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Customers Evaluation and Clustering by Using Applications of Data Science in Marketing

Mardi Mamaqani, Hamid Reza | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53096 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Shadrokh Sikari, Shahram
  7. Abstract:
  8. Nowadays we see competition between organizations to maintain excellence and survival in business. Organizations should focus on maintaining and satisfying their customers in their services and products, because it is the customers who, as buyers of services and products, provide the company's revenue stream. With the advancement of computer technology today, a large amount of customer data can be stored and maintained. This data is a valuable resource for analyzing customer behavior and making the right decisions for the organization. Organizations need to use customer behavioral data to make their marketing activities smart and data-driven. In fact, organizations need to move towards data-driven marketing using data science tools. In the current research, the researcher has used data science tools to analyze customer behavior and categorize them to improve marketing activities and adopt the right policies in this area. In other words, the researcher has used data science tools to analyze the behavior, classification, and evaluation of customers of a business in the field of stockbroking brokerage so that the business can be based on the analysis of data Customers improve their marketing policies and have an analysis of their customer behavior. To conduct research, it is necessary to gain knowledge of the business and its goals, and then the required data is analyzed, collected, and pre-processed. After this step, using machine learning algorithms with supervision and without supervision, Analyzed the desired data and developed the model. Finally, it is necessary to evaluate the results and if the results are confirmed, the results can be used correctly in the organization
  9. Keywords:
  10. Customer Clustering ; Marketing ; Data Mining ; Customer Satisfaction ; Data Science ; Data-Driven Marketing

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