3 Uses of Artificial Intelligence in Interaction Analytics

Clarabridge series title image

By: Shorit Ghosh

September 1, 2020

Clarabridge Analytics
Contact Center
Artificial Intelligence

Artificial Intelligence has paved the way for innovative interaction analytics features that could reflect a human’s understanding of language and automate processes to free users to do higher order analysis. In this blog, we share three ways artificial intelligence can be used in interaction analytics. 

AI-augmented analytics can predict outcomes

Organizations are finally in a position to use AI to identify predictors of certain outcomes. This ability is possible for three reasons:

  • Companies have access to billions of data points and customer feedback records, most of which are available from voice and text.
  • Technological advancements in cloud computing capacity mean that it’s possible to store huge amount of data.
  • AI-powered technology exists to analyze complex data in near real time and present “actionable” insights to various stakeholders.

Today, artificial intelligence and machine learning algorithms can ingest all of an organization’s data, analyze it and quickly return actionable insights. An innovative application of this technology involves identifying predictors of certain outcomes related to customer service operations, such as low agent ratings. By applying data selection and model training, the proper software can conduct multivariable analysis to discover the drivers of this outcome. In another example, you may want to improve operational efficiency by understanding what drives long calls or high silence in your contact center. 

Ultimately, this capability makes it possible to transform your customer service operations over time and ensure you’re fully leveraging the information that’s available.

Natural Language Understanding (NLU) modeling to enhance Customer Service

One of the most significant advances in artificial intelligence has been in the field of Natural Language Understanding (NLU). NLU goes beyond traditional speech analytics to interpret sentiment, effort, customer intent, emotion type and emotional intensity. These measures work by analyzing millions of interactions between customers and agents across a variety of industries to determine accurate results, ultimately offering a deeply nuanced understanding of your customers. 

NLU enrichments can be applied to Customer Service in a variety of ways: 

  • They allow users to prioritize cases in which customers have had especially intense emotional interactions and route them to agents who are especially skilled at demonstrating empathy.
  • Companies can also create contact lists of customers whose interactions fall below a certain threshold for measures such as sentiment (marking them as candidates for churn) and set up a proactive service recovery program that will help improve customer loyalty.
  • Organizations can identify primary contact drivers to determine why customers are contacting their company and use the information to create resources and engagements to address the root causes of these drivers.

AI-augmented analytics to refine your digital bots and workflows

As customer service increasingly occurs on digital channels such as social media, messaging apps, live chat and SMS, organizations must take measures to create a consistent customer experience across these platforms. 

To support these interactions, AI-augmented analytics can be used to get a better understanding of the contact trends that occur on digital channels as well as more traditional channels like phone calls. 

By gaining a deep understanding of these contact trends, companies can automate agent workflows and create response scripts to improve agent and bots’ ability to respond effectively, thereby increasing digital containment without sacrificing customer experience. 

Information about why a customer couldn’t perform an action on digital channels and how the digital customer experience compares to the customer experience on other channels also informs other digital strategy initiatives and creates effective digital customer service that is characterized by quick, high quality interactions that are continuously optimized for the latest trends and events.

Other Articles in This Series:

Why Customer Experience Management Is Vital for Your Organization
Published October 10, 2020

Using Speech Analytics to Maximize Your Service Levels
Published August  1, 2020

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About the Author:
Shorit Ghosh is the Vice President of North America Services at Clarabridge. Shorit manages a team of consulting managers, business consultants and technical architects to help his customers improve their own customer experience, increase revenue, and reduce cost and churn.