Espacios. Vol. 37 (Nº 38) Año 2016. Pág. 2

Big Data: trend emerging from research in marketing

Big Data: tendencia emergente de investigación en Mercadeo

Sandra Patricia ROJAS Berrio 1; Ricardo Arturo VEGA Rodriguez 2; Óscar Javier ROBAYO Pinzón 3; Luz Alexandra MONTOYA Restrepo 4; Giovanny Andrés PIEDRAHITA Solórzano 5

Recibido: 13/07/16 • Aprobado: 30/08/2016


1. Introduction

2. Methodology

3. Results

4. Discussion



This document presents Big Data as an emerging trend in marketing research. As methodology two secondary sources, Marketing Science Institute and five search equations at Scopus were used to perform a systematic literature review. Findings: the selected search equation generates 243 abstracts in Scopus at observation window from 2005 to 2015; Big Data has relevance in the international dynamics of knowledge and constitutes a useful tool for market management, being an information management strategy. Management Implications: The central concepts used in the literature present management challenges applicable marketing the Latin-American context as consumption or purchase patterns, E-Commerce, Relationship Management Clients, Customization of Products, Services and Processes, Competition, Adoption of New products and georeferencing.
Keywords: Marketing , Big Data , Market Research , Consumer Behavior.


Este documento presenta el Big Data como tendencia emergente de investigación en Mercadeo. Como metodología se utilizaron dos fuentes secundarias, el Marketing Science Institute (MSI) y se ejecutaron 5 ecuaciones de búsqueda en Scopus con revisión sistemática de literatura. Hallazgos: se encuentra que la ecuación de búsqueda utilizada para el Big Data en Mercadeo arroja 243 resúmenes en Scopus, para la ventana de observación de 2005 a 2015, se evidencia que este tema tiene relevancia en las dinámicas internacionales del conocimiento y se constituye en una herramienta útil para la gestión de mercados, siendo una estrategia de gestión de información y de investigación emergente. Implicaciones Gerenciales: Los conceptos centrales utilizados en la literatura presentan retos de gestión de Mercadeo aplicables al contexto latinoamericano como: Patrones de consumo o compra, Comercio Electrónico, Gestión de Relaciones con Clientes, Personalización de Productos, Servicios y Procesos, Competencia, Adopción de Nuevos Productos y Georeferenciación.
Palabras clave: Mercadeo, Big Data, investigación de mercados, Comportamiento del Consumidor.

1. Introduction

For Drucker (1954), marketing goes and must be interpreted beyond selling: “It is not a specialized activity at all. It is the whole business seen from the point of view of its final result, that is from the customer’s point of view” (1954, p. 37). From this point of view, it is more than evident the focus on the customer. However, for Botch (1957) the concept of marketing incorporates three principles in order to exercise its activities: 1) The customer as the center of the operations carried out by the organization, 2) The philosophy of organizations is based on the concept of profit and not volume, 3) Companies should coordinate functions such as product design, price setting, and development engineering. Afterwards, this author focuses on commercialization.
However, once again, the concept evolves from commercialization to one that focuses on stakeholders in Saxe and Weitz (1982), who believe that the marketing concept requires an organization to "determine the needs of a target market and adapt itself to satisfying those needs better than its competitors,” (1982, p. 343). After analyzing these points of view, it is inferred that the organization wants its customers to be satisfied in order to satisfy its stakeholders.
In this sense, the meaning and application of the concept of marketing has been referred to as "customer oriented,” "driven by the market", "market oriented," and "commercialization oriented,"(Kohli & Jaworski, 1990; Shapiro, 1988). This philosophy guides every activity of an organization towards the understanding and satisfaction of the customers in a superior way.
This way, during the 90s, after the works of Kohli and Jaworski (1990) and Narver and Slater -and their focus on information problems to explain market orientation, consumer’s heterogeneity is of relevance as a fundamental concept for the strategy planning of marketing. In fact, it becomes the key aspect for market segmentation and micromarketing orientation, positioning, and actions (Kamakura, Kim, & Lee, 1996.)
From that point of view to Market Orientation, Kotler, Armstrong, Cámara, and Cruz  (2004), develop the concept of marketing and define it as a social process that implies management, and through which groups or individuals can satisfy different needs within the framework of value generation relations. According to this, Lamb, Hair, and McDaniel (2006) see marketing as an organizational function that involves processes around the generation of value and the management of relationships with customers.
On the other hand, other approaches define marketing as commercialization although it is focused on the goals the shareholders of an organization have. “Marketing is a total system of business activities thought to plan products that satisfy needs, to assign their prices, promote them, and distribute them to target markets in order to fulfill the objectives of an organization.” For Gummesson (2007), the concept of marketing includes the satisfaction of the customer –needs and desires– and it is the “cornerstone” of business. He also says that market orientation is superior to product orientation by suggesting, at the same time, customer orientation as the main organizational process.
Along these lines, the literature identifies three positions: Commercialization, orientation –to the product, the market, and the customer–, and value creation. Within these definitions, several elements enable the contextualization of the discipline and the specification of basic aspects when training a professional within the area of economics.
However, it is necessary to specify research tendencies in marketing –beyond what can be considered as market research, which is just a picture of a particular situation that is inherently biased. For this reason, this document is based on the question: How can Big Data tendency be put together in a methodological research strategy for marketing?
The purpose of this document is to present Big Data as an emerging research tendency in management and especially in marketing. According to the MSI, this is one of the most relevant topics to address marketing as a science due to its qualities to be used as a tool in order to deal with multiple amounts of data, sources, and structures. (J. J. Berman, 2013)

2. Methodology

For this document, we executed 5 search equations in Scopus, and for one of them, we carried out a systematic literature revision. The protocol for this was developed according to the parameters established by Kitchenham (2004). Therefore, we developed the following actions for each stage:

  1. Specification of Interest Questions: Which are the previous studies in Big Data, Data Mining, and Pattern Recognition for marketing?, and Which are the most used methods and algorithms within marketing?
  2. Search Strategies: We built a search equation to be executed in Scopus in order to carry out the process. The equation was previously validated by the research team and adjusted to the research questions. It was: ( TITLE ( "Big Data"  OR  "data mining"  OR  "pattern recognition" )  AND  TITLE (consum*  OR  market* ) )  AND  PUBYEAR  >  2004). This equation used prototypes of the terms, expressions, thesaurus, syntagmas, and Boolean operators.
  3. Inclusion Criteria: We exclusively used articles that explain Big Data developments, Data Mining, and Pattern Recognition for marketing.
  4. Data Synthesis and Extraction Procedure: We revised the central concepts used in documents, application environments, research objectives, algorithms used, and findings of the documents where the empirical validation was methodologically specified.

As a result, we found that the search equation displayed 243 summaries in Scopus, for the observation window from 2005 to 2015. This topic is relevant for international knowledge dynamics, as shown in Figure 1:

Figure 1. International Production Dynamics on Big Data, Data Mining, and Pattern Recognition for Marketing

Source: Own Construction from Scopus, Search Date: 2015/06/06

Next, we refer to the central concepts, purposes, and algorithms in the literature that is focused on marketing. It is important to clarify that from the literature that was collected with the search equation, when verifying those documents that referred specifically to commercialization and marketing, we found that 42% of that literature includes this environment within its empirical validation. The rest of the documents deal with topics such as valuation of shares and consumer electronics fraud, among others that are not to be studied in this research.

3. Results

3.1. Central Concepts Used in Documents that Use Big Data in Marketing

Articles focused on marketing have central concepts in descending order: Consumption and/or Purchase Patterns, E-commerce, Management of Relationships with Customers, Product Service and Process Customization, Competition, Adoption of New Products, and Georeferencing. Table 1 shows the amount and percentage of articles according to the corresponding central concept.

Table 1. Amount and Percentage of Articles according to Corresponding Central Concept

Central Concept

Number of Documents


Consumption and/or Purchase Patterns






Management of Relationships with Customers



Product Service and Process Customization






Adoption of New Products






General Total




Source: Own Construction from Scopus, Search Date: 2015/06/06

Firstly, the articles that are focused on Consumption and/or Purchase Patterns are mostly meant to explore available data mining techniques in order to carry out an adequate market segmentation (Dutta, Bhattacharya, & Kumar, 2014) and algorithm tests to explain consumption patterns (M. Chen, Cao, & Wen, 2014; Kurokawa, 2006; Raschman & Ďuračková, 2009).
Secondly, the purpose of the researches that were revised was classifying variables tied to consumption such as services, products, organizations, or brands (Hsu, Chang, & Kuo, 2012; W. P. Li, Quan, & Cai, 2014; W. Li, Wu, Sun, & Zhang, 2010; Liao, Chen, & Hsu, 2009; Vintilǎ & Gherghina, 2014); to support customer classification; calculation of their lifetime value; product offer; and segmentation (Ahn et al., 2010; Ahn, Ahn, Oh, & Kim, 2011; Biscarri et al., 2008; Buruncuk & Badur, 2010; Ciskowski & Zaton, 2010; Hayashi, Hsieh, & Setiono, 2009; Hemalatha, 2012; Hsieh & Chu, 2009; Huang & Huang, 2011; Knuth, 2012; Kurokawa, 2006; Y. Li, Cook, & Wreford, 2009; Liang, Liang, & Wang, 2013; Liu & Chen, 2009; Nce, Ünal, & Yüksek, 2007; Setiabudi, Budhi, Purnama, & Noertjahyana, 2011; Singh, Turi, & Malerba, 2007; Tian, Chen, & Wang, 2008; Trnka, 2010; Zeng & Pan, 2010; Zhang, Yang, Shi, & Lu, 2008; Zhou & Lei, 2010), not only in physical environments (Crone & Soopramanien, 2005; Suxiang & Yonsheng, 2009; Tian et al., 2008; Wang, Li, Zhang, Tian, & Shi, 2009), but also in virtual ones (Ge, 2009; Hu, Hu, & Wang, 2006; Sammour, Schreurs, & Vanhoof, 2009; Suxiang & Yonsheng, 2009), including the decision making of service location (Dzieciolowski & Kina, 2008), as well as internationalization (Athappilly, Razi, & Tarn, 2010; Golsefid, Turksen, & Zarandi, 2012).
Thirdly, the main objective has been to explain typical (Liang et al., 2013) and atypical consumption patterns (Kwong, McPherson, Shibata, & Zee, 2012; Xie, Zhang, Fu, Li, & Li, 2014), to predict product adoption cycles (Chunfang, Yingliang, & Haijun, 2008; Crone & Soopramanien, 2005; Rahman, Fung, & Liu, 2014), preferences from an emotional perspective (Lü, Chen, & Sui, 2013), new uses a product may have along with their risks (C.-H. Chen, Yan, & Chen, 2013; Hirata, Kitamura, Nishida, Motomura, & Mizoguchi, 2013), and development needs of new products (Al-Noukari & Al-Hussan, 2008).

3.2. Most Used Algorithms for Big Data in Marketing

The most relevant algorithms found within the literature are: Classification or Decision Tree, K-means or T-means, Clustering, Neural Networks and Data Envelopment Analysis, Regression, and Correlation and/or Factorial Analysis, which are shown in Table 3 . Below, you can find the description of the use of these algorithms.

The revision of the literature allows identifying the different algorithms used for Big Data in marketing. First of all, we found the Classification or Decision Tree (C 4.5; C 5.0; Japanese Candlestick) as well as K-means or T-means that have been used mostly to discriminate information (Vintilǎ & Gherghina, 2014), improve information classification and the answers of the users (Surma & Furmanek, 2011), in addition to commercialization performance. Secondly, we found Clustering algorithms and methods that have also contributed to the classification of customers (Vintilǎ & Gherghina, 2014), to the discovery of consumption profiles (Ramos, Vale, Santana, & Duarte, 2007), and to the offer of adequate products and communication according to segments (Nce et al., 2007).

Likewise, neural networks have contributed to the improvement of data visualization and to the finding of new business opportunities (Enke & Thawornwong, 2005; Hsieh & Chu, 2009; Viktor, Pena, & Paquet, 2012). This way, it is possible to highlight its predictive power (Hayashi et al., 2009). Data Envelopment Analysis, Regression, and Correlation and/or Factorial Analysis have contributed to the finding of intermediary and latent variables that are present in the consumption and/or purchase patterns that affect loyalty and therefore the repurchase patterns of a consumer (Kong & Zhang, 2011), as well as the possible cross-selling mixtures according to consumer segments (Ahn et al., 2011).

Table 2. Most Used Algorithms in Marketing Big Data Literature


Number of Documents

Classification or Decision Tree (C 4.5; C 5.0; Japanese Candlestick)






Data Envelopment Analysis, Regression, Correlation and/or Factorial Analysis


Neural Networks


K-means or T-means


A priori Algorithm


Support Vector Machine (SVM)




Market Basket Analysis


Self-organizing Maps




Genetic Algorithm (GA)


Source: Own Construction from Scopus, Search Date: 2015/06/06

4. Discussion

This document presented Big Data as the emerging research tendency in marketing translated as an alternative to address the problems of the discipline as long as an organization aims to include market orientation and value creation within its scope (Gummesson, 2007; Kohli & Jaworski, 1990; Kotler et al., 2004; Narver & Slater, 1990; Shapiro, 1988).

At the same time, the reader can notice that besides the growth and evolution of literature regarding Big Data in marketing, it is also possible to conceptually and methodologically apply this tool in Latin American organizations when dealing with purchase or consumption patterns as well as product, service, and process customization.

First of all, for purchase or consumption patterns there are previous publications (Sandoval, Pinzón, Rincón, & Cortés, 2009) of Colombian cases that along with supermarket information systems or those of any organization that has customer loyalty cards enable business intelligence and Big Data analysis.

Second of all, for Product, Service, and Process Customization, Latin American organizations have a high penetration level of Customer Relationship Management (CRM) systems (Smart_Process, 2014) and a clear utility of this type of tools from the perspective of its users (Borja, Pineda, & Rojas, 2016). However, it was not possible to evidence a frequent use for mass customization, which constitutes an opportunity because companies have not only structured but also badly structured data (J. J. Berman, 2013.) This is one of the principles when considering the use of this type of techniques and not only business intelligence.

Nevertheless, it is important to highlight that dealing with marketing problems by using Big Data implies considering academic and research work from a complex and interdisciplinary perspective. This is because it is not enough to keep into account the perspective of the organization (from a Business Administration point of view), but it is also necessary to continue building the discipline according to its origin and evolution: based on the progress and perfection of other areas of knowledge.


Ahn, H., Ahn, J. J., Oh, K. J., & Kim, D. H. (2011). Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques. Expert Systems with Applications, 38(5), 5005-5012. doi:10.1016/j.eswa.2010.09.150

Ahn, H., Song, C., Ahn, J. J., Lee, H. Y., Kim, T. Y., & Oh, K. J. (2010). Using hybrid data mining techniques for facilitating cross-selling of a mobile telecom market to develop customer classification model. En 43rd Annual Hawaii International Conference on System Sciences, HICSS-43. School of Business Information Technology, Kookmin University, Seoul, South Korea. doi:10.1109/HICSS.2010.429

Al-Noukari, M., & Al-Hussan, W. (2008). Using data mining techniques for predicting future car market demand. En 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA. Arab International University, Damascus, Syrian Arab Republic. doi:10.1109/ICTTA.2008.4530367

Athappilly, K., Razi, M. A., & Tarn, J. M. (2010). A multi-technique data mining approach to exploring consumer behaviors. Human Systems Management, 29(3), 153-163. doi:10.3233/HSM-2010-0727

Berman, J. J. (2013). Principles of Big Data Preparing, Sharing, and Analyzing Complex Information. (J. J. B. T.-P. of B. D. Berman, Ed.). Boston: Morgan Kaufmann. doi:

Biscarri, F., Monedero, I., León, C., Guerrero, J. I., Biscarri, J., & Millán, R. (2008). A data mining method based on the variability of the customer consumption - A special application on electric utility companies. En ICEIS 2008 - 10th International Conference on Enterprise Information Systems (Vol. AIDSS, pp. 370-374). Department of Electronic Technology, University of Seville, C/Virgen de Africa, 7, 41011 Sevilla, Spain.

Borja, A., Pineda, Z., & Rojas, S. (2016). Influencia del uso de un aplicativo CRM en el desempeño de funcionarios del área de mercadeo: revisión de caso en una entidad bancaria. Estrategias, 13(23), 22. Recuperado a partir de de las TIC en entidades bancarias.pdf

Botch, F. (1957). The Marketing Philosophy as a Way of Business Life. Marketing Series, 99, 3-16.

Buruncuk, G., & Badur, B. (2010). A data mining study for customer segmentation and profiling: A case study for a fast moving consumer goods company. En 14th International Business Information Management Association Conference, IBIMA 2010 (Vol. 3, pp. 1692-1702). Department of Management Information Systems, Bogaziçi University, Istanbul, Turkey: International Business Information Management Association, IBIMA. Recuperado a partir de

Chen, C.-H., Yan, W., & Chen, N.-F. (2013). Consumer-oriented product conceptualization via a web-based data mining approach. En 19th ISPE International Conference on Concurrent Engineering, CE 2012 (pp. 945-956). School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore. doi:10.1007/978-1-4471-4426-7-80

Chen, M., Cao, M., & Wen, Y. (2014). Cloud-based massive electricity data mining and consumption pattern discovery. (H. Z., L. C., H. J., & H. G., Eds.)14th International Workshops on Web Information Systems Engineering, WISE 2013 with Big WebData, MBC, PCS, STeH, QUAT, SCEH,and STSC. Shandong University of Science and Technology, Qingdao, Shandong, China: Springer Verlag. Recuperado a partir de

Chunfang, Z., Yingliang, W., & Haijun, G. (2008). Study on knowledge acquisiton of the telecom customers’ consuming behaviour based on data mining. En 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008. School of Economics and Commerce, South China University of Technology, Guangzhou, China. doi:10.1109/WiCom.2008.2541

Ciskowski, P., & Zaton, M. (2010). Neural pattern recognition with self-organizing maps for efficient processing of forex market data streams. 10th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2010. Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Poland. doi:10.1007/978-3-642-13208-7_39

Crone, S. F., & Soopramanien, D. (2005). Predicting customer online shopping adoption - An evaluation of data mining and market modelling approaches. En 2005 International Conference on Data Mining, DMIN’05 (pp. 215-221). Department of Management Science, Lancaster University, United Kingdom. Recuperado a partir de

Drucker, P. (1954). The Practice of Management. Harper & Row.

Dutta, S., Bhattacharya, S., & Kumar, K. (2014). Data Mining in Market Segmentation: A Literature Review and Suggestions. En Proceedings of the Third International Conference on Soft Computing for Problem Solving (Vol. 259, pp. 225-235). doi:10.1007/978-81-322-1768-8

Dzieciolowski, K., & Kina, D. (2008). Data mining in marketing acquisition campaigns. En Informatics 2008 and Data Mining 2008, MCCSIS’08 - IADIS Multi Conference on Computer Science and Information Systems (pp. 173-175). John Molson School of Business, Concordia University, Montreal, QC, Canada. Recuperado a partir de

Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927-940. doi:10.1016/j.eswa.2005.06.024

Ge, Y. (2009). Consumers’ behavior on web based on data mining. En 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009 (pp. 43-46). School of Business Administration, Shandong University of Finance, Ji’nan 250014, China. doi:10.1109/ICCSIT.2009.5235087

Golsefid, S. M. M., Turksen, I. B., & Zarandi, M. H. F. (2012). A type-2 data mining optimization for predicting pistachio global market. En 2012 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2012. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran. doi:10.1109/NAFIPS.2012.6291065

Gummesson, E. (2007). Extending the service-dominant logic: from customer centricity to balanced centricity. Journal of the Academy of Marketing Science, 36, 15-17. doi:10.1007/s11747-007-0065-x

Hayashi, Y., Hsieh, M.-H., & Setiono, R. (2009). Predicting consumer preference for fast-food franchises: A data mining approach. Journal of the Operational Research Society, 60(9), 1221-1229. doi:10.1057/palgrave.jors.2602646

Hemalatha, M. (2012). Market basket analysis - A data mining application in Indian retailing. International Journal of Business Information Systems, 10(1), 109-129. doi:10.1504/IJBIS.2012.046683

Hirata, A., Kitamura, K., Nishida, Y., Motomura, Y., & Mizoguchi, H. (2013). Accident-data-aided design: Visualizing typical and potential risks of consumer products by data mining an accident database. En 2013 6th IEEE/SICE International Symposium on System Integration, SII 2013 (pp. 376-381). Tokyo University of Science, 2641 Yamazaki, Noda-shi, Chiba 278-8510, Japan: IEEE Computer Society. Recuperado a partir de

Hsieh, N.-C., & Chu, K.-N. (2009). Enhancing consumer behavior analysis by data mining techniques. International Journal of Information and Management Sciences, 20(1), 39-53. Recuperado a partir de

Hsu, C.-H., Chang, A.-Y., & Kuo, H.-M. (2012). Integrating grey theory into kano’s QFD based on data mining to enhance supply market survey with purchasing. WSEAS Transactions on Information Science and Applications, 9(2), 37-47. Recuperado a partir de

Hu, J.-S., Hu, Y.-C., & Wang, C.-H. (2006). Data mining for analyzing consumer behavior of on-line game players. En 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 (pp. 929-942). Institute of Information Management, Hsuan Chuang University, 48,Hsuan Chuang Road, Hsinchu City 300, Taiwan. Recuperado a partir de

Huang, D., & Huang, Z. (2011). Consumption pattern recognition system based on SVM. En 2011 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011 (Vol. 1, pp. 79-82). College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China. doi:10.1109/ICICTA.2011.27

Kamakura, W., Kim, B., & Lee, J. (1996). Modeling Preference and Structural Heterogeneity in Consumer Choice. Marketing Science, 15(2), 152-172.

Kitchenham, B. (2004). Procedures for performing systematic reviews.

Knuth, E. (2012). Trading Between the Lines: Pattern Recognition and Visualization of Markets. Trading Between the Lines: Pattern Recognition and Visualization of Markets. John Wiley and Sons. doi:10.1002/9781118531532

Kohli, A., & Jaworski, B. (1990). Market Orientation: The Construct, Research Propositions, and Managerial Implications. Journal of Marketing, 54(2), 1-18.

Kong, Q., & Zhang, M. (2011). The research on model of mediating effect of relationship benefits in impact of relationship marketing on customer loyalty: Using data mining. Journal of Computational Information Systems, 7(1), 311-318. Recuperado a partir de

Kotler, P., Armstrong, G., Cámara, D., & Cruz, I. (2004). Marketing (Decima.). Madrid: Pearson Educación, S.A.

Kurokawa, T. (2006). Evolutionary strategy with fuzzy candlestick pattern recognition for market timing. En 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 (pp. 2335-2345). Aichi Institute of Technology, Department of Applied Information Science, 1247 Yachigusa, Yagusa-cho, Toyota 470-0392, Japan. Recuperado a partir de

Kwong, B. M., McPherson, S. M., Shibata, J. F. A., & Zee, O. T. (2012). Facebook: Data mining the World’s Largest Focus Group: With the data at Facebook's disposal, could it predict outcomes within the typically volatile financial markets. Graziadio Business Report, 15(3). Recuperado a partir de

Lamb, C., Hair, J., & McDaniel, C. (2006). Marketing (Onceava.). Mexico D.F.: Thomson. Recuperado a partir de Octava

Li, W. P., Quan, D. Q., & Cai, J. (2014). Application of data mining in sports in the consumer market segmentation. (Y. S.-F., Ed.)3rd International Conference on Information Technology and Management Innovation, ICITMI 2014. Xi‘an Physical Education University, Xi‘an, China: Trans Tech Publications Ltd. doi:10.4028/

Li, W., Wu, X., Sun, Y., & Zhang, Q. (2010). Credit card customer segmentation and target marketing based on data mining. En 2010 International Conference on Computational Intelligence and Security, CIS 2010 (pp. 73-76). Management Department, City College, Dongguan University of Technology, Dongguan, Guangdong, China. doi:10.1109/CIS.2010.23

Li, Y., Cook, G., & Wreford, O. (2009). Online insurance consumer lifetime value evaluation - A mathematics and data mining approach. En 2009 WRI World Congress on Software Engineering, WCSE 2009 (Vol. 1, pp. 401-407). River and Harbor Department, Nanjing Hydraulic Research Institute, Nanjing, 210024, China. doi:10.1109/WCSE.2009.383

Liang, J.-F., Liang, J.-M., & Wang, J.-P. (2013). Empirical study on B/C apparel consumption behavior based on Data Mining technology. Journal of Donghua University (English Edition), 30(6), 530-536. Recuperado a partir de

Liao, S.-H., Chen, J.-L., & Hsu, T.-Y. (2009). Ontology-based data mining approach implemented for sport marketing. Expert Systems with Applications, 36(8), 11045-11056. doi:10.1016/j.eswa.2009.02.087

Liu, S. S., & Chen, J. (2009). Using data mining to segment healthcare markets from patients’ preference perspectives. International Journal of Health Care Quality Assurance, 22(2), 117-134. doi:10.1108/09526860910944610

Lü, J., Chen, D., & Sui, Y. (2013). A data mining method for fashion design to evaluate consumers’ emotional preference. International Journal of Applied Mathematics and Statistics, 51(23), 148-155. Recuperado a partir de

Narver, J., & Slater, S. (1990). The Effect of a Market Orientation on Business Profitability. Journal of Marketing, 54(4), 20-35.

Nce, O., Ünal, A., & Yüksek, K. (2007). Data mining algorithms on the web: An application for marketing campaigns. En 37th International Conference on Computers and Industrial Engineering 2007 (Vol. 1, pp. 405-410). Department of Industrial Engineering, Faculty of Engineering and Architecture, Istanbul Kültür University, Istanbul, Turkey. Recuperado a partir de

Rahman, O., Fung, B. C. M., & Liu, W.-S. (2014). Using data mining to analyse fashion consumers preferences from a cross-national perspective. International Journal of Fashion Design, Technology and Education, 7(1), 42-49. doi:10.1080/17543266.2013.864340

Ramos, S., Vale, Z., Santana, J., & Duarte, J. (2007). Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements. En 2007 IEEE Power Engineering Society General Meeting, PES. Department of Electric Power Systems, Polytechnic Institute of Porto. doi:10.1109/PES.2007.385996

Raschman, E., & Ďuračková, D. (2009). Area chip consumption by a novel digital cnn architecture for pattern recognition. 19th International Conference on Artificial Neural Networks, ICANN 2009. Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia. doi:10.1007/978-3-642-04274-4_38

Sammour, G., Schreurs, J., & Vanhoof, K. (2009). Data mining and marketing approach to track customer movements. En 15th Annual Scientific Conference on Web Technology, New Media Communications and Telematics Theory Methods, Tools and Applications, EUROMEDIA 2009 (pp. 5-9). Hasselt University, Diepenbeek Campus, Agoralaan Gebouw D, B3590 Diepenbeek, Belgium: EUROSIS. Recuperado a partir de

Sandoval, M., Pinzón, O., Rincón, J., & Cortés, O. (2009). Patrones de elección de marca en función de los cambios en los niveles de refuerzo utilitario e informacional en categorías de productos de consumo masivo. Revista Latinoamericana de Psicologia, 41(3), 497-517.

Saxe, R., & Weitz, B. (1982). The SOCO Scale: A Measure of the Customer Orientation of Salespeople. Journal of Marketing Research, 19(3), 343-351.

Setiabudi, D. H., Budhi, G. S., Purnama, I. W. J., & Noertjahyana, A. (2011). Data mining market basket analysis’ using hybrid-dimension association rules, case study in Minimarket X. En 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering, URKE 2011 (Vol. 1, pp. 196-199). Informatics Department, Petra Christian University, Surabaya, Indonesia. doi:10.1109/URKE.2011.6007796

Shapiro, B. (1988). What the Hell is Market Oriented? Harvard Business Review.

Singh, R. P., Turi, A., & Malerba, D. (2007). Grid-based data mining for market basket analysis in the retail sector. En 8th International Conference on Data, Text and Web Mining and their Business Applications including Information Engineering Management, DATA07 (Vol. 38, pp. 293-302). Department of Computer Science, University of Bari, Italy. doi:10.2495/DATA070291

Smart_Process. (2014). El impacto del CRM en Colombia. El impacto del CRM en Colombia. Recuperado 8 de agosto de 2014, a partir de

Surma, J., & Furmanek, A. (2011). Data mining in on-line social network for marketing response analysis. En 2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011 (pp. 537-540). Faculty of Business Administration, Warsaw School of Economics, Warsaw, Poland. doi:10.1109/PASSAT/SocialCom.2011.72

Suxiang, W., & Yonsheng, J. (2009). Deeply analysis in mobile clients’ consumptive behavior based on data mining technology. En 2009 International Forum on Information Technology and Applications, IFITA 2009 (Vol. 1, pp. 222-225). School of Economic and Management, Beijing University of Posts and Telecommunications(BUPT), Beijing 10086, China. doi:10.1109/IFITA.2009.570

Tian, Y., Chen, J., & Wang, X. (2008). The research of railway transport market subdivision based on data mining technology. En 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE/SOLI 2008 (Vol. 1, pp. 147-152). School of Traffic and Transportation, Beijing Jiaotong University, BJTU, Beijing, China. doi:10.1109/SOLI.2008.4686381

Trnka, A. (2010). Market basket analysis with data mining methods: Six sigma methodology improvement. En 2010 International Conference on Networking and Information Technology, ICNIT 2010 (pp. 446-450). Department of Applied Informatics, University of SS. Cyril and Methodius, Trnava, Slovakia. doi:10.1109/ICNIT.2010.5508476

Viktor, H. L., Pena, I., & Paquet, E. (2012). Who are our clients: Consumer segmentation through explorative data mining. International Journal of Data Mining, Modelling and Management, 4(3), 286-308. doi:10.1504/IJDMMM.2012.048109

Vintilǎ, G., & Gherghina, Ş. C. (2014). Pattern recognition techniques to classify the European emerging markets companies from the valuation perspective. Economic Computation and Economic Cybernetics Studies and Research, 48(1). Recuperado a partir de

Wang, G., Li, F., Zhang, P., Tian, Y., & Shi, Y. (2009). Data mining for customer segmentation in personal financial market. Communications in Computer and Information Science. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China. doi:10.1007/978-3-642-02298-2_90

Xie, Y., Zhang, D., Fu, Y., Li, X., & Li, H. (2014). Applied research on customer’s consumption behavior of bank POS machine based on data mining. En 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014 (pp. 1975-1979). School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB) Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing, China: Institute of Electrical and Electronics Engineers Inc. doi:10.1109/ICIEA.2014.6931492

Zeng, H., & Pan, D. (2010). A knowledge discovery and data mining process model in E-marketing. En 2010 8th World Congress on Intelligent Control and Automation, WCICA 2010 (pp. 3960-3964). Center for Business Intelligence Research, Management School, Jinan University, Guangzhou 510632, China. doi:10.1109/WCICA.2010.5553834

Zhang, X.-H., Yang, X.-C., Shi, W.-H., & Lu, T.-J. (2008). Data mining-based marketing support system for telecom operators. En 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008. Economics and Management School, Beijing University of Posts and Telecommunications, Beijing 100876, China. doi:10.1109/WiCom.2008.2749

Zhou, S., & Lei, G. (2010). An analysis and forecasting of white liquor market based on web data mining. En 2010 International Conference on Computational and Information Sciences, ICCIS2010 (pp. 211-213). Department of Computer Science, Sichuan Institute of Technology, Zigong, China. doi:10.1109/ICCIS.2010.57

1. Facultad de Ciencias Económicas, Universidad Nacional de Colombia, Bogotá, Colombia,
2. Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia.
3. Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia.

4. Facultad de Minas, Universidad Nacional de Colombia, Medellín, Colombia.

5. Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia.

Revista Espacios. ISSN 0798 1015
Vol. 37 (Nº 38) Año 2016

[En caso de encontrar algún error en este website favor enviar email a webmaster]