From Conversations to Conversions

AI agents are no longer demos or side experiments. They are core product surfaces — booking flights, resolving issues, onboarding users, closing sales, and handling sensitive workflows.

Yet most product managers building conversational AI agents face a familiar problem:

We know users are talking to the agent — but we don’t know how users are experiencing the interaction.

When you use traditional analytics tool in web or mobile apps - where the UX flow is predefined, its easy to measure and baseline how users interact with your app. But user interactions within a conversational interface is not predefined, traditional analytics cannot help measure engagement and conversion within human-AI interactions. This is where AI-native analytics — and Mindlytics — come in.

In this post, we’ll walk through:

• A concrete AI agent use case
• The user intents that actually matter
• The KPIs product managers should track
• How Mindlytics’ visualization portal turns raw conversations into actionable insight

The Use Case: An Airline “Smart Concierge” AI Agent

Imagine you’re a product manager responsible for an Airline Concierge AI Agent used across web, mobile, and messaging channels.

The agent handles:

• Flight booking & modifications
• Seat upgrades
• Baggage questions
• Delays, cancellations, and rebooking
• Loyalty program inquiries

From a business perspective, this agent is expected to:

• Reduce call center volume
• Increase self-service completion
• Improve customer satisfaction
• Drive ancillary revenue (upgrades, add-ons)
• Loyalty program inquiries

From a PM perspective, the core question is simple:

Are users accomplishing what they came here to do — and are we helping them do it efficiently?

To answer that, you need to understand intent.

Step 1: Thinking in Intents (Not Events)

In conversational products, intent replaces the idea of “page views” or “button clicks" of traditional apps.
For our airline agent, some key intents might be:

• Check flight status
• Change flight
• Upgrade seat
• Add baggage
• Talk to an agent

Each user conversation may contain:

• A single intent
• Multiple intents
• A failed or abandoned intent
• An intent that changes mid-conversation

Mindlytics automatically detects and tracks these intents at the conversation layer, without requiring PMs to predefine every possible path. Mindlytics automatically extracts intents, friction signals, sentiment, and engagement patterns from raw human-AI interactions, without custom event tagging. Right there, mindlytics offloads the PM of the time consuming task of creating the analytics event schema for the app and constantly updating it as app evolves - which is expected with any app lifecycle management.

Step 2: Core KPIs Every AI Agent PM Should Track

As soon as the mindlytics SDK is instrumented, mindlytics automatically tracks all relevent conversational analytics events and also computes & presents an array of intent-centric KPIs for your app / agent. These default KPIs, also referred as foundational KPIs are immediately usable and relevant for all AI-centric applications. In addition to the foundational KPIs, you could create and configure various analysis charts to create your own metrics charts specific to your app and business.

1. Foundational KPIs

The foundational KPIs can be accessed under either Conversational KPI chart or the Intent KPI chart in the mindlytics portal. These KPI charts are auto-created but can be configured to filter the data to suit your unique needs. A few of the intent related KPIs are described below. For more exhaustic list, refer the developer portal documentation.

Intent Conversion Rate

Measures the effectiveness of your AI agent in resolving user intents.

First Intent Friction

Quantifies the responsiveness of your AI agent at the start of a conversation.

New Intents

Tracks the emergence of user intents outside the expected scope.

Intent Fulfillment Friction

Measures the level of friction encountered by the AI agent when resolving user intents.

Intent Fulfillment Sentiment

Evaluates the sentiment expressed by users during the process of intent fulfillment.

Intent Acceptance Rate

Assesses the frequency and the acceptance rate of the intent recommendations proposed by the AI agent.

Besides the foundational KPIs, you could create and configure different analysis charts to track metrics specific to your app and business. In the next sections, you will see some charts — explained using our airline agent.

2. Intent Volume & Distribution

The mindlytics Intent Distribution Chart gives product managers a real-time view into what users are actually trying to accomplish when they engage with an AI agent. Instead of guessing user demand based on feature releases or anecdotal feedback, this chart aggregates and visualizes detected intents across conversations, helping you understand behavioral patterns at scale.

It answers a fundamental product question:

Are we building for what users truly need?

By surfacing dominant, emerging, and under-served intents, this chart becomes the starting point for roadmap prioritization and resource allocation.

Mindlytics chart:

When you create a Intent distribution chart in mindlytics portal, by default mindlytics creates the chart for all users but you can select the user group you want by configuring the filter on the left side, and also by applying different time filter to narrow down the analysis.

Example insight:

The chart shows the distribution, for the last 30 days, of the reasons why the users came to your agent. It shows distribution as below,

• 38% →
  Check flight status
• 24% →
  Change flight
• 18% →
  Add Baggage
• 12% →
  Upgrade seat
• 8% →
  Talk to an Agent

Why it matters:

• Validates whether the agent matches real user demand. Is your agent optimized for the right intent?
• Surfaces unexpected intents you didn’t design for
• Helps prioritize roadmap investments

3. Intent Conversion Rate

Intent Conversion Rate KPI can be immediately accessed from the Conversation KPI chart. The Intent Conversion Rate provides a clear, outcome-focused view of how effectively your AI agent fulfills user needs. For every detected intent, it measures the percentage of conversations that successfully reach completion — whether that means resolving an issue, completing a booking, or executing a transaction.

Question it answers:

Did the user successfully complete what they intended to do?

Example insight:

In conversational interfaces, completion is not guaranteed simply because a dialogue occurred. This chart isolates performance at the intent level, helping product managers distinguish between high-demand intents and high-performing intents. Analysing conversion may bring you insight as below,

• Check flight status
  → 92% conversion
• Change flight
  → 61% conversion
• Upgrade seat
  → 34% conversion

Mindlytics chart:

By navigating to the Conversation KPI chart, you can track intent conversion rates by intent or aggregate; over time, across channels, versions, or user cohorts. Using this KPI, teams can directly connect conversational effectiveness to business impact. In the example below, monthly conversion rate of the intent "Change flight" shown using the bar chart stays around 61%, clearly providing signals to where to focus your optimization efforts based on your business goals.

Why it matters:

• Separates popular intents from effective ones
• Reveals revenue leakage (e.g., failed upgrades)
• Highlights where conversation design or backend integration breaks down
• Measure business impact like : A 10% increase in Intent Conversion Rate correlates with X% reduction in support costs or Y% increase in upsell revenue.

4. First-Intent Friction

First Intent Friction (FIF) KPI can be immediately accessed from the Conversation KPI chart. The First Intent Friction chart measures how much effort users experience while attempting to express and execute their primary intent at the beginning of a conversation. It captures signals such as clarification loops, fallback responses, repeated rephrasing, and extended turn counts before the intent is properly recognized or progressed as the user is warming up to the agent.

Question it answers:

How hard was it for users to get started?

This measures:

• Clarification loops
• “I didn’t understand that” responses
• Repeated rephrasing by the user

Example insight:

• 27% of
  Change flight
  conversations require ≥ 2 clarification turns
• Mobile users show 1.4× higher friction than desktop

Mindlytics chart:

By navigating to the Conversation KPI chart, you can track FIF by intent or aggregate; over time, across channels, versions, or user cohorts. In the example, we could visualize FIF across mobile and desktop channels, to identify if any channel specific FIF differences. Reducing First Intent Friction directly improves engagement, conversion, and overall user trust in the AI experience.

Why it matters:

• High friction = higher abandonment
• Direct signal of prompt quality, NLU gaps, or UX issues
• Helps PMs optimize how intents are captured, not just whether
• Understand business impact like, Reducing First Intent Friction by 15% lowers fallouts and can boost self-service completion rates.

In conversational AI, early friction is especially costly — it increases abandonment, escalations, and negative sentiment before value is delivered. For product managers, this metric acts as an early warning system, revealing whether the agent truly understands users from the first exchange or forces them to work too hard to be understood.

5. Drop-Off & Escalation Rate

Using the Funnel Charts in the portal, you could measure conversion or drop-off within a conversation and to get a structural view of how users move through intent execution and where they abandon or escalate. It maps conversational steps and identifies the exact moments where engagement breaks down or can be improved. This is especially critical for high-value or high-complexity intents, such as rebooking flights or completing financial transactions.
For product managers, this visualization shifts the discussion from “users are failing” to “users are failing at this specific step” — enabling precise optimization and experimentation.

Question it answers:

Where do users give up or ask for a human?

Example insight:

• 22% of failed
  Change flight
  intents escalate to agents
• 15% abandon after pricing explanation
• 9% drop after policy disclosure

Mindlytics chart:

In the following example chart, we will measure the failed Change flight intent and the success of the human agent taking over the conversation. The funnel can be created by configuring a sequence of Events that you want to analyse on the left side. Here we are tracking the intent Change flight expressed by the user followed by the intent failed scenario and the arrival of the custom event Agent Handover.

As you could see, 43.5% of the attempt to change flight failed and out of that, only 22% of the conversations is successfully handed over to the human agent. This gives clear signal and baseline to look into where to improve and measure the improvements applied.

Why it matters:

• Identifies exact conversation moments causing failure
• Quantifies operational cost (agent handoffs)
• Guides which intents should be redesigned vs. automated further

6. Sentiment & Engagement Trends

The Sentiment Analysis chart adds an emotional dimension to agent analytics in aggregate or by a specific intent. While conversion metrics tell you whether a task was completed, sentiment analytics reveal how users felt during the process. This distinction is crucial: successful intent completion does not always equate to a positive experience.

Question it answers:

How do users feel as they interact with the agent?

By layering sentiment over intent performance, product teams can balance efficiency, trust, and long-term user satisfaction. Mindlytics tracks sentiment shifts across the conversation, not just at the end.

Example insight:

• Sentiment drops sharply during rebooking steps
• Positive sentiment spikes after successful upgrades
• Negative sentiment correlates with longer clarification loops

Why it matters:

• Engagement ≠ success
• A “completed” intent with negative sentiment is still a product problem
• Enables PMs to balance efficiency and experience

Step 3: Using the Mindlytics Portal as a PM

The Mindlytics visualization portal is designed for exploration, not just the dashboards you'd stare at once a month.

As a PM, you can:

• You can create analysis charts matching your specific usecase
• Filter by intent, user cohort, channel, or version
• Compare conversion rates before and after prompt changes
• Identify which intents deserve deeper automation
• Tie conversational outcomes directly to business KPIs

Most importantly, you move from:

“We think users are struggling here…”

to:

“28% of users attempting flight changes drop off at pricing clarification — and it costs us X in agent escalations.”

That’s product-grade insight.

Conversational AI is not magic. It’s software — and software needs measurement.
Mindlytics helps product managers:

• Understand why users talk to agents
• Measure whether those needs are met
• Optimize how conversations drive business outcomes