Mitacs Accelerate Research Internship Grants Awarded
Professor Morad Benyoucef and two graduate students under his supervision each received grants of $30,000 from the Mitacs Accelerate research internship program and industry partners for projects that examine web page classification and lead-based management systems for optimising sales performance.
Web Page Classification (MITACS and SweetiQ)
Morad Benyoucef with Zhengyang (Steve) Lu (M.Sc. E-Business Technologies)
This project focuses on the process of assigning a web page to one or more predefined categories and it is one of the essential techniques of web mining. Web page classification identifies what type of web page we are extracting data from and can help search engines to effectively deal with and rank web pages by category. The process typically involves machine learning and data mining techniques.
The researchers will compare several existing machine learning and data mining methods commonly used in web page classification, selecting optimal one(s) that can fulfill the project goals. For companies that provide local analytics and insight for large brands and marketing agencies, web page classification techniques can help them to build a healthy mix of listings on search engines, large directories, niche directories, blogs, wikis and so on. This will ultimately provide more insight into the distribution of the types of web pages on which the firm’s local business listings are found.
How List-based and Queued-based Lead Management Systems Drive Inside Sales Performance (MITACS and VanillaSoft)
Morad Benyoucef with Alhassan Abdullahi Ohiomah (M.Sc. E-Business Technologies)
Sales-based customer relationship management (CRM) tools have given sales representatives the ability to utilize customer information and selling strategies to facilitate cross-selling and up-selling activities from inside the organization. This concept, known as “inside sales,” is a fast-growing trend that depends for its success on how efficiently generated leads are managed.
Leads (information about potential customers) are managed either through a list-based platform, which offers a long list of leads requiring the sales representative to filter and select which lead to manage, or a queued-based platform which uses a designed workflow sequence to automatically filter and select the next-best lead for a representative to manage. This project seeks to identify the best lead management system (List-based or Queued-based) to use as part of an inside sales tool to achieve an optimal sales performance.