Factores de éxito de un emprendimiento: Un estudio exploratorio con base en técnicas de data mining (Entrepreneurial success factors: An exploratory study based on Data Mining Techniques)

El Centro de Emprendedurismo CCEEmprende de- sarrolla, desde 2007, un programa de apoyo a emprende- dores. Para mejorar su gestión, resulta de gran importancia analizar, en forma preliminar, los emprendimientos en una de dos categorías: éxito o fracaso. En este artículo se identifican los principales factores asociados al éxito de un emprendimiento y cómo se vincu- lan para anticipar el futuro del emprendimiento. Se presenta un caso de estudio con base en los datos de una encuesta realizada a emprendedores participantes del programa, aplicando técnicas de clasificación. Las dos técnicas utilizadas de data mining son árbol de decisión y regresión logística, en ambas se obtuvieron resultados coincidentes. Los hallazgos muestran que los dos elementos más relevantes para anticipar el éxito de un emprendimiento son contar con financiamiento y que, anteriormente, la situa- ción laboral del emprendedor sea trabajador independiente. Estos primeros resultados obtenidos en el estudio de caso revelan información útil acerca de las mejores formas de apoyo al emprendedor, cómo generar incentivos al em- prendedor y la definición de herramientas o actividades que incidan favorablemente en el éxito de los emprendimientos. Si bien desde la teoría o para otras realidades existe infor- mación sobre los factores que colaboran en la determina- ción del éxito, para la realidad del Uruguay no se identifican estudios similares. Abstract Since 2007, the CCEE Entrepreneurship Centre has developed a supporting program for entrepreneurs. A preliminary analysis to determine if the venture was successful or a failure is made to improve the program’s management . In this article, the authors identify the main factors associated with entrepreneurship’s success, and how they can anticipate entrepreneurship’s performance. The case study is based on a survey data applied to the Entrepreneurship Program participants. The two data mining techniques are decision trees and logistic regression. The results were consistent across both tech- niques. The findings show that the two most important elements to predict entrepreneurship’s success are fun- ding and previous experience as self-employed. The results provided very useful insight about the best ways to support entrepreneurship, how to encoura- ge entrepreneurs, and define tools or activities to impact positively ventures success in Uruguay, since similar stu- dies have not been developed.

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Applying Data Mining Techniques to Identify Success Factors in Students Enrolled in Distance Learning: A Case Study

Advances in Artificial Intelligence and Its Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-27101-9_15 ◽

2015 ◽

pp. 208-219 ◽

Cited By ~ 3

Author(s):

José Gerardo Moreno Salinas ◽

Christopher R. Stephens

Keyword(s):

Data Mining ◽

Distance Learning ◽

Success Factors ◽

Data Mining Techniques

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Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

JURIKOM (Jurnal Riset Komputer) ◽

10.30865/jurikom.v7i2.1762 ◽

2020 ◽

Vol 7 (2) ◽

pp. 200

Author(s):

Puji Santoso ◽

Rudy Setiawan

Keyword(s):

Data Mining ◽

Decision Tree ◽

Microsoft Excel ◽

Customer Data ◽

Data Mining Techniques ◽

C4.5 Algorithm ◽

Marketing Costs ◽

Excel Format ◽

Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry

Computational and Structural Biotechnology Journal ◽

10.1016/j.csbj.2016.05.002 ◽

2016 ◽

Vol 14 ◽

pp. 185-199 ◽

Cited By ~ 10

Author(s):

A. Anguera ◽

J.M. Barreiro ◽

J.A. Lara ◽

D. Lizcano

Keyword(s):

Data Mining ◽

Time Series ◽

Data Mining Techniques

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Addressing the Cold-Start Problem Using Data Mining Techniques and Improving Recommender Systems by Cuckoo Algorithm: A Case Study of Facebook

Computing in Science & Engineering ◽

10.1109/mcse.2018.2875321 ◽

2020 ◽

Vol 22 (4) ◽

pp. 62-73 ◽

Cited By ~ 1

Author(s):

Saman Forouzandeh ◽

Atae Rezaei Aghdam ◽

Soran Forouzandeh ◽

Shuxiang Xu

Keyword(s):

Data Mining ◽

Recommender Systems ◽

Cold Start ◽

Data Mining Techniques ◽

Using Data ◽

Cold Start Problem ◽

Cuckoo Algorithm

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Applying Data Mining Techniques to Predict Student Dropout: A Case Study

2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI) ◽

10.1109/colcaci.2018.8484847 ◽

2018 ◽

Cited By ~ 5

Author(s):

Boris Perez ◽

Camilo Castellanos ◽

Dario Correal

Keyword(s):

Data Mining ◽

Data Mining Techniques ◽

Student Dropout

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Land Subsidence Modelling Using Data Mining Techniques. The Case Study of Western Thessaly, Greece

Advances in Natural and Technological Hazards Research - Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques ◽

10.1007/978-3-319-73383-8_4 ◽

2018 ◽

pp. 79-103 ◽

Cited By ~ 2

Author(s):

Paraskevas Tsangaratos ◽

Ioanna Ilia ◽

Constantinos Loupasakis

Keyword(s):

Data Mining ◽

Land Subsidence ◽

Data Mining Techniques ◽

Using Data

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Data-Driven Decision Making and Proactive Citizen–Scientist Communication: A Cross-Sectional Study on COVID-19 Vaccination Adherence

Vaccines ◽

10.3390/vaccines9121384 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1384

Author(s):

Emil Syundyukov ◽

Martins Mednis ◽

Linda Zaharenko ◽

Eva Pildegovica ◽

Ieva Danovska ◽

...

Keyword(s):

Public Health ◽

Logistic Regression ◽

Exploratory Study ◽

Large Scale ◽

Severe Disease ◽

Data Driven ◽

Personal Factors ◽

Cross Sectional ◽

Digital Platform ◽

Factors Associated

Due to the severe impact of COVID-19 on public health, rollout of the vaccines must be large-scale. Current solutions are not intended to promote an active collaboration between communities and public health researchers. We aimed to develop a digital platform for communication between scientists and the general population, and to use it for an exploratory study on factors associated with vaccination readiness. The digital platform was developed in Latvia and was equipped with dynamic consent management. During a period of six weeks 467 participants were enrolled in the population-based cross-sectional exploratory study using this platform. We assessed demographics, COVID-19-related behavioral and personal factors, and reasons for vaccination. Logistic regression models adjusted for the level of education, anxiety, factors affecting the motivation to vaccinate, and risk of infection/severe disease were built to investigate their association with vaccination readiness. In the fully adjusted multiple logistic regression model, factors associated with vaccination readiness were anxiety (odds ratio, OR = 3.09 [95% confidence interval 1.88; 5.09]), feelings of social responsibility (OR = 1.61 [1.16; 2.22]), and trust in pharmaceutical companies (OR = 1.53 [1.03; 2.27]). The assessment of a large number of participants in a six-week period show the potential of a digital platform to create a data-driven dialogue on vaccination readiness.

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Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

SRPH Journal of Applied management and Agile Organisation ◽

10.47176/sjamao.3.1.1 ◽

2020 ◽

Keyword(s):

Data Mining ◽

Customer Segmentation ◽

Food Distribution ◽

Data Mining Techniques ◽

Customer Lifetime ◽

Rfm Model ◽

Marketing Activities ◽

Using Data ◽

Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

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