Listening Methods was founded to provide packaged, fast time to value, high ROI Voice Self Service and End-to-End Call Analytics SaaS solutions. The company enables customer teams and solution partners to quickly identify and implement opportunities to reap improvements in productivity and customer experience across the steps in the end-to-end call.
To accomplish this, Listening Methods engineers invented a completely new, patent-pending, voice search technology - pattern analysis - that enables the company’s solutions to identify any repeated machine-generated audio in milliseconds in audio recordings or streams at very high levels of performance.
The company’s technology may address many other customer interaction channels, since it is able to identify any machine-generated audio in any customer communications channels.
The company creates and offers its Sound Analytics SaaS platform from its offices in the San Francisco Bay and Boston regions.
Why Did We Choose Our Focus? Because of What Users Say about IVR and End-to-End Call Analytics
Listening Methods discussed with users and industry experts the available approaches to understanding and continually improving caller interactions with voice self service solutions.
They stated that the biggest barriers to achieving an environment of continuous voice self service improvement, first, are total cost of ownership, ROI and project complexity. This conclusion was due the fact that no one set of analytics tools provides the insights they need and that many of current analytics solutions require extensive implementation projects and support.
In addition, users pointed out that there are several major limitations in current options making it difficult to optimize performance in voice self service - no one system supports their analytics requirements.
Log-based Solutions
Log-based solutions often identify problems in voice self service solutions, but they only provide a portion of the insight needed to identify the root cause of the issues. Specifically, analysts are rarely able to listen to callers interacting with the IVR.
It is difficult or impossible for the analyst to choose a selected path through the IVR and then listen to any consequent conversations between the agent and caller. Without being able to listen not only to a caller’s interaction with prompts but also to the conversation with the agent, one can’t really understand the performance of an IVR solution.
Secondly, a common analyst task is to survey calls to discover IVR routing issues or identify new call containment opportunities. The lack of an end-to-end call recording requires the analyst use another system to audit calls– one that is not linked to caller behavior in the IVR.
Third, log-based systems focus on single IVR applications. In fact, callers may first interact with prompts on the ACD, then be routed to one of several different IVR applications and then transfer to an agent. There also may be a post-call survey. Log-based solutions limit analyst views to one of the multiple systems experienced by the caller and they do not reveal the flow from one system to another.
Voice Recognition-based Solutions
The other, less frequently used approach to voice self-service analytics records end-to-end calls and uses voice recognition to reveal system performance. This approach solves the issue of access to call audio. However, a barrier confronted by this approach is processing performance. It is low often resulting in the processing of only a few hundred calls a day. Thus these solutions make it difficult to reveal a number of significant issues.
Also by their nature, voice recognition solutions have capture rates that vary by search term and only capture a portion (often less than 50%) of what they are intended to find. This combination of processing limitation and low capture rates render this approach only somewhat effective.
In summary, when asked if available solutions provide business value at a reasonable total cost of ownership, users' responses are guarded and qualified. In many cases, they have chosen to invest their time and resources into completely different projects. Users strongly express a desire to have the business results, but find it difficult and expensive to get what they need.
Listening Methods concluded that to address users’ goals, we needed to create a new approach to address the issues of performance, cost, insight and, ultimately, value.
This observation sparked the creation of Listening Methods.
