Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters’ customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem. 

Outcomes of social media marketing in sport brands

In an increasingly globalized world, social networks appear as an indispensable tool for sports clubs, as they make it easier for them to reach a large number of supporters. The main objective of this study is to measure the impact that this commitment between supporter and club has on the social networks, on the purchase and reference intention. Based on two sequential studies (one survey and in-depth interviews), the methodology aimed to help understand both the viewpoint of the fans and decision-makers. The results show that the commitment between the supporter and the club on the social networks has a positive influence on the relationship between the two; the commitment between the supporter and the club on the social networks has a positive influence on the intention of fans to buy; the commitment between the supporter and the club on the social networks has a positive influence on the intention of reference by the fansinfo:eu-repo/semantics/publishedVersio

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

3rd ASIA International Conference (AIC 2017) Conference Program and Abstract Book

Rural tourism hasbeen shown to benefit local communities from the economic perspective. Digitalmarketing allows marketing information to be transmitted directly to potentialvisitors without the need for an intermediary, in a low-cost but effective way.Rural tourism destinations in Sarawak now have an opportunity to benefit from the Sarawak state government’sinitiative, the Digital Sarawak Centre of Excellence, in terms of digitalcontent creation and website maintenance. However, the current level of adoption is zero to minimal in ruraltourism destinations. This study examines the barriers towards digital marketingadoption from the perspective of rural tourism providers. Fieldwork was performed at two sites,Ba’kelalan and Long Lamai, in July 2016 and February 2017 respectively. A total of 19 respondents were interviewedin-depth. The study revealed thattourism providers currently depended on word-of-mouth or direct contact forbookings, but were willing to adopt digital marketing with the assistance ofknowledgeable parties. However, certainphysical, logistical and social constraints may have a detrimental effect onthe community’s readiness level to entertain tourists on a larger scale and mayfurther impede the overall progress of digital marketing adoption, at both theindividual and destination levels

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way