How an F500 Education Company Is Combatting Churn with Contact Center Visibility

For a Fortune 500 education company with eight contact center teams distributed across four different countries, a lack of verifiable data coupled with poor agent performance was contributing to low NPS scores. The company needed a way to pinpoint drivers of customer behavior and overhaul its performance management strategy to drive consistent and measurable improvements at the individual agent level for its global tech support team.

Gaining Actionable Insights with Conversation Intelligence

The company turned to Observe.AI to better understand the root cause of customer dissatisfaction. This allowed it to build a scaled customer experience strategy, leveraging the power of conversation intelligence to uncover hidden insights and test theories on what was contributing to low NPS scores. 

Best-in-class conversation intelligence

At the heart of Observe.AI is best-in-class conversation intelligence, which allows the company to transcribe and analyze 100% of its agent-customer conversations with AI and machine learning. Observe.AI enabled the company to understand what customers were saying about the product and identify agent behaviors or attributes contributing to the student experience.

Observe.AI’s moment builder

Observe.AI’s moment builder is a low-code speech analytics query engine that allows users to build and uncover insights from their interactions at scale. By leveraging the power of NLP and AI, this easy-to-program tool allows users to capture language insights with semantic and syntactic tools to help along the way.

‍

‍

The company began to monitor “moments,” including customer uncertainty, follow-up, agent talk over time, client desire to talk, and professionalism to track how agents were handling enrollment requests. With the follow-up moment, the company began to track how often agents followed up with students, what words they were using, and what tonality they used in calls to increase retention.

Layering in sentiment analysis to track the perceived emotion of the customer (positive or negative) allowed the company to determine the customers’ opinion towards a product, person, topic, or event.

‍

The company’s contact center leaders used this to create a “communication gap” moment, which specifically tracks negative sentiment layered on top of other key indicators of negative language, such as legal threats or payment language programmed into the moment. This enabled the company’s teams to quickly gain contextual sentiment insights that are used to identify where agent training efforts needed to be focused.

Unifying a Global QA Strategy

Distributed contact centers meant that each team had its own QA process, its own management style, and no consistency—not to mention manual and subjective. With just a handful of calls being reviewed and scored per month and no accountability across its agents, the company used Observe.AI to overhaul its QA process. 

Agent evaluations at scale

Now, the company leverages Observe.AI to monitor quality across all conversations and rapidly evaluate agent performance in a single tool, increasing efficiency, productivity, and accuracy. Observe.AI’s built-in evaluation forms leverage moments the company has identified and incorporated in agent evaluation forms, creating a harmonized workflow that gives unprecedented visibility for every agent, QA manager, and business leader.

‍

Its contact center teams have the ability to listen to a call, read the transcript, and deliver contextual, actionable feedback to agents using a single pane of glass, giving the company a line of sight into every conversation so it can prioritize the right conversations for review, while also focusing on driving teamwide consistency with verifiable QA data.

With a standardized QA program, the company increased its QA efficiency by more than 200% within just thirty days and began programs to drive churn-reducing agent behavior. 

‍

Driving Behavior Change for Low Performers

With conversation insights and an optimized QA process, the company began to drive change in agent behavior with Post-interaction AI agent performance and coaching, which enables agent coaches to provide timely and targeted feedback to the agents based on results from QA evaluations.

Targeted and contextual coaching for every agent

First, the company uses the team dashboard, which is populated by QA evaluation scores, to automatically identify top and bottom performers to prioritize coaching for agents that need the most guidance. This dashboard provides a curated set of metrics that help supervisors effectively monitor the performance of their entire team at the high level, as well as the individual-level view, providing contextual insight into overall performance. This is the value of an efficient QA process: More QA evaluation data means more visibility into coaching opportunities.

‍

Automated coaching recommendations

Coaches were now able to target bottom performers and create highly relevant coaching sessions within the platform. Observe.AI’s automated recommendations surfaced the skill, behavior, or knowledge-related gaps detected on QA evaluations so contact center leaders could make their coaching more focused and targeted.

With each coaching session, agents are given feedback connected to referenceable moments in the conversations captured by the platform. Now, the company event tests out different promotions/offers and takes those learnings back to marketing teams to inform improvements on offerings and services.

By delivering 155% more targeted coaching sessions, the company has seen a 49% reduction in agent fails and a 10% improvement in the overall performance of its agents, along with a 17% improvement in AHT for teams measured by efficiency metrics.

Looking Ahead

The company continues to iterate on its agent performance improvement strategy in an effort to reduce subscriber loss and increase customer satisfaction by leveraging conversation intelligence to analyze customer interactions and pinpoint data-driven evidence that can shape its business strategy.

With the use of auto QA, the company will be empowered to not only capture and analyze 100% of its interactions but also automatically QA score them based on AI-driven evidence, allowing them to spot every churn risk, underperforming agent, or improvement opportunity in order to foster a culture of continuous improvement.

‍