Call centers today are interested in using AI-powered solutions to improve customer experience. Reason? With a sea of contact center data at hand, large contact centers are using AI-powered insights to understand agent and customer behavior and foresee trends to develop appropriate performance improvement programs for agents.
Benefits of AI-powered insights in contact centers
Machine learning technologies like Natural Language Understanding (NLU) are fast, accurate, and get better with each dataset. So, when given a job of analyzing large volumes of support conversations, they not only offer better insights, but also get faster and more accurate while processing the next set of data. These capabilities will help contact center leaders uncover never-seen-before insights about their contact center, which in turn will help them optimize their team’s key performance metrics such as the average handling time (AHT), first resolution time, and CSAT scores. But, why aren’t we seeing a lot of large consumer companies and enterprises use these technologies? What’s stopping them from adopting these technologies? There are two main challenges.
The following are the challenges that prevent call centers from leveraging AI-powered Insights.
Data too large to comprehend
The data present in contact centers has millions of conversations and thousands of data points that are impossible to make sense of without AI-powered Insights. And the tools used by companies today aren’t equipped enough to handle data at this scale. Reporting and analytics platforms today don’t offer anything beyond surface-level metrics. Organizations need a powerful AI-powered analytics solution to scour through the entire sea of contact center data, correlate the relationship between data points, and extract meaningful insights.
Lack of a unified data platform
Omnichannel contact centers are siloed. Companies often use multiple tools and custom apps to handle support conversations for different channels (email, chat, social, SMS, WhatsApp, etc.), resulting in the distribution of contact center data across multiple tools and platforms.This makes it hard for contact centers to understand and comprehend agent performance or customer support trends for a given period. Contact centers today need an AI-powered Insights solution that pulls key contact center information from different tools in the support stack and makes sense of them. With platforms like Level AI, call centers can overcome these challenges.
How AI-powered insights can help contact centers?
Identify conversations that matter
QA teams often look at conversations that exhibit unique characteristics (conversations that exhibit positive and negative behavior). But today, the chances of stumbling upon such conversations is purely accidental. But, identifying these conversations are crucial as the insights from these conversations are valuable in understanding agent performance and the current state of your company’s support quality.
InstaReview in Level AI analyzes all the support conversations for positive and negative behavior and picks out the outlier conversations which might be of interest for your QA teams to review.This will help your contact center in two ways: one, it will ease the pain of the Quality Analysts. And two, it will help contact center leaders like you to effectively identify gaps in customer experience.
Apart from Instareview, Level AI also comes with automatic categorization of support issues based on the key conversation points used by the agents and customers that our semantic intelligence engine can recognize. Our semantic intelligence engine eliminates the QA team’s need to manually categorize issues. This capability also extends to the analytics dashboard – you can quickly pull up a report to see which issue categories are trending for the month and plan accordingly.
Automatically grade agent performance
Level AI uses Instascore as its AI-powered Insights to grade parts of a support conversation automatically. Our semantic intelligence engine grades rubric questions by collating agent performance metrics from conversation tags, metric tags, and predefined machine learning models, and provides a quality score for a conversation on a scale of ten.
Since Instascore already grades a part of the conversation, it makes it easy for QAs to grade the rest of the conversation. The sum of Instascore and the score from manually evaluated questions make up the total QA score of a conversation giving AI-powered Insights.
A unified coaching interface to analyze and track agent progress
The agent coaching module in Level AI is designed to help quality coaches plan and implement the learning journey of agents. With agent coaching, coaches can analyze agent performance, document their feedback, set goals, and track agent progress against goals. The AI-powered Insights coaching dashboard allows coaches to identify the right conversations by letting them filter conversations based on category-wise and section-wise QA scores, Instascore, along with a range of other filters.
The three features, when used effectively, can help your quality teams achieve amazing results in terms of performance improvement, CSAT score, and the overall customer experience. But, there’s more. Level AI has every possible tool you can think of to streamline your company’s quality assurance process. Check out our website to know more.
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