Today’s customers are empowered like never before. With the internet and social media, even a small number of unhappy customers, can result in significant reputational damage. A consistent and scalable process, for ensuring that unhappy customers have an opportunity to speak first to the business, and have their complaints addressed, is essential for every business.
In highly regulated industries, the requirement to demonstrate the effectiveness of the complaint management processes, has been mandated by Government.
CentraCX VOC is an effective complaints management platform that supports rigorous processes for ensuring that customer complaints are collected, understood and actioned.
CentraCX VOC provides the capability for customers with unresolved issues to make the business aware of their concerns. Offering feedback opportunities to each and every customer, after every interaction, businesses ensures that customers have the maximum ability to make themselves heard.
The breadth of survey channels offered by CentraCX VOC ensures that customers are surveyed on the same channel as the interaction. This makes the survey effectively a continuation of the interaction and ensures the complaint management process is an integral part of every interaction.
Understanding the issues at the heart of customer complaints requires contextual information that links the customer, and their interaction, to records within underlying business systems. CentraCX VOC incorporates flexible meta-data with each survey, that can include customer and system references, as well as, links to call and interaction recordings.
CentraCX Tribal Analytics applies an additional layer of context, with customer complaints contextualised by the frontline team member that supported the customer. With the Team Member’s unique insights the underlying causes for complaints and issues can be readily identified.
As customer complete surveys and feedback is received, Tribal Analytics analyses the feedback together with the contextual meta-data. Frontline team member perspectives are sought to further contextualise the feedback. At each stage, machine learning analyses the data, appropriate rules are triggered, and the feedback is presented to the stakeholders that can action and resolve the complaint.