How can AI-driven speech analytics help in quality & compliance

In contact centers of today, artificial intelligence (AI) technology sits right at the intersection of compliance, quality management, and speech analytics. It makes use of cutting-edge speech technologies to support and transcribe support calls at an expansive scale while enabling firms to optimally analyze customer conversations with the core goal of elevating the customer experience and agent performance.

With AI-driven speech analytics, vital moments in customer conversations can be effectively unearthed to provide a better picture of how the contact center is performing across key metrics, enabling them to make proactive quality improvements. Carrying out analytics on interactions like supervisor escalations, emotions, hold times sentiments, and other such aspects can be extremely helpful for businesses that traditionally had low QA coverage.

As contact centers have to handle extensive amounts of data, it can be quite a challenge for them to maintain compliance at times. This can however be made much easier with speech analytics. AI-driven speech analytics facilitates automatic processing of call data, to reduce risk and enhance value. This system essentially enables businesses to monitor and analyze each and every call with custom categories, so as to ensure absolute compliance, rather than being just spot-checked by managers.

AI-driven speech analytics help ensure compliance

Owing to the growing threat of data breaches, having a proper call monitoring process in place has become vital to augment compliance efforts. The process of monitoring calls manually is extremely tedious and involves a great number of resources. Even while having all agents on board, combing through all conversations manually in search of valuable insights is next to possible. This in turn raises the risk of personally identifiable information (PII) being leaked.

AI-driven speech analytics makes it possible to acquire a scrubbed transcript and recording of each conversation almost instantly. By making use of keyword spotting techniques, one can even precisely identify where and when any non-compliant offers are being given, prohibited language is being used, or when the agents are not properly using the needed greetings and disclosures. With its help, one can screen 100% of their call volume to ensure compliance with regulatory standards, and subsequently to redeploy compliance personnel to put their focus on discerning high-value activities like trend analysis and identifying major cultural risks.

Speech Analytics and the QA Process

Even though AI-driven speech analytics cannot essentially replace the whole contact center QA system, this technology surely has the capability to enhance its effectiveness. One of the biggest facilities provided by this technology is that it is able to listen to 100% of the customer conversations, and subsequently extract information about their pain points and preferences on the basis of the statements mentioned. This information is then used by them to make suggestions that can significantly help the contact center team to deliver a superior customer experience. However, for this process to be optimally effective, it is required to be incorporated with the current QA process in place at a company for scoring and monitoring calls.

In addition to tracking every call, speech analytics can prove to be extremely effective in looking for risky language, violations, and behaviors, which would have a huge impact on the failure or success of the process. These data insights, when combined with human insights provided by agents, enable businesses to formulate a refined call center QA system that is able to deliver measurable and accurate feedback when it comes to enhancing the customer experience.

The speech analytics platform of Knowlarity allows businesses to transcribe millions of calls to analyze customer conversations and improve their operational efficiency while ensuring compliance. It is equipped with features like keyword spotting that help in identifying specific words and phrases to filter only the relevant conversations, so as to derive domain-specific insights and performance.