How to Optimize Your Speech Analytics Operations

How to Optimize Your Speech Analytics Operations

2 min read

Speech Analytics cuts the costs and complexities of determining implications revealed from customer interactions. It does so in a way similar to business intelligence and analytics, which determine parameters from real-time data and the database history.

However, speech analytics needs sophisticated Speech Recognition tools and audio data-mining to come up with information that helps think more with relatively less call time, in spite of expanding user bases. While the technology is still new, users need to know the standard output and how to nurture it over a period.

Fundamentally, a Speech Analytics solution should deliver:

  • Reduced complexity and cost of agent and service-quality monitoring
  • Lower call-handling times and better responsiveness
  • Easier compliance with consumer-protection regulations
  • Better, less tedious customer experience

How organizations must act to optimize Speech Analytics

When a company finds access to speech analytics, it must consider the scope, of transforming many tedious processes into voice-based ones. Using the technology to understand the human language, machines can trigger well-defined processes and turn them from cumbersome to productive.

Organizations can speed up operations and limit errors. However, they may need to realign services for a leaner voice-based workflow. Using speech-based interaction, companies can make operations more pleasant for customers. When customers call, their interactions need not require many of the current steps which humans or press-button menus require. Voice-biometric authentication and machine-based interaction are some examples.

Criteria to ensure optimization of speech analytics operations:

  • Leaner, cheaper: To ensure perfect working machine intelligence to respond to calls, at the organization end, machines have to pick up ‘what is being said’ and apply the knowledge to if-then scenarios. A cloud-based and software-as-a-service model will be useful for companies, as they need to adapt to new workflows and expand the ability to execute faster.
  • Accountability and transparency: Contact centers need better, non-intrusive monitoring capabilities besides insights from speech analytics. It is what will help relate customer sentiments to agent performance, transparently, and make sense of other market parameters holistically. It can improve the measures for accountability and performance management.
  • Scientific training methods: Speech analytics offers opportunities to develop skills scientifically. Using data-mining techniques, a company can easily conduct focused training sessions to improve their agents’ skills and simulate strategies. Content for such sessions is easier to build for managers with speech analytics technology, throwing a rich mix of customer data.
  • Service perfection in large consumer bases: Customer services are the main support for the markets. However, it is important to look beyond just costs and analytical efficiency. Speech analytics provides the scope for automation in unexpected ways. To uphold the brand image, occasional errors are not affordable. They can stem from poor workflow management or occasional mistakes, which are imminent when dealing with large consumer bases.

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