Direct Marketing Based on Fuzzy Clustering of Customers (Case Study: on one Mobile Company)

Objective
There is a general tendency toward direct marketing these days. Therefore, instead of designing advertisement and marketing strategies for all the customers in the market, it is recommended to classify the customers based on clustering techniques and then design specific strategies accordingly. This will reduce marketing and advertisement expenses, increase sale department efficiently, build closer and quicker relationships with different customers and etc. There are a variety of clustering methods. Provided that clustering means classifying customers in different groups with maximum similarities within the groups and maximum difference among the groups, it may not be appropriate to apply such a rule in clustering customers (people) due to their nature. Hence, fuzzy clustering technique seems more appropriate for customers because there are no absolute borders considered among different groups just as the market suggests. This study, then, aims to emphasize on this concept in order to apply fuzzy clustering on market.
 
Methodology
This practical research is descriptive-exploratory in nature of data collection. The statistical population includes all the customers of a mobile company, but due to availability issues only a part of their customers would be involved in the present study. A questionnaire including 6 questions was distributed among those customers and only 760 were correctly responded. Finally, EXCEL and S-PLUS were used to analyze the data. 
 
Findings
The data in this study include three different parts of information. The first part includes some indexes selected for analysis of the clustering. Second part concerns with the customers service usage such as distant phone calls, free calls and wireless services. The third part refers to other mobile services provided for each customer. This part is presented in a binary fashion deciding whether a customer has received a specific service or not. Such services include activating more than one mobile line at the moment, using voicemail, paging, internet and other services. This algorithm was used to conduct fuzzy clustering in the present study. Following applying fuzzy clustering, only 2 clusters were judged appropriate for such data. The first cluster includes customers with lower income, job stability and lower loyalty to the mobile company, while the second cluster includes customers with higher income, higher job stability and more loyalty to the mobile company. The customers in the first cluster used services like free calls, wireless networks and pay phones. On the other hand, the customer in the second cluster mainly used services like distant calls and rarely used wireless services. In general, we can claim that paging services were the highest requested and then there are voicemail services, internet, and e-pay services respectively. The two clusters reported to have a similar tendency in using services such as voicemail, multi-lines, conferencing; yet, they were different in services like paging, internet, call forwarding (diverting), call waiting and e-pay services. At the end, mobile companies can set marketing strategies based on such findings.
 
Conclusion
It is suggested that mobile companies focus on general advertisements and distant call services, but only a little focus on wireless services. They can also put more thought on services like paging, voicemail, internet and e-pay services respectively. It is also recommended that, for female customers (mostly within the first cluster), the companies should focus on pay phone services, distant calls, and free calls as well as voicemail and internet. On the other hand, for male customer with higher job stability, it is suggested to focus the most on distant call services and provision of special discounts with this regard, but the least on wireless and pay phone services. Besides, voicemail services, paging, call waiting, call forwarding and e-pay services should be the mobile company’s priority for male customers.