Project - Predictive Complaints Analysis
Complaints result from the building frustration as customers encounter friction navigating processes, systems and bureaucracy. Machine learning can make it possible to see everything that has happened leading up to a complaint, recognising stress points and predicting when and how complaints are likely to arise.
Our value goal was to predict the circumstances and when a complaint is likely, enable preventative fixes and achieve a 10% reduction in complaints.
We analysed over 43 billion data points, identifying areas requiring priority attention to avoid potential complaints. The work replaced a manual process that took 4 days to classify just 20% of complaints, with a new automated process that takes just 40 minutes to classify 100% of complaints.
Our analysis directed remediation work to areas of the web application which were common triggers for complaints, building support prompts at typical friction points in order to improve the customer experience and prevent potential future complaints.
Automate complaint categorisation
Natural Language Processing analysed the complaints, replacing the existing manual process.
Link complaints to customers and events
We built an automated solution identifying individual customers, the timeline and interactions leading up to their complaints.
Machine learning path analysis
Machine learning identified the journey features which were highly predictive of a complaint.