

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Information retrieval is used to identify the relevant documents in a document collection, which is matching a user"s query. It also refers to the automatic retrieval of documents from the large document corpus. The most important application of information retrieval system is search engine like Google, which identify those documents on the World Wide Web that are relevant to user queries. In most situations, users may download the files that are already downloaded and stored in their computer. Then, there is a chance of multiple copies of the files that are already stored in different drives and folders on the system, which in turn reduces the performance of the system and these files occupy a lot of memory space. Analyzing the contents of the file and finding their similarity is one of the major problems in text mining and information retrieval. The main objective of this research work is to analyze the file contents and deletes the duplicate files in the system. In order to perform this task, this research work proposes a new tool named Duplicate File Detector Tool i.e. DFDT. DFDT helps the user to search and delete duplicate files in the system at a minimum time. It also helps to delete the duplicate files not only with the same file category, but also with different file categories. Boyer Moore Horspool and Knuth Morris Pratt string searching algorithms are existing algorithms and these algorithms are used to compare the file contents for finding their similarity. This work also proposes a new string matching algorithm named as W2COM (Word to Word COMparison). From the experimental results it is observed that the newly proposed W2COM string matching algorithm performance is better than Boyer Moore Horspool and Knuth Morris Pratt algorithms.

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“…Content mining also enables researchers to capture a large number of web resources (e.g., consumer reviews and comments) efficiently and the text resources are from real consumers. From previous studies, this method was used in the manufacturing sector to facilitate mass customization (e.g., Zhou & Ji, 2015) and in exploring individual customers' probability of responding to a campaign (e.g., Ramsey & Bapna, 2016). However, to date, this effective approach has not been used to excavate and examine comments from consumers who have had real experiences in menswear customization.…”

Section: Introductionmentioning

confidence: 99%

Fashion or Function

Liang

Rangineni

Liu

2022

International Journal of Business Analytics

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Mass customization has been used in various clothing products including T-shirts, dress shirts, jeans, sports shoes, accessories, etc. However, even though the concept of mass customization has been accepted among scholars and business practitioners, and the application of mass customization has been practiced in the fashion industry for more than two decades, the acceptance among ultimate consumers and the market of MC in the apparel industry has not taken off yet. To this end, this study intends to fill the identified gap through a content mining exploratory qualitative study using customer value theory to evaluate individual consumers' actual customization experiences. Results identified that functional value, self-expressive value, and aesthetic value were derived from menswear customization experiences. Among them, functional value (e.g., quality, fit) is the dominant dimension of value derived from menswear customization experiences, and it determined consumers' overall attitudes toward mass customization of menswear.

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Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns

Cited by 9 publications

References 37 publications

Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Exploring Insurance and Natural Disaster Tweets Using Text Analytics

An Efficient String Matching Technique for Desktop Search to Detect Duplicate Files

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