Transformation of marketing with technology: Case approach for artificial intelligence

Marketing lives as a living organism with its competitive environment and several internal conditions. Marketing functions should adapt to the world they are bound to and develop their activities by taking into account the changes and developments in micro and macro factors. With the increase in technology and the spread of social media, transformation in marketing has become inevitable. Advances in business applications have better results about understanding customer and markets. As previous studies examine artificial intelligence concept with different contexts, there is lack of integrated study which examine marketing and artificial intelligence together. This study proposes three-steps plan for implementing AI with marketing while it includes nine scenarios from different marketing goals. Study also has suggestions for implementing AI methodologies into business processes

A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy

A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy