Case study data mining applied in data analysis as a marketing strategy for vegan and vegetarian restaurant

Autores/as

  • Valeria de Jesús Castañeda García

Palabras clave:

Clustering, data mining, K-Means, marketing

Resumen

Marketing identifies unrealized needs and desires; define, measure and quantify the size of the identified market and the potential
profit; For this, it is essential to capture, store and analyze consumer information. Big data can be analyzed to obtain information
that in the long term leads to better business decisions and strategies, but it could improve customer relationships and optimizing
the operation of the company?
The objective of this research is to apply a data mining technique that allows a large volume of data to be analyzed in detail, with
the purpose of extracting the most relevant information to implement a digital marketing strategy to retain customers by
generating clusters and the classification of them, to identify the attributes that allow developing a marketing strategy appropriate
to customer retention. The technique to be applied is the K-Means in which it is an unsupervised algorithm, when applied to a set
of data that show the attributes that define the consumer profile, identifying the centers obtained will generate a modeling of the
customer profile with the purpose of implementing a more precise marketing strategy for customer retention for vegan restaurants.
The application of data analysis tools is important for PyMES because it helps determine customer profiles to improve marketing
strategy. The importance of developing the project lies in supporting the learning of new trends in marketing tools through the
analysis of big data using grouping techniques based on data mining, thus encouraging its introduction in PyMES. Likewise, the
project will contribute to support the themes developed, which will allow a more detailed analysis of customer profiles that cannot
be identified through traditional statistical analysis.

Publicado

2021-05-13

Número

Sección

5th Conference on Computer Science and Computer Engineering