Analytics Suite

The Analytics Suite provides visibility into how Relational Agents perform across projects. It combines usage statistics with qualitative signals, allowing teams to evaluate both the efficiency of conversations and the outcomes they generate. Analytics can be viewed globally at the project level or drilled down to individual agents, making it possible to track overall adoption while also refining specific experiences.

At the highest level, analytics show how users move through the conversational funnel — from entry into a session through to completion. This helps identify points of friction where users disengage or abandon the process.

Filters

At the top of the Analytics Suite you can narrow down the data using the following filters:

From / To

Set a custom date range for the data you want to view.

Time Period

Choose a preset range such as Last 30 days.

Select Agent

View data for all agents or focus on a specific one.

Filter by User

View data across all users or filter by a specific one.

Core Metrics

  • Conversations – visualises the conversational throughput of all sessions by length (short, medium, long) with lifetime totals.

  • Users Total – active user counts including monthly, weekly, and daily active users.

  • Total Conversation Length – cumulative duration of all conversations.

  • Average Conversation Length – average duration per conversation.

  • Average Turns per Session – average number of interaction turns within a session.

  • Relational Metrics – qualitative measures such as interaction style matching, relationship building, user engagement, empowerment, and task resolution.

  • Business Metrics – purpose fulfillment rate and business process adherence.

  • Evaluation Metrics – results from evaluations run across conversations. Filter by evaluation type and view all evaluations or select a specific one.

  • Interactive Content Metrics – counts of structured actions such as forms filled, images displayed, or web views opened.

  • Conversations – a table listing individual conversations and a summary of the interaction.

These metrics allow product teams to continuously improve agents: reducing drop-off, shortening resolution times, and optimising relational quality. They also provide evidence for business leaders on how relational intelligence contributes directly to KPIs like customer satisfaction, efficiency, and conversion.