We’re living in a world of tightening regulations and ever-changing business environments, where understanding and enhancing customer interactions has taken centre stage. If you analyse customer calls, you have an opportunity to deepen relationships, uncover hidden patterns, address client needs and concerns and ultimately elevate customer experiences.
Seems pretty straightforward, right? But how do we see the gold from the grit? Gaining insights from the vast volume of customer interactions isn’t easy at the best of times. Firms often lack the resources to analyse all customer calls in detail. They typically rely on reviewing randomly selected calls as a gauge of the quality of all interactions. This approach carries a lower level of risk oversight and misses out on valuable insights.
Aveni Detect transforms the way firms approach customer call interactions by employing AI to assess 100% of all interactions. This enables deeper customer insights, comprehensive risk oversight, process automation and data-driven decision making. It also provides the capability to better understand client-adviser dynamics, client comprehension levels, and support compliance to regulations like Consumer Duty.
So how does Aveni Detect analyse your customer calls?
Accessing the data
Aveni Detect operates on a file-based approach. It processes various audio file formats like mp3 and wav. In this initial step, the appropriate metadata is attached to the file and uploaded into the system. The system can also be integrated with your call recording or video conferencing platform, making the entire process even more seamless.
The metadata includes the name of call participants, time of call, call type, and other important details. This information is crucial in providing the context of the analysis and selecting which requirements the call should be assessed against.
Streamlining machine assessment
Behind the scenes, Aveni Detect generates a transcript of the calls. This transcript becomes the foundation for subsequent analysis processes, all of which happens very quickly.
The technology typically uses the call’s type to determine which form of analysis to apply. For example, if it is a fact find call, it will trigger a corresponding assessment form that will check for all the expected components of a fact-find within the recorded call. In this case, Aveni Detect is typically employed to find discussions within the call about verifying client identity, client’s risk appetite, clients goals and objectives, etc.
The system evaluates the calls with a focus on questions relevant to a fact-finding assessment.
Assessing for indicated metrics
Aveni Detect uses advanced Natural Language Processing (NLP) techniques to meticulously analyse all the call transcripts. The transcript undergoes a comprehensive series of assessments, each targeting specific aspects of the call.
The assessment typically depends on the type of call. Some typical call types include ‘Fact find,’ ‘Advice Presentation,’ ‘Yearly review,’ ‘New client call,’ ‘Mortgage & protection sale,’ ‘Debt/collection call,’ etc. Each of these call types has its specific range of questions analysed in their own relevant QA form. The system is programmed with each firm’s own selection of QA forms, so it measures exactly what they care about.”
Aveni Detect’s NLP models also scans every call for complaints and vulnerabilities. It identifies which interaction contains a vulnerability, the specific type of this vulnerability, and its severity. These cases are flagged for the assessor to further review and address accordingly.
The human assessor’s point of view
The machine assessment process leads to a rich repository of data insights. A dashboard is instantaneously populated, showing the number of failed cases/calls, advisor performance metrics, specific question failures, etc. Not only is this a vast increase in the amount of data the QA function can serve decision makers, but also a guarantee that their time has been spent intervening with those cases and clients where they can have the most impact.
The Management Information output of the human quality assessor team is supplemented by automatic analysis on 100% of calls for complaints, vulnerabilities, and call-specific requirements. The dashboard has highlights with relevant tags such as dissatisfaction, complaints, vulnerabilities, and more.
It empowers firms with real-time feedback, aiding in quality assurance and performance enhancement. For example, it provides insights into metrics such as calls that failed the review, which adviser has the highest number of call failures, which assessment question is receiving the most failures, etc.
These data-driven insights enable assessors to focus their efforts on cases that demand attention, enhancing efficiency and decision-making.
Machine assessment of customer calls is revolutionising the way firms analyse customer calls, driving a higher level of oversight. The result? Elevated customer experiences, informed decision-making, optimised processes, cost saving, regulatory compliance and a deeper understanding of client needs and concerns.
Aveni Detect remains at the forefront of innovation in this field, and you can book a demo and see firsthand how it can transform your business.
