Designing Diversified Sales Strategy by Using Database Marketing (Case Study: Active Insurance Company in Free Trade Zones)

Document Type : Qualitative Research Paper

Authors

1 Associate Prof., Faculty of Entrepreneurship, Tehran University, Tehran, Iran

2 Master of Entrepreneurial Management, Faculty of Entrepreneurship, University of Tehran, Iran

10.34785/J018.2022.561

Abstract

Today, new economic, political, cultural and social trends have emerged in the business environment that has changed the business environment and new challenges are facing businesses. As a result, there is a very difficult competitive environment for businesses to be able to sustain themselves in this situation. One of the most important processes that are the foundation of business success in this situation is sales, and what is choosing the best sale strategy and it uses fundamental innovation, leads to more sales and profit maximizing. Therefore, the main attempt of this research is to design various sales strategies by using database-based marketing methods in the active insurance company in free trade zones. This research is applied based on purpose, how to collect data is descriptive and the research method is a case study with a qualitative approach. Data collection was done through an internal portal and interview with insurance company technical experts. Plus, In the field of research methods, database-based marketing methods have been used on customers’s buying behavior and the composition of the best selling insurance field has been obtained in different regions; so, customers classification is based on demographic variables by clustering method. The results of the research indicate the optimal offer of combining the best-selling insurance field through various sales methods in right areas for people who are more likely to buy than others, which ultimately increases the insurance company’s portfolio.

Keywords

References

 
Ahn, H., Ahn, J., & Kim, D. (2010). Facilitating cross-selling in a mobile telecom market to develpe customer classification model based on hybrid data mining techniques. Expert Systems with Applications, 38(5), 5005-5012.
A.kamakura, W., Wedel, M., de Rosa, F., & Mazzon, J. (2003). Cross selling through database marketing :a mixed data factor analyzer for data augmentation and prediction. International Journal of Research in marketing, 20(1), 45-65.
Ameli, M., & Ayubi-Mobarhan, H. (2012). Introducing eCRM and examining its role and importance in relationship marketing and customer relationship management in the insurance industry. The first specialized conference and exhibition of electronic insurance, Tehran, https://civilica.com/doc/118548 (in Persian).
Azima, S., Jafari, P., Motie Nasrabadi, A., & Sammaknejad, N. (2021). Evaluating Consumers' Emotional, Cognitive, and Behavioral Responses to Nostalgic Stimuli in Advertising: A Mix Method Study. Consumer Behavior Studies Journal, 8 (3), 140-171(in Persian).
Blattberg, R.C., Kim, B.-D., & Neslin, S. A. (2008). Database marketing: analysing and managing customers. New York: Springer.
Bolourian Tehrani, M. (2004). Sales, strategic planning and professional selling skills.Tehran: Commercial Publishing Company (in Persian).
Fasli, M. & Kovalchuk, Y. (2011). Learning approaches for developing successful seller strategies in dynamic supply chain management. Information Sciences, 181(16), 3411-3426.
Grabowska, J. (2013). Significance of database marketing in the process of target segments identification & service. Problems of managemnet in the 21st century, 6, 40-47.
Homburg, C., Böhler, S., & Hohenberg, S., (2020). Organizing for cross-selling: Do it right, or not at all. International Journal of Research in Marketing, 37(1), 56-73.
Hughes, T. (1992). The customer database: cross selling retail financial services. International journal of bank marketing, 10(7), 11-16.
Kumar, B., & Singh, A. (2014). Relationship marketing research (1983-2012): an academic review and classification. International Journal of Electronic Customer Relationship Management, 8(4), 221-250.
Lau, K.-n., Chow, H., & Liu, C. (2003). A database approach to cross_selling in the banking industry: practices, strategies and challenges. Database marketing & customer strategy management, 11(3), 216-236.
Li, S., Sun, B., & L.Montgomery, A. (2011). Cross selling the right product to the right customer at the right time. Journal of Marketing Research, 48(4), 683-700.
Mollashojayi, E. (2014). The role of GIS and DATAMINING geographic information systems in managing the relationship with BCRM banking customers. First National Conference on Monetary and Banking Management Development, Tehran, https://civilica.com/doc/244775 (in Persian).
Pashang, H., Malek-Shahkoohi, J., & Emadi, M. (2012). Applying data mining techniques in customer shopping cart analysis. The first regional conference on new approaches in computer engineering and information technology, Rudsar, https://civilica.com/doc/120004 (in Persian).
Peltier, J., Schibrowsky, J., Davis, J., Schultz, D. (2002). Interactive psychographics: cross selling in the banking industry. Journal of advertising research, 42(2), 7-18.
Seifollahi, N., & Eskandari, N. (2021). Social media marketing and consumer buying intent; Image mediation and brand awareness. Consumer behavior studies, 8(3), 199- 220. (in Persian).
Salazar, M., Harrison, T., & Ansell, J. (2007). An Approach for the identification of cross-sell and up sell opportunities using financial services customer database. Journal of financial services marketing, 12(2), 115-131.
Vyas, R. S., & Math, N. (2006). A comparative study of cross-selling practices in public and private sector banks in India. Journal of Financial Services Marketing, 10(4), 123-134.
Vyas, R. S. (2008). A Study of customers ' perception of cross-selling: using a grounded theory approach. Indian Institude of Management Bangalore Management Review, 20(2), 149-157.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Consumer Behavior Studies Journal
Volume 8, Issue 4
January 2022
Pages 21-45

Files

Share

How to cite

Statistics