Customer Success Using Deep Learning

Abstract

Customer Success is gaining priority for Organizations in transforming to recurring revenue business model. For this we need to shift our paradigm from being a “reactive troubleshooting” to “proactively advising” our customers. As part of this transformation various capabilities are being built, to capture customer data, have smart agents that collect information from customer networks to predict a failure before it happens and to advise the customer of the resolution. Products can be both hardware and software. It is trickier to predict a failure or an issue beforehand in software when compared to hardware because in hardware there are predefined set of symptoms for a failure. In software, predicting an issue beforehand means knowing and understanding what code is going in with each commit, defect or an enhancement. In most cases, defects found during internal testing, which are often neglected, crop up as customer issues at a later point in time. In this paper, we propose a solution to predict the potential defects that the customer might find after the release of the product using LSTM and CNN. We also predict the time (weeks or months) within which the customer might face this issue. This knowledge helps the teams to prioritize the defects and proactively resolve them on time before going live with known backlog of issues. Thus improving the quality of product that we deliver. Post production this can help proactively advise customers on these known issues that he might face and recommend a software patch or upgrade path. This paper is aimed at reducing internal failures cost component of Cost of Quality leads to Customer Retention and Success.