Adapting the CRISP-DM Data Mining Process: A Case Study in the Financial Services Domain

Plotnikova, Veronika; Dumas, Marlon; Milani, Fredrik

doi:10.1007/978-3-030-75018-3_4

Cited by 8 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A clear description of the input data is vital for maintaining the analysis's validity and precision. The CRISP-DM framework, widely recognized in the industry for data mining, is often used to analyze and interpret consumer trends (Plotnikova et al 2021).…”

Section: Methodology and Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis

Pitka,

Bucko,

Krajči

et al. 2024

J Market Anal

View full text Add to dashboard Cite

Data analytics plays a significant role within the context of the digital business landscape, particularly concerning online sales, aiming to enhance understanding of customer behaviors in the online realm. We review the recent perspectives and empirical findings from several years of scholarly investigation. Furthermore, we propose combining computational methods to scrutinize online customer behavior. We apply the decision tree construction, GUHA (General Unary Hypotheses Automaton) association rules, and Formal concept analysis for the input dataset of 9123 orders (transactions) of sports nutrition, healthy foods, fitness clothing, and accessories. Data from 2014 to 2021, covering eight years, are employed. We present the empirical discoveries, engage in a critical discourse concerning these findings, and delineate the constraints inherent in the research process. The decision tree for classification of the year’s fourth quarter implies that the most important attributes are country, gross profit category, and delivery. The classification of the morning time implies that the most important attributes are gender and country. Thus, the potential marketing strategies can include heterogeneous conditions for men and women based on these findings. Analyzing the identified groups of customers by concept lattices and GUHA association rules can be valuable for targeted marketing, personalized recommendations, or understanding customer preferences.

show abstract

Section: Methodology and Dataset Descriptionmentioning

confidence: 99%

“…Acknowledging the significance of this phase is essential for successful research. Data understanding A crucial step in the CRISP-DM process is understanding the data (Plotnikova et al 2021). This phase is closely linked to the first stage, as the data's nature often sets the research's scope and possible findings.…”

Section: Methodology and Dataset Descriptionmentioning

confidence: 99%

Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis

Pitka,

Bucko,

Krajči

et al. 2024

J Market Anal

View full text Add to dashboard Cite

show abstract

“…Data mining is needed in the new quest to determine the concept of valuable results and non-trivial information in the volume of datasets to draw conclusions on data that has been formed, which is achieved in a balance from human knowledge to visualizing pictures of problems and specific goals assisted by computer search capabilities [17]. CRISP-DM is an acronym that comes from the term Cross-Industry Standard Process for Data Mining, which is a model that provides an overview of the life cycle of a data mining project consisting of 6 stages [18].…”

Section: Methodsmentioning

confidence: 99%

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Hasan

Aziz

Nofendri

2023

matrik

View full text Add to dashboard Cite

Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.

show abstract

“…Deployment is implementing new knowledge obtained from the CRISP-DM stages that have been passed (Plotnikova et al, 2021). Project Cycle Management is one method that can be implemented to produce a development framework consisting of five stages, namely initiation, identification, formulation, implementation, and evaluation and audit (Melecký and Staníčková 2018).…”

Section: Deploymentmentioning

confidence: 99%

Cross-Industry Standard Process for Data Mining (CRISP-DM) Approach in Determining the Most Significant Employee Engagement Drivers to Sales at X Car Dealership

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

A car dealership is a car sales business that sells cars and services, which is the benchmark for prospective buyers. Increasing sales within the company are needed in developing business through building loyalty and trust between buyers and businesses. Companies can thrive on innovating and being sustainable when sales are in good shape. This research was conducted to find the correlation between employee engagement and sales. It is focused on making good business decisions to increase sales. This study focuses on determining the driving factors for employee engagement that have a significant effect on sales, the magnitude of its influence on sales, and suggestions for increasing sales from employee engagement. This research was conducted through Multiple Linear Regression modeling with Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. All independent variables in all models, namely NPBT Sales, NPBT Service, Unit Sales, and Unit Entry, each have a level of determination coefficient of 0.6889, 0.4962, 0.6795, and 0.4968. All models can be explained by 68.89%, 49.62%, 67.95%, and 49.68%, respectively, by the independent variables of each model. Suggestions were also obtained as a framework for maximizing sales based on Project Cycle Management.

show abstract

Adapting the CRISP-DM Data Mining Process: A Case Study in the Financial Services Domain

Cited by 8 publications

References 7 publications

Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis

Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Cross-Industry Standard Process for Data Mining (CRISP-DM) Approach in Determining the Most Significant Employee Engagement Drivers to Sales at X Car Dealership

Contact Info

Product

Resources

About