Introducing the Observe.AI Intelligent Workforce Platform 💪
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With conversation intelligence and data-driven coaching programs, Company boosts transparency to help agents deliver better customer service.
A customer’s journey with a brand starts with the agent. But what happens when there are no actionable insights on how they’re performing?
For a multinational education and publishing company that has eight contact center teams distributed across four different countries, a lack of visibility into how agents were interacting with customers was contributing to DSAT scores. The Company needed a way to uncover these inefficiencies and improve performance with customer interaction insights that were data-driven, and most of all, actionable.
Before Observe.AI, the Company monitored less than 1% of calls they received daily. Contact center team leads knew call duration and average handle time (AHT) were out of range, but didn’t have an action plan to improve KPIs in a measurable way.
The agent performance evaluation process was also tedious and manual, preventing contact center teams from having a data-driven way to evaluate overall agent performance. Instead, they relied on a subjective process, manually listening to agent calls to understand performance.
With specific goals in mind, the Company leveraged Observe.AI to harness 100% visibility into customer interactions to find the root causes behind customer sentiment and DSAT scores.
With the Observe.AI platform in place, the Company immediately got visibility into 100% of its customer interactions. One feature within the Observe.AI platform called ‘Moments,’ or AI-powered interaction monitoring, allows the Company to track key points of interest on calls that offer signals on agent performance as it relates to customer satisfaction, process adherence, call drivers, compliance, and more.
Moments also enable the Company’s contact center teams to hone in on nuances of interactions with sentiment analysis, which sheds light on the tonality of a speaker towards a product, person, topic, or event. Sentiment analysis allows the Company to analyze customer and agent interactions on a deeper level by automatically detecting negative or neutral interactions on calls.
As an example, the Company’s contact center teams created a ‘Communication Gap’ Moment, which specifically tracks negative sentiment to flag miscommunication between an agent and the customer. 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.
With Built-In Evaluation Forms, the Company goes beyond scoring calls. Their contact center teams now have the ability to listen to a call, read the transcript, and deliver contextual, actionable feedback to agents in a single window view.
With Observe.AI’s Agent Performance & Coaching capabilities, the Company’s contact center teams identify outliers in performance, surface opportunities to celebrate great performance, and correct or clarify based on real data.
The Company uses the Leaderboard feature to identify the top and bottom performing agents across their team with the Agent Performance Dashboard, reviewing interactions that are driving poor CX and bookmarking specific interactions to address in coaching sessions.
The Company’s coaches then prepare, document and track coaching feedback effectively, ensuring agents have the information they need to improve performance, backed by data.
With Observe.AI’s newest QM Reporting feature and the upcoming release of Auto-QA, the Company will continue to provide deeper, more impactful coaching to impact their CSAT and DSAT scores, leveraging sentiment insights to provide a data-backed way of defining these metrics for the first time.
