How Tethr Benefits from Deepgram
The Landscape
Improving Customer Experience Through Conversational Data
When it comes to contact center speech analytics, traditional tools focus solely on contact center efficiency and monitoring individual agent behavior. But they miss the deeper and more valuable insights that are key to ensuring an excellent and personalized customer experience. Tethr is a speech analytics platform that understands this need and has disrupted the current model by ingesting data directly from customer interactions and surfacing valuable, and often hidden insights to the enterprise. Tethr leverages Voice of the Customer (VoC) data, including recordings and transcripts from customer service interactions to help companies identify customer needs and priorities and measure the effectiveness of a customer support call.
Tethr believes in liberating voice data and unlocking the value of customer conversations across entire organizations. Through its partnership with Deepgram, Tethr is able to quickly turn speech into text, allowing its customers to surface rich, actionable customer experience (CX) insights from conversational data.
The Challenge
Unlocking Valuable Customer Insights Hidden Within Your Unstructured Interaction Data
To gather insights on customer pain points and improve the overall CX, enterprises have traditionally relied on customer analytics solutions that focus on contact center efficiency and agent behavior monitoring. However, these solutions often surface customer insights that lack context and don’t take every touchpoint that makes up the customer journey into consideration—most importantly, valuable phone conversations. Without access to accurate phone calls or analysis of customer and agent experiences, it is extremely difficult for enterprises to understand the true state of CX and unlock critical insights in the customer journey.
Traditional solutions are too costly and cannot not match the speed needed for enterprises. Enterprise customers have issues mining unstructured interaction data, accurately transcribing all phone conversations, and analyzing all CX data. They are missing out on valuable insights to improve business outcomes in every department including sales, product development and customer experience teams.
To ensure they were continuing to meet the changing agent and customer needs, the Tethr team set out to ensure that they had the most up-to-date automatic speech recognition (ASR) technology available in their platform, alongside existing ASR solutions. “We periodically evaluate the speech recognition market to uncover new solutions and ensure we’re matching customers with the best provider for their needs,” said Adam Larsen, chief technical officer at Tethr.
The Solution
Finding the Right Provider to Meet Customer Needs
With the introduction of artificial intelligence (AI), organizations now have the ability to transcribe and analyze all customer and agent conversations effortlessly. Tethr’s out of the box solution provides companies with access to pre-built, research-backed insights in addition to a proprietary 200+ variable AI algorithm that allows enterprises to pinpoint the best and worst interactions and drive best practices. When conducting a recent audit of the speech recognition space, Tethr discovered Deepgram who built an AI driven speech recognition solution. Tethr were further drawn in to its unparalleled accuracy and scalability. Outsourcing transcription has allowed Tethr to build Natural Language Processing (NLP) models and focus more on user experiences. “We made a strategic decision not to invest in building our own transcription engine so that we could leverage the innovation and investments being made by other companies,” said Larsen. “By using ASR providers like Deepgram, we can quickly provide our customers with accurate transcripts, providing them with the actionable insights they need to transform their business. While our partnership with Deepgram is fairly new, we anticipate an efficiency gain and less false positives than we have seen in the past.”
