US businesses lose an estimated $62B every year because of poor customer service. To address the myriad problems with customer service, companies have adopted a multi-channel strategy. In addition to the phone support, we now have new channels like email, SMS, chatbots and social media.
This has led to an explosion in the number of interactions companies have to manage, leading to significant drops in the ‘Quality of customer service’, which leads to poor CSAT scores. And to resolve this problem, companies continue to invest in newer channels and the cycle continues.
Companies realize the problem but simply don’t have the resources to do a great job. For example, below is a sample ‘Quality’ scorecard.
Presently, this is a laborious job with dedicated QA staff scoring every interaction manually. This leads to significantly low coverage (industry average is about 1%) and bias in scoring.
Teams then rely on such sub-optimal insights to drive their decision making on agent performance, customer happiness and even product-related insights. Simply put, the status quo is expensive and not scalable.
Legacy solutions exist in the call center space which helps with a very limited automated QA option. They provide speech analytics scanning for very limited keywords like ‘supervisor’, ‘legal’, ‘complaint’.
However, these were built exclusively for the world where all support was phone-based. They are mostly on-premises, very clunky and expensive leading to large groups of modern CS teams excluded from this.
At Emtropy, we have built an AI-powered product that is infinitely scalable, addresses all the modern channels of support from voice to text, is bespoke and saves significant costs for the already stretched customer support teams.
