Bizvento - Knowledge Extraction for Business Opportunities
Since 2013, Glasgow-based Bizvento, has been developing innovative business software for the event management industry. The Scottish start-up has developed a mobile software platform specifically for professional event organisers which lets them manage all aspects of an event in one place. It also gives real-time data analysis capability, useful to event organisers.
After establishing a successful business in providing mobile solutions, Bizvento realised the potential value of the data gathered by its apps at events in the higher and further education sectors, specifically at college and university open days. The app is designed to provide information to prospective students at these events around available courses and programmes. Based on this information, students then have the opportunity to attend talks and information sessions about what’s on offer. This information is recorded in the app and analysed by Bizvento.
The introduction of tuition fees in the UK has created a £20billion market for the higher and further education sector. Bizvento saw the potential to offer universities and colleges across the country the ability to predict and forecast the number of prospective student applicants using reliable data.
To do this, Bizvento created project KEBOP (Knowledge Extraction for Business Opportunities) and approached The Data Lab for partnership, strategic support and grant funding to realise this opportunity. The Data Lab then facilitated the academic partnership between Bizvento and the University of Glasgow based on the data analysis requirements.
KEBOP is made up of a suite of sophisticated analysis tools capable of extracting actionable information from two main sources, namely the usage logs of the Bizvento technologies and the registration data of the Bizvento users.
In the case of the usage logs, the analysis tools adopt information theory to model the behaviour of the users and to identify classes of usage. In particular, the analysis tools manage to discriminate between average usage patterns (those adopted most frequently by the users), peculiar usage patterns (those that appear less frequently while being correct) and wrong usage patterns (those that correspond to incorrect usages of the app). Furthermore, the tools allow the analysis of user engagement as a function of time. This has shown that usage dynamics tend to change abruptly at specific points of time rather than continuously over long periods.
In the case of the registration data, the key-point of the KEBOP approach is the integration of basic information provided by the users (name and postcode) and publicly available data about sociologically relevant information (gender, status, education level, etc.).
The main outcome of KEBOP is a suite of data analysis technologies capable of making sense of the digital traces left by the users of Bizvento products at academic open days.
The usage logs and specifically the registration data captured and analysed by KEBOP technologies will identify the main factors that determine the participation in a large-scale event, not only in terms of the very chances of participating at the event but also in terms of the preferences for different aspects of the event itself (e.g., the choice of specific sessions in a conference). The experiments have been performed over data collected at the Open Days of the University of Glasgow and show that the most important factors underlying the participation of prospective students to the Open Days are as follows: education level, unemployment rate and average income in the area where an individual lives. Advancing on this, the analysis tools also show the interplay between gender, social status and the choice of the subject of study.
Through the analysis of rich data sets, Bizvento can reliably predict the number of prospective student applicants to universities and colleges throughout the UK. This information can be used by higher and further education institutions to inform their student recruitment processes and forecast levels of applications and interest in specific courses and programmes.