Contact centers are adopting speech-to-text technology to transform customer and agent interactions into data-driven insights and actionable outcomes.
As modern customer service evolves, the complexity of calls handled in contact centers is growing. Advances in automation technology and self-service tools mean that 73% of customers now call in to specifically address concerns – meaning conversations are likely to be more difficult and time-consuming than ever before.
For the average contact center agent, work is becoming more challenging, sometimes less engaging, and potentially more emotionally draining. At the same time, customer experience leaders and other stakeholders need to meet rising customer expectations to deliver against KPIs. Regulatory pressures, data concerns, and legacy tech issues are mounting.
But with agents potentially becoming less engaged and stretched too thinly, how can businesses ensure their contact center delivers better customer service and faster resolutions? Especially since, in many instances, call volumes are rising significantly, but without a correlated rise in resource – adversely affecting the agent experience.
Unlocking insight from existing data is a cost-effective way to improve efficiency, enhance customer experience (CX) and give agents the information they need to perform – elevating the employee experience. It’s time to view contact center conversations not just as interactions, but as a rich data set to be mined and used to empower agents and achieve outcomes.
With many agents taking up to 50 calls a day, most contact centers process thousands of conversations a week. And each interaction contains valuable clues as to how to build best practices, serve customers more effectively, improve CX, and make life better for agents.
Captured and analyzed in the right way, voice data can be combined with text-based data (such as email, SMS and chatbots) to offer a 360-degree view of an interaction, including guidance around CX and customer sentiment. Crucially, it provides key pointers for agents to improve or build on the service they’re offering – and helps team leaders to recognize and reward high performers.
Many CX professionals already understand the opportunity – Speechmatics’ research has found that 91% of contact center companies believe capturing voice data would be valuable for their organization. But, as it stands, there’s an invisible drawbridge between the reams of voice data recordings contact centers are able to store and collect, and their ability to leverage value from them.
It’s a widely cited stat that just 3% of customer calls are currently analyzed by contact centers – meaning that data is often 'trapped' in audio files which are unwieldy to digest and understand. Many contact centers are therefore sitting on a seam of gold that they’re unable to mine effectively.
So, how can contact centers bridge this gap?
The latest next-generation tools – such as speech recognition, natural language processing and speech analytics – offer a solution by transforming voice data into text so that interactions between customers and agents can be analyzed more easily.
For businesses which can capture, store and analyze them in the right way, customer calls are a mine of information – offering guidance on how to improve customer processes, how to boost NPS scores, and how customers are feeling and why it matters. They can also help reveal recurring business issues that need addressing, where the business could drive efficiencies, where contact center agents are struggling to resolve issues, which agents need further guidance and support – and which agents are high performers, as well as the secrets of their success.
Using voice technology transforms the contact center experience. A better picture of the customer and what makes them tick leads to an improved contact center customer experience. Better analysis of the agent experience to ensure they are happy and well-trained means an improved contact center agent experience – and, in turn, even better customer service. A win-win situation.
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