Artificial Intelligence in the Contact Center

Series title image

October 22, 2019

Tags:
Clarabridge Analytics
Contact Center
Artificial Intelligence
Interaction Analytics
Natural Language Processing

Machine learning and related artificial intelligence techniques are useless without data. Training these advanced algorithms for business use cases requires large data sets that are representative of your business, your industry and your customers’ challenges. The Contact Center is a natural source of such data; Contact Centers can use these technologies to improve contact center efficiencies and to build an intelligence hub that empowers the entire organization to improve customer experience. Some compelling examples of how AI-powered technologies can transform the contact center and the entire organization are enumerated below:

Understanding Customer Intent

Your customers may contact you for a multitude of reasons. Some of these reasons are fairly straightforward, others are intricate, yet, rarely are they entirely novel. With the immense data that you are collecting with every recorded phone call, chat interaction and email, you have a strategic asset that can be used to train machine learning models to understand customer intent within conversations. Once you understand true customer motivations, you can then leverage these AI-powered outputs to optimize interactions such as smarter routing of your customers for the best possible outcome, potential upsell and cross-sell suggestions for your agents based on intent and past history, or flagging interactions for fraud and compliance risk. 

Understanding Customer Effort

Customer effort is a leading indicator of loyalty. Analyzing customer effort can guide companies in identifying emerging issues before they explode into major issues. Traditionally, effort has been quantified through structured questions on a survey. However, AI and machine learning techniques combined with text analytics and Natural Language Processing capabilities can aid in evaluating the level of effort expressed in any piece of unstructured customer feedback. By interpreting word choice and sentence structure, you can quickly understand which aspects of the customer experience cause friction in any feedback source – not just in surveys.  

Offering up the “Next Best Action”

Once you have a good understanding of customer intent and customer effort, you can create an interaction history for each customer. By analyzing all of their feedback, you can create a personalized profile that includes summaries of customer sentiment, interests, and their expectations from your brand. When you combine this rich interaction feedback with other metadata such as purchase or claims history, customer lifetime value, and customer demographics, you develop a clearer picture of the customer’s motivations and decision-making process. Together, the qualitative and quantitative data can be used to train machine learning algorithms to predict a “next best action” for each customer. This recommended action could be as simple as proactive outreach to a disgruntled customer that is predicted to churn or more complicated like offering an exact discount or promotion code that will have the most likelihood of resulting in a new sale.

Other Articles in This Series:

Quick Tips to Improve Your Customer Experience Management
Published November 14, 2019

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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.