Getting the Most From Speech and Text Analytics

Speech and text analytics concept with magnifying glass and speech bubble

With contact centre traffic on an ever-rising trajectory, CX and service leaders are caught between a rock and a hard place: keep recruiting or watch service levels degrade. Neither of which are desirable nor sustainable.

Jas Bansal at Kerv Experience explains six top ways speech and text analytics can fill the void, while extracting valuable meaning and learning from every interaction.

Scaling QM in Ways Not Humanly Possible

No organisation could reasonably justify the numbers of quality management (QM) personnel needed to carefully listen to and review every call, email, text, or chat message traversing its contact centres.

Powered by AI and natural language processing (NLP), speech and text analytics bridge the gap by automating interactions and minutely examining customer-agent conversations.

Creating QM meaning and actionable insight from mountains of otherwise unstructured information – more cost effectively than humans ever could.

Why Consider Speech and Text Analytics?

Among myriad reasons for contact centres investing in speech and text analytics are:

  • Better Agent Performance – lowering average handle time, increasing first contact resolution, improving sales conversion, and closing training gaps.
  • Better Customer Journeys – identifying and addressing areas of high customer effort along with communication issues, process disconnects, and hidden frustrations.
  • Better Compliance Monitoring – ensuring adherence to scripts and policies, and timely and appropriate escalations.
  • Better Planning – spotting trends and predicting future customer behaviours and needs.

Set up correctly, these tools work hand-in-hand with QM processes. The fundamental difference being random manual call sampling methods – which only tend to capture less than 2% of all interactions – are replaced by comprehensive real-time analysis of every individual interaction, improving accuracy, minimising bias, and ensuring compliance at every step.

Picking the Right Use Cases

Importantly, speech and text analytics form a key pillar for the digital-first, data-driven contact centre.

Not least because they enable managers and team leaders to better visualise what’s really happening during conversations and thus transform decision-making, internal processes, and service improvements.

Here are six practical use case examples:

  1. Automated Transcription – Get complete, accurate transcriptions of every voice and digital conversation.  Uncover customer pain points and unmet needs along with evidence of agent performance.
  2. Empathy Analysis – Assess empathy and emotional intelligence levels exhibited by agents.  Assign scores reflecting their ability to relate to the customer’s emotions and concerns.
  3. Targeted Training – Empower managers to pinpoint coaching opportunities, while improving feedback with examples of agent best practices and well-executed interactions.
  4. Interaction Overview – Easily visualise and understand customer topics and trends by agent, queue, and flow.  Search and filter data based on chosen parameters to get simple-to-use insights.
  5. Sentiment Analysis – Decipher speech or text to evaluate underlying emotional tone (positive, negative, neutral) and triggers for mood changes during the conversation.
  6. Topic Spotting – Detect topics based on predefined phrases.  For example, customer intent and keywords associated with fraud risks, and running vulnerability registers.

Hard Quantifiable Benefits

McKinsey found that organisations implementing speech analytics achieved cost savings of between 20 and 30%, customer satisfaction score improvements of 10% or more, and stronger sales.

Yet, those results could be understated. According to Opus, although 82% of organisations actively transcribed speech data, two-thirds leveraged less than half of those audio assets for business objectives.

Opportunities for CX, sales and efficiency gains increase further still when text analytics tools are factored in.

As digital tools continue to improve AI and NLP capabilities – paired with selecting the right partner – are helping businesses identify customer dissatisfaction causes and revealing opportunities to improve compliance, operational efficiency, and agent performance.

Author: Guest Author

Published On: 6th Feb 2024
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