Abstract
Customer contacts to various businesses identified in telecom call records convey their interest in availing those services. Multi-dimensional attribute dependence with day and time of such communications generate useful insights for targeted advertising. Also, frequent and significant inter patterns of service associations give the probability that takers of one service may also be the prospects of the other. This work presents a multi granulation rough sets model to address the issue of prospect discovery from interest traits depicted in call records. The proposed method solves problems like higher computational complexity and large statistically insignificant patterns space inherent in traditional intra and inter-pattern mining methods. The algorithm is tested to generate target audience for food and restaurant business using one-month data of anonymous call records of a Thailand based telecom service provider. Some interesting mathematical properties of underlying knowledge structures are also validated.
Highlights
Marketers face the challenge of reaching the right audience for product promotion
Two possible constructs used in work are optimistic and pessimistic multi-granulation rough sets
In pessimistic multi-granulation rough sets, the lower approximation is approximated by a family of equivalence relation with the condition that all granular spaces satisfy the inclusion condition between the equivalence class and the target
Summary
Marketers face the challenge of reaching the right audience for product promotion. telecom service providers have heaps of knowledge nuggets in the form of call records but face challenges in transforming data into revenue. This work combines the research in pattern interestingness measures and rough sets based granulation and partitioning based knowledge structures to address issues in present multi-dimensional intra and inter pattern mining methods like higher algorithm time complexity and huge error prone pattern space of implication relations. The proposed information retrieval system derives Intra patterns of most common and significant attribute dependence of day and Hour with the services under study This knowledge is useful for identification of best Day an Hour for service-specific promotions. Along with best day and Hour of promotion for all the food and restaurant business derived using multi-dimensional association rules; inter pattern mining is used to generate prospects for other related entertainment concepts like travel agencies, beauty salon and nightclub. The paper presents validation of mathematical properties of Multi-granulation knowledge structures so formed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
