Glossary

Speech analytics

What is speech analytics?

Speech analytics refers to the analysis of voice-based conversations for key words and phrases with the goal of extracting meaningful data to help make informed business decisions and provide quality assurance.

Speech analytics are collected using speech recognition software that use artificial intelligence (AI) and natural language processing (NLP) to “listen” to conversations and convert them into text-based transcripts. Speech analytics software can then take these transcripts and analyze what was said, who said it, the sentiment behind statements (known as sentiment analysis) and index this information in a way that is accessible to an organization.

There are three steps involved in converting unstructured data from the audio of recorded calls into structured data that can be analyzed:

  1. Incorporating metadata: It is not only what was said that is important to this process but which agent was involved, when the conversation occurred and customer details. This additional information is called metadata.
  2. Speech recognition: Using AI and NLP, grammar, syntax, structure and composition of speech is analyzed to process what is being said and convert the conversation into a text transcript.
  3. Analysis: The conversations are analyzed for patterns and the information is relayed in a format that is actionable to the organization.

Benefits of speech analytics

Speech patterns such as long pauses, voice inflection or repeated words reveal critical “moments of truth” between customers and brands.

Other benefits of speech analytics include:

  • Refining customer service agent performance: Speech analytics help to identify where an agent’s performance might reflect gaps in their training. Alternatively, it can offer a chance to identify the key attributes of a successful agent.
  • Optimizing talk scripts: By listening to all customer interactions, speech analytics can help identify when agents regularly deviate from a given script and can track the resulting customer outcome. If it’s a more positive outcome for the customer and/or the business, then adjustments to the talk script can be made to reflect the new language.
  • Improve sales: The differences between every successful and non-successful sales call can be analyzed. For example, looking at what phrases were associated with the successful sale or upsell, where in the call the sale occurred or what words or phrases were counter-productive. The terms, phrases, flow of the calls and timing of the ask between top-performing and low-performing sales agents can be compared to uncover best practices.
  • Ensure compliance: By monitoring for certain keywords or phrases in every call — or the absence of certain phrases — it is possible to zero in on the calls most likely to be in non-compliance.
  • Improve customer experience: Companies can turn voice-based conversations into an encyclopedia of knowledge about their customers’ likes, dislikes and pain points, which can be used to improve their customer’s overall experience.

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