Validating Goal-Oriented Hypotheses of Business Problems Using Machine Learning: An Exploratory Study of Customer Churn

Supakkul, Sam; Ahn, Robert; Gonçalves, Ronaldo A. L.; Villarreal, Diana Carolina Treviño; Zhao, Liping; Hill, Tom; Chung, Lawrence

doi:10.1007/978-3-030-59612-5_11

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…4) must be always greater than 60%, if the variance is less than 35% then the data is meant as an unusable dataset. But if the variance is more than 60% then it means this has more complete original data [1].…”

Section: Methodsmentioning

confidence: 99%

“…Here the business intelligence platform was utilized for the analysis of their "multi-work term agreement with one employer" and customer segmentation. These procedures resulted in betterment in customer satisfaction [1]. • Xerox: Xerox established their own Knowledge Management solution termed as Eureka.…”

Section: Real Time Applications Of Business Intelligence and Knowledg...mentioning

confidence: 99%

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Role of Business Intelligence and Knowledge Management in Solving Business Problems

Qhal

2022

Teh. glas. (Online)

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The term “Business intelligence” is described as a plan or a strategy where the operations like reporting, data analysis, data mining, event processing are performed to improve the production and growth of a business enterprise or a business entity. And on the other hand, the “Knowledge management” is explained as well-organized management of resources and information within a commercial organization it can be a business too. Almost all business will have limitations and challenges which can be also known as the business problems. One of the main business problem is demand, the business plans must work according to the demand of the consumers. Analyzing the demand would provide the solutions for queries like what is the business trend? What is the need of the users? What should be the improvement make in the production? Where is the current position of the enterprise? And who all will be the competitors? For the predictive analysis a dataset of bitcoin is taken. The major aim of the study is to implement the strategies to overcome the business problems mainly the demand prediction. And the objective is to find out the relevant issues and the remedies by using knowledge management and business intelligence to the common business problems. The dataset has columns called lowest price, highest price, open price, close price, trading volume and market capital. The research methodology used is predictive analysis using PCA and K-means clustering algorithm. By this dataset predictive plots are developed as achieved results for easy analysis by using research methodology. PCA and K-means are the algorithm used for accurate prediction. The importance of study is to predict the future sale, as it is very essential for a business enterprise to find future demand so that the organization can improve production.

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Section: Methodsmentioning

confidence: 99%

Section: Real Time Applications Of Business Intelligence and Knowledg...mentioning

confidence: 99%

Role of Business Intelligence and Knowledge Management in Solving Business Problems

Qhal

2022

Teh. glas. (Online)

View full text Add to dashboard Cite

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“…Drawing on our previous work, GOMA [8] and Metis [9], this paper presents the GOMPHY, a Goal-Oriented and Machine learning-based approach using a Problem HYpothesis, to help validate business problems [10,11]. Four technical contributions are made in this paper.…”

Section: Introductionmentioning

confidence: 99%

Validating Business Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach

Ahn

Supakkul

Zhao

et al. 2022

Big Data – BigData 2021

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Validating an elicited problem to hinder a business goal is often more important than finding solutions in general. For example, validating the impact of a client's account balance toward an unpaid loan would be critical as a bank can take some actions to mitigate the problem. However, business organizations face difficulties confirming whether some business events or phenomena are causing a problem against a business goal. Some challenges to validate a problem are identifying testable factors for the identified problem, preparing data to validate, analyzing relationships between the factors and a problem, and reasoning the relationships towards high-level problems. Information systems developed to solve unconfirmed problems frequently tackle an erroneous problem, leading to some dissatisfying systems, consequently not achieving business goals. This paper proposes a goal-oriented and machine learningbased approach, Gomphy, for validating a business problem. The Gomphy presents an ontology and a process, a problem-related entity modeling method to identify relevant data features, a data preparation method, and an evaluation method of a problem for high-level problems. To illustrate our approach, we have validated problems behind an unpaid loan in one bank as an empirical study. We feel that at least the proposed approach helps validate business events negatively contributing to a goal, giving some insights about the validated problem.

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“…Drawing on our previous work, GOMA [6] and Metis [7], we present a goal-oriented data preparation approach, DREGON(Data pREparation using GOal-orieNtation) to support business problem validation. Four technical contributions are made in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Goal-Oriented Approach for Preparing a Machine-Learning Dataset to Support Business Problem Validation

Ahn

Supakkul

Zhao

et al. 2021

2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Preparing a dataset representing business problems is an essential task in Machine Learning (ML). A suitable dataset is critical to accurate ML algorithms, which helps validate business problems. For example, preparing a dataset for predicting loan default in one bank would be vital in the ML project as bank staff may take some actions to mitigate the problem. However, preparing a dataset for identifying potential business problems is challenging. Some challenges might include determining possible events leading to problems, identifying testable factors of the events, and mapping a testable factor to data features to extract relevant data from source data. ML models using irrelevant or unimportant data may give incorrect predictions, negatively impacting problem validation, consequently not solving business problems. We present a goal-oriented approach for preparing an ML dataset to address this challenge. The approach provides an ontology and a process for guiding data preparation. In addition, it helps capture problematic business events, refine a business event to find a testable factor, map a testable factor to a database entity and features, and extract data from a database or Big data. We illustrate the approach using a retail banking application and a Financial database. The experimental results, we believe at least, show that the approach supports preparing a relevant ML dataset, helping validate business problems.

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Validating Goal-Oriented Hypotheses of Business Problems Using Machine Learning: An Exploratory Study of Customer Churn

Cited by 4 publications

References 10 publications

Role of Business Intelligence and Knowledge Management in Solving Business Problems

Role of Business Intelligence and Knowledge Management in Solving Business Problems

Validating Business Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach

A Goal-Oriented Approach for Preparing a Machine-Learning Dataset to Support Business Problem Validation

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