Putting Customer Conversations to Work in the Call Centre

holding yellow speech bubble

Filed under - Industry Insights,

Customers have conversations with more than one department in your organisation during the customer lifecycle journey.

Once they move out of sales, they can interact with individuals in service, support, and even your billing departments.

According to Ventana Research, the majority of these interactions take place via voice conversations, but other communication channels are gaining popularity at a rapid pace.

It should come as no secret that many organisations rely on speech analytics to analyse and interpret customer conversations.

In fact, according to Ventana’s new research Putting Customer Conversations to Work:

  • 1 in 5 organisations use speech analytics technology
  • An additional 36% plan to use it in the next two years
  • The top 3 reasons for using new analytics has to do with customers

Speech analytics analyses conversations across various touch points and uncovers trends that organisations might not be able to detect otherwise due to time and resource constraints.

Artificial Intelligence and Machine Learning Trends

As artificial intelligence (AI) and machine learning (ML) continue to see growth in technology advances, speech analytics grows in value along with it.

By combining speech analytics with AI/ML, companies can:

  • Improve first-call resolution rates
  • Automate cross-sells and upsells to customers
  • Predict customer sentiment
  • Uncover customers that need additional attention
  • Automate the analytical process and review

AI already has an impact on customer experience and will continue to drive more meaningful change as the technology matures.

Today about 20% of call centres in the US use speech analytics to analyse their customer interactions, surfacing intelligence such as customer preferences and agent performance, and predicting outcomes such as a customer’s likelihood to cancel service or make a purchase.

Speech analytics is fuelled by speech recognition which has greatly improved in accuracy in recent years due to AI and the use of deep neural networks (systems that can continually improve the language models that convert speech to text). By using such solutions, organisations are improving the performance of their agents, handling calls more efficiently, maintaining compliance, and driving more revenue.

A core capability of speech analytics solutions is automatically categorising calls as containing specific behaviours or events. This automated tagging creates maps or blueprints for customer interactions, which will continue to become more detailed over time, identifying the optimal path to a desired outcome for any given type of interaction.

Voice conversations may soon meet their match, but the voice of the customer will always be present and require attention. Organisations that want to create a better experience for customers need to start by listening to customer conversations today.

The report Putting Customer Conversations to Work highlights the importance of analysing customer interactions and why organisations can’t afford to wait. Read the full report from Ventana Report to fully understand the significance of speech analytics and the rise of artificial intelligence and machine learning.

This blog post has been re-published by kind permission of CallMiner – View the original post

To find out more about CallMiner, visit their website.

About CallMiner

CallMiner CallMiner is the leading cloud-based customer interaction analytics solution for extracting business intelligence and improving agent performance across all contact channels.

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Published On: 3rd Sep 2018 - Last modified: 3rd Jan 2020
Read more about - Industry Insights,

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