Please use this identifier to cite or link to this item: http://repository.kln.ac.lk/handle/123456789/25355
Title: LYZGen: A mechanism to generate leads from Generation Y and Z by analysing web and social media data
Authors: Senanayake, Janaka
Pathirana, Nadeeka
Keywords: lead generation, named entity recognition, web crawling, web data analyzing
Issue Date: 2021
Publisher: Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka
Citation: Senanayake Janaka; Pathirana Nadeeka (2021), LYZGen: A mechanism to generate leads from Generation Y and Z by analysing web and social media data, International Research Conference on Smart Computing and Systems Engineering (SCSE 2021), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. 59-64.
Abstract: Identifying an appropriate target audience is essential to market a product or a service. A proper mechanism should be followed to generate these potential leads and target audiences. The majority of people who were born between 1981 and 2012 hold top positions in companies. These people are regular social media and website users, since they represent generations Y and Z. They usually keep digital footprints. Therefore, if an accurate method is followed, it is possible to identify potential contact points by analysing publicly available data. In this research, a novel lead generation mechanism based on analysing social media and web data has been proposed and named LYZGen (Leads of Y and Z Generations). The input to the LYZGen model was an optimised search query based on the user requirement. The model used web crawling, named entity recognition (NER), and pattern identification. The model found and analysed freely available data from social media and other websites. Initially, person name identification was performed. An extensive search was carried out to retrieve peoples’ contact points such as email addresses, contact numbers, designations, based on the identified names. Cross verification of the analysed details was conducted as the next step. The results generator provided the final output, which contained the leads and details. Generated details were verified with responses captured via a survey and identified that the model could detect lead details with 87.3% average accuracy. The model used only the open data posted on the internet by the people. Therefore, it did not violate extensive privacy or security concerns. The generated results can be used, in several ways, including communicating promotional details to the potential target audience.
URI: http://repository.kln.ac.lk/handle/123456789/25355
Appears in Collections:Smart Computing and Systems Engineering - 2021 (SCSE 2021)

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