Real-Time Speech Analytics to Improve QA in Call Centers

By | June 10, 2014

Today’s call centers have multiple options when it comes to providing customer service, and each subsequent technology improves the efficiency of the center and the agents working in the facility. Until very recently, one of the processes still being performed using traditional methods was quality assurance (QA). The processes involved QA-use procedures and tools to evaluate how agents in the call center interact with customers to identify performance, quality, and compliance issues. For many organizations that haven’t migrated to automated QA solutions, this is a very time-consuming process, and the question is why so many call centers are reluctant to get rid of these antiquated systems.

Jason Napierski asked this very same question in an article he recently wrote on, and he concluded organizations don’t want to change and are misinformed regarding the new technology. This conclusion not only affects the call center industry, but virtually every organization without the right management to appreciate the benefits of adopting a new way of doing things.

Why use automated QA platforms?

Making the transition to automated QA platforms with speech analytics introduces a new level of efficiency that lowers costs, provides better customer service, and delivers a scalable technology that grows to meet the demand of the organization. Traditional QA on the other hand is time-consuming, and the more the company grows, the more expensive it becomes to carry out this monitoring practice.

Speech analytics and automated QA offer a real-time ‘decisioning’ engine, which provides agents with access to different solutions based on the organization’s needs. This includes real-time step-by-step guidance based on an agent’s skill level and quality scores, as well as the customer’s profile and data on past interactions and preferences. While this help is being delivered, the interaction is being recorded from beginning to end so it can be part of the QA automatically.

The application can retrieve real-time data from different systems and databases with contextual information about the interaction with the agent. Data from the agent’s desktop and/or conversation with customers along with profiles that include tenure, skill proficiency, KPIs and more can be used to deliver a more accurate analysis.

This gives supervisors actionable tools to provide a concrete course of action to improve any weaknesses highlighted by the speech analytics and automated QA platform. Once problems have been identified, the technology can also be used to provide real-time call-outs with step-by-step guidance with feedback tools that allow supervisors to monitor agents. In this way, agents can improve their performance and increase their skill level, which can go a long way in reducing the high attrition rate the industry is known for.