With conversation intelligence and targeted coaching programs, Company boosts transparency to help agents deliver better customer service.
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 their performance management strategy to drive consistent and measurable improvements at the individual agent level for their global tech support team.
The company turned to Observe.AI to better understand the root cause of customer dissatisfaction, allowing 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 our best-in-class Conversation Intelligence that allows the Company to transcribe and analyze 100% of its agent-customer conversations with AI and machine learning. Observe.AI enables the Company to understand what customers are saying about the product and identify agent behaviors or attributes that are 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, we've created an easy-to-program tool that 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 Talkover 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 this call in order to increase retention.
Layering in sentiment analysis to track perceived emotion of the customer (positive or negative), allowed the Company to determine the customer’s 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 enables the Company’s teams to quickly gain contextual sentiment insights that are used to identify where agent training efforts needed to be focused.
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.
Now, the Company leverages Observe.AI to monitor quality across all conversations and rapidly evaluate agent performance in a single tool and with the assistance of automation and machine learning capabilities increasing efficiency, productivity, and accuracy. Observe.AI’s Built-In Evaluation Forms leverages Moments the Company has identified and incorporates them in agent evaluation forms, creating a harmonized workflow that gives unprecedented visibility for every agent, QA manager, and business leader.
Their contact center teams have the ability to listen to a call, read the transcript, and deliver contextual, actionable feedback to agents in 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.
With conversation insights and an optimized QA process, the Company began to drive change in agent behavior with Observe.AI’s Agent Performance and Coaching, which enables agent coaches to provide timely and targeted feedback to the agents based on results from QA evaluations.
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 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 has begun coaching agents on specific ways to handle customer objections, and can event test out different promotions/offers and take 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 overall performance of its agents, along with a 17% improvement in AHT for teams measured by efficiency metrics.
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 their business strategy.
With the use of Auto QA, the Company will be empowered to not only capture and analyze 100% of their 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.