Interviews Aided with Machine Learning

We have designed and implemented a Computer Aided Personal Interview (CAPI) system that learns from expert interviews and can support less experienced interviewers by for example suggesting questions to ask or skip. We were particularly interested to streamline the due diligence process when estimating the value for software startups. For our design we evaluated some machine learning algorithms and their trade-offs, and in a small case study we evaluates their implementation and performance. We find that while there is room for improvement, the system can learn and recommend questions. The CAPI system can in principle be applied to any domain in which long interview sessions should be shortened without sacrificing the quality of the assessment.</p

Client churn prediction with call log analysis

© Springer International Publishing AG, part of Springer Nature 2018. Client churn prediction is a classic business problem of retaining customers. Recently, machine learning algorithms have been applied to predict client churn and have shown promising performance comparing to traditional methods. Despite of its success, existing machine learning approach mainly focus on structured data such as demographic and transactional data, while unstructured data, such as emails and phone calls, have been largely overlooked. In this work, we propose to improve existing churn prediction models by analysing customer characteristics and behaviours from unstructured data, particularly, audio calls. To be specific, we developed a text mining model combined with gradient boosting tree to predict client churn. We collected and conducted extensive experiments on 900 thousand audio calls from 200 thousand customers, and experimental results show that our approach can significantly improve the previous model by exploiting the additional unstructured data