By: Mark Lockyer, CallMiner Sales Director UK & EMEA
Both UK energy and water companies offer services specifically designed to support customers in vulnerable positions due to situations such as medical and mental health issues, disability, challenging social circumstances and old age. Companies in both sectors collect data to help identify and support these customers, and now the UK regulators are pushing companies to collaborate between each other through data sharing.
In a recent partnership with the UK Regulators Network (UKRN), Ofwat and Ofgem have challenged their respective water and energy companies with making better use of data and participating in sharing arrangements across the sectors for improved scale and consistency in identifying and supporting vulnerable customers. The working group for this new initiative will start producing quarterly reports through UKRN to both Ofwat and Ofgem on the progress of sharing non-financial vulnerability data.
As water and energy companies begin to participate in this initiative, they must consider the principles of effective data sharing as outlined by UKRN, but also how they will operationally execute their data sharing program. Automated speech analytics can help with both.
Speech analytics solutions are designed to uncover customer insights coming through the contact centre and do it at scale. Unstructured conversational data is transformed into structured data that can be searched, tagged, and safely stored for short and long-term analysis.
Analyzing 100% of customer conversations with automated tagging can provide energy and water companies with a view into the scale and common needs of vulnerable customers and any trends surrounding those needs.
Uncovering actionable insight with speech analytics can provide energy and water companies with the data they need to support customers and share across the industry, as well as the insight to improve customer experience and confidence that their providers are looking out for their best interests as a vulnerable customer.