HOME | ÍNDICE POR TÍTULO | NORMAS PUBLICACIÓN Espacios. Vol. 37 (Nº 38) Año 2016. Pág. 2
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
ABSTRACT: 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. |
RESUMEN: 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. |
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)
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:
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.
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 |
59 |
57,8% |
E-commerce |
13 |
12,7% |
Management of Relationships with Customers |
10 |
9,8% |
Product Service and Process Customization |
9 |
8,8% |
Competition |
8 |
7,8% |
Adoption of New Products |
2 |
2,0% |
Georeferencing |
1 |
1,0% |
General Total |
102 |
1 |
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).
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
Algorithm |
Number of Documents |
Classification or Decision Tree (C 4.5; C 5.0; Japanese Candlestick) |
25 |
Other |
20 |
Clustering |
13 |
Data Envelopment Analysis, Regression, Correlation and/or Factorial Analysis |
9 |
Neural Networks |
8 |
K-means or T-means |
8 |
A priori Algorithm |
5 |
Support Vector Machine (SVM) |
4 |
Kernel |
3 |
Market Basket Analysis |
3 |
Self-organizing Maps |
2 |
Kohonoen |
2 |
Genetic Algorithm (GA) |
2 |
Source: Own Construction from Scopus, Search Date: 2015/06/06
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
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1. Facultad de Ciencias Económicas, Universidad Nacional de Colombia, Bogotá, Colombia, sprojasb@unal.edu.co
2. Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia. rvegarod@poligran.edu.co
3. Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia. osrobayo@poligran.edu.co
4. Facultad de Minas, Universidad Nacional de Colombia, Medellín, Colombia. lamontoyar@unal.edu.co
5. Institución Universitaria Politécnico Grancolombiano, Bogotá, Colombia. gapiedrahita@poligran.edu.co