The availability of prospective customer information present on social media platforms has led to many marketing and customer-facing departments utilizing social media data in processes such as demographics research, and sales and campaign planning. However, if your business needs require further filtration of data, beyond what is provided by existing filters, the volume and rate at which data can be manually sifted, is constrained by the speed and accuracy of employees, and their digital competency. The repetitive nature of filtration work, lends itself to automation, that ultimately has the potential to alleviate large productivity bottlenecks, enabling organizations to distill larger volumes of unfiltered data, faster and with greater precision. This project employs automation and artificial intelligence, to filter Linkedin profiles using customized selection criteria, beyond what is currently available, such as nationality and age. By introducing the ability to produce tailored indices of social media data, automated filtration offers organizations the opportunity to better utilize rich prospective data for more efficient customer review and targeting.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-75275 |
| Date | January 2018 |
| Creators | Wöldern, Lars |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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