What is the Conversation Monitoring
Conversational agents such as voice-powered bots are becoming a prominent channel of interaction in customer service and similar domains.
Even though the technology is making an impact in different industry use-cases, some customer interactions aren’t so smooth, because of multiple factors such as background noise, insufficient knowledge of the conversational agent, a hard to understand the accent, etc.
It’s as essential to keep an eye on these conversational agents as it is to implement them. Conversation monitoring is similar to infrastructure monitoring; the only difference is, it’s automatically analyzing the root cause of performance issues in the conversations being handled by the voice-bot.
The traditional way of monitoring the conversations in a contact center is to employ audit and quality teams, where these teams look at a small set of calls handled by human agents.
In the new setup, where an automated agent is attending the calls, it’s an essential and hard problem to ensure that the conversations are still happening as per the instructions/guidelines.
Going by the old way, you would need an army of human agents just validating if the automated agent is doing good or not, which isn’t a cost-effective solution. And that’s why we have been working on a cheaper and effective way to tackle this problem.
Our conversation monitoring technology identifies off-the-mark conversations in real-time, which can be used to seamlessly transfer the calls to a human-agent if possible, OR flags this particular conversation so that learnings can be utilised, resulting in better performance in the next set of calls without human intervention.
The technology works by continuously monitoring an ongoing call and classifying it as a good or bad conversation based on over 70 conversation-level features. It generates a score for a call where a score of 0 means the call went pretty bad, and a score of 100 means the call went pretty good.
Identifying bad conversations is crucial since disappointing customer service may hamper customer loyalty and impact the brand image. Our conversation monitoring technology detects such communications using multiple behavioral cues in a user-bot interaction.
This technology helps us catch these instances early on before it can make a significant impact and help us ensure that our customers have the best experience, and the voice-bot is getting the maximum possible satisfaction score, even when we sleep.