AI Predictive Analytics for Customer and Business Insights

Most CSAT programs only get feedback from the customers who take the time to respond, and this group tends to consist of both the most satisfied and the most angry customers. All those in the middle, including customers who are quietly frustrated and therefore choose not to respond, fail to appear in the data. Convozen eliminates this gap by directly working out satisfaction, escalation risk, and compliance risk from each conversation in real time, without having to wait for a survey.

How Convozen Infers Outcomes From Every Conversation

Convozen’s AI Analysts for Leadership layer reviews 100% of voice, email, WhatsApp chat conversations and infers risk and outcome signals directly from the interaction, not from a post-call form.

It functions reliably since the inference is based on a correctly transcribed transcript. The speech-to-text model called Akshara, which is trained on more than 50,000 hours of real telephone audio and further refined using over 4,000 hours of manually annotated data, can accurately capture the tone and word choice when a mood or escalation signal is present, even in the presence of background noise, accents, and code-switching, conditions in which a generic model’s transcript would already be degraded before any inference takes place.

Turning an Inferred Signal Into an Action

A signal only has any significance if there’s a subsequent event. Since Agent Assist displays mood insights and a live customer summary during the call, the agent is able to make adjustments before a conversation which is moving towards frustration actually escalates. Convozen’s Automated Quality Assurance component checks each conversation against the relevant SOPs and business-specific parameters, enabling the increase in inferred dissatisfaction to be linked to a particular issue rather than leaving it as an unexplained figure. Furthermore, AI Coaching converts the patterns found in the scored calls into automated peer coaching, thus completing the process from signal to solution.

The automation and event triggers used by Cars24 on Convozen automatically convert key moments into follow-ups, tags, escalations, and disposition updates, and this process is supported by live dashboards that show performance at the representative level, which is how the inference-to-action loop appears in practice rather than in theory.

Where This Applies

• Customer support and contact centres: satisfaction levels and escalation risks are determined from every single call, not just from a sample of surveys
• In the field of banking and financial services, compliance and violation risks were identified in all of the regulated conversations.
• E-commerce and retail. Throughout the sales conversations, repeated objections and friction patterns appeared.
• Insurance and edtech. The Voice of Customer themes gathered from each interaction are used to inform changes to policy and process.

Why Full-Coverage Inference Changes the Picture

Every conversation counts as a data point, not only the more prominent ones.

• It is real-time, not retrospective. The signals relating to mood and escalation reach the agent during the ongoing call.
• Not just the score, but the root cause. The SOP and QA scoring process traces the inferred risk signal back to the actual issue that caused the failure.
• Based on accurate transcripts. This is because the speech recognition used is of telephony quality, and thus the inference is based on a transcript that accurately reproduces what was said.

Turn Every Conversation Into a Signal with ConvoZen.AI

You can have Convozen determine satisfaction, escalation, and compliance risk based on your own conversations, without having to carry out any surveys. Book a Demo