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Over the past several years, Contact Center teams have made significant advancements in QA automation with rules-based automation. According to Gartner, over 59% of customer service leaders prioritize improving or automating inefficient processes like manual QA1, mostly through rules-based automation. While contact center leaders understand the potential of Gen AI to improve many contact center workflows, and in particular QA processes, the landscape is clouded by the many vendors who claim to utilize generative AI to solve for QA automation –because, well, who isn’t claiming it? But, when you peel back the layers, which companies out there are truly implementing generative AI solutions in the right way, for the right reasons? It may be difficult to comb through all the noise, but if a vendor’s Gen AI solution isn’t solving for 100% QA coverage with high accuracy, then it’s likely they’re just jumping on the Gen AI bandwagon. The proof of the pudding is always in the eating, so let me walk you through exactly how Observe.AI’s is made – with a dash of precision controls, a sprinkle of predefined intents and a mound of generative AI – these carefully selected ingredients make up a whole lot of QA coverage with extremely high accuracy.
A Moment for every insight
Moments are the critical building blocks of the Observe.AI Conversation Intelligence Platform. Moments help our customers capture instances within their calls that are important to their business, and that fit specific parameters. Moments help users find an intent in a call, find that intent repeatedly, and get to a point where they can track it on 100% of their calls.
Observe.AI supports multiple types of Moments that Contact Center teams can use to get the specific insights they need. For highly precise insights, they can use Keyword Moments with operators that string together multiple individual Moment blocks. They can also use a predefined set of Moments to get started quickly. Choose a Moment that fits your need:
- Keyword Moment - a simple, yet precise Moment that uses keywords or phrases to define what a user wants to locate within an interaction (eg: Phrase for a Greeting Moment - “Thank you for calling <company name>.”)
- Predefined Moments—Ready-to-use moments that significantly reduce the time and effort required to configure a moment, whether it is associated with compliance or agent performance. Best for use cases relating to agent performance, agent behavior, agent speech quality or tracking customer sentiment.
- Advanced Moments - Keyword Moment and/or predefined Moments, strung together by one or more operators (AND/OR/NEAR/BEFORE/AFTER). Best for cases where a customer needs granular controls as to where the Moment should be flagged. For example, for a Greeting End Moment, a user can configure certain keywords to be present and not present and set that Moment to trigger only in the last 30 seconds of an interaction.
In addition to deterministic Moments based on an organization’s definition, Observe.AI can also suggest Moments based on unsupervised learning and generative AI. For example, a defect on a newly launched product that was not previously tracked can become a Suggested Moment, giving users the autonomy to decide whether or not this trend is worth creating into a Moment.
While these approaches to conversation intelligence could unlock precise yet narrow insights, deriving insights based on complex reasoning and broader subjective questions has remained a key challenge for various vendors.
Introducing Gen AI Moments for faster time-to-value and and higher automation
Now, using the full power of generative AI, we are launching Gen AI Moments. Users can easily define a Gen AI Moment bespoke to their organization’s definition, by utilizing natural language prompting that looks for characteristics of agent or customer behavior, validate process and procedures and ultimately, get Contact Center teams the answers they need in the quickest way possible. Gen AI Moments increases coverage for Auto QA use cases with high out-of-the-box accuracy and controls for end users to iterate upon. With Gen AI Moments users can easily address questions that are subjective or conversational. For example:
- Agent Behaviors - “Did the agent respond appropriately to customer objections?” or “Did the agent ask probing questions to understand the customer’s issue?”
- Conversational - Customer describes their lifestyle so agent can determine what financial product will best fit their profile or Customer describes the issue they're running into with a product or service so agent can best determine how to resolve the issue.
Using natural language users can effortlessly set up the Gen AI Moment and quickly verify its accuracy before launch. This validation process emphasizes trust and transparency, enabling users to confidently apply the results across all conversations.
With Gen AI Moments, Observe.AI eliminates the complexity of setup and maintenance for faster Time to Value (TTV) and continually fosters human-centered feedback to improve Gen AI accuracy and relevance. Contact Centers can now leverage the full power of generative AI to fully automate 100% of their QA questions with full transparency and control and no longer have to settle for slow and complex implementations using just rules-based automation that may be incomplete or inaccurate. Now, that’s a recipe for success!
Experience the power of Observe.AI Gen AI Moments. To learn how, request a demo.
1. https://customerthink.com/15-statistics-that-forecast-the-future-of-the-contact-center/
