Aircall dashboard: what's built in and how to go further

If you manage a sales or support team running on Aircall, you probably already know the dashboard. Call volume by agent. Average handle time. Missed call rate. Availability heatmaps. It's a decent operational view, enough to run a daily standup or flag a rep who's going dark.

But at some point, someone asks a question that the Aircall dashboard can't answer.

"Which reps have the best conversion rates based on call patterns?" "Are customers who called support more than twice in their first month churning at higher rates?" "What's our average revenue per outbound call, broken out by segment?"

These aren't exotic questions. They're the questions sales managers, RevOps leads, and CS directors ask every week. The problem is that answering them requires data that lives outside Aircall: your CRM, your billing system, your support tool. Aircall's analytics can't reach any of that.

This post covers what Aircall's native dashboard gives you, where it hits its limits, and how to build a more complete view that actually connects call data to business outcomes.

What Aircall's built-in analytics covers

Aircall ships with a reasonably solid set of operational metrics. Inside the dashboard, you can track:

• Call volume: inbound and outbound calls by team, agent, or time period
• Missed calls and missed call rate: how many calls went unanswered and who was responsible
• Average handle time: how long calls last on average, by agent or team
• Wait time and ring time: how long callers wait before being picked up or abandoned
• Agent availability: who's logged in, on a call, or away
• Call recordings and transcripts: accessible for review and coaching
• Voicemail activity: messages received and response rates

You can filter most of this by team, by agent, by tag, and by date range. For day-to-day call operations, it works. You can see who's answering calls, how quickly, and whether inbound volume is trending up or down.

The dashboard is also useful for capacity planning. If you can see that your team is handling 40% more calls on Tuesdays than Fridays, that's worth knowing before you schedule your next hiring cycle.

Where Aircall reporting hits its limits

The limitations show up the moment you want to move from operational metrics to business outcomes.

Call data lives in a silo. Aircall knows about calls. It doesn't know about deals, customers, segments, or revenue. So while you can see that a rep made 50 calls last week, you can't see whether any of those calls resulted in a demo booked, a deal advanced, or a contract signed. That context lives in your CRM.

No cross-source joins. This is the core issue. To answer questions like "which call patterns correlate with higher win rates?" or "do customers who call in more than twice per month churn less?", you need to join Aircall data with HubSpot, Salesforce, Stripe, or your product database. Aircall can't do that natively. Neither can most native analytics tools that live inside a single platform.

Custom metrics don't exist. Aircall doesn't have a concept of "revenue per call" or "calls to close" because it has no visibility into revenue or deals. You can export call data and build these in a spreadsheet, but then you're back to manual exports, stale data, and a one-off analysis that nobody else can use.

Historical trend analysis is limited. Aircall's dashboard is oriented toward current state. Running a multi-month cohort analysis on how call volume affected customer retention requires getting data out of Aircall and into something that can handle more complex queries.

Reporting doesn't flow to your team. Aircall doesn't have a clean way to share a live dashboard with stakeholders outside the platform. Screenshots and PDF exports exist, but they're not a real reporting workflow.

None of this is a knock on Aircall. It's a calling platform, not a BI tool. But if you're using Aircall as a core part of your sales or support motion, you eventually need more than call-level metrics.

What a better Aircall dashboard looks like

The goal isn't to replace Aircall's operational view. You still need to see who's available and how many calls your team is handling. The goal is to add a layer that connects call data to the business context it belongs in.

Here's what that looks like in practice:

Calls to conversion. Connect Aircall data to HubSpot or Salesforce, and you can see which calls resulted in demos booked, opportunities created, or deals closed. Not just call volume: outcomes. This lets you measure rep effectiveness based on results, not just activity, and identify the call patterns (volume, timing, duration) that actually predict conversion.

Revenue per call and cost per outcome. Once you join Aircall data with billing or CRM data, you can calculate real unit economics. Which team generates the most revenue per outbound call? Which inbound call type has the highest average deal value? These numbers don't exist in Aircall. They require connecting call records to customer and revenue data.

Churn signals from call patterns. Customers who are struggling with your product tend to call support more. Customers who stop calling often aren't using the product at all. Joining Aircall data with product usage and billing data lets you build an early-warning system: flag accounts where inbound call volume spiked or dropped sharply and correlate that with upcoming renewals.

Rep coaching based on outcomes, not just activity. Aircall shows you how long calls are and how many each rep makes. Connecting call records to deal outcomes shows you which call behaviors (talk time, follow-up speed, call time of day) predict success, so coaching conversations are based on what actually moves the needle.

Unified GTM view. Sales and support teams often both use Aircall. A unified dashboard that shows inbound support call volume alongside outbound sales activity, mapped against customer health scores, renewal dates, and revenue — gives leadership a complete view they can't get from any single tool.

Three ways to build a more complete Aircall dashboard

Option 1: Spreadsheet exports

Export call data from Aircall's reporting section. Export contact and deal data from your CRM. Match records and build pivot tables in Google Sheets or Excel.

This works for one-off analysis. It doesn't scale: the data is stale as soon as you export it, matching records by phone number or email is error-prone, and the analysis disappears into someone's downloads folder. If you're doing this monthly, you're spending a meaningful amount of RevOps time on a problem that has better solutions.

Option 2: Data warehouse + BI tool

Extract Aircall data via API into a data warehouse (Snowflake, BigQuery, Redshift). Load your CRM and billing data alongside it. Build dashboards in Looker, Tableau, or Metabase.

This is the enterprise path. It gives you full flexibility and a durable single source of truth. The cost: 4-8 weeks of setup, a data engineer (or a very patient RevOps lead with SQL skills), warehouse and BI licensing, and ongoing pipeline maintenance when schemas change. For a team under 100 people, this is usually more infrastructure than the problem warrants.

Option 3: Connect directly with an AI-native analytics platform

This is the approach we built Fabi around.

Instead of extracting data into a warehouse first, connect Aircall, your CRM, your billing system, and other sources directly. Then ask questions across all of them in plain English, or let the AI surface the metrics that matter automatically.

"Show me calls from the last 30 days joined with deal stage at the time of the call."

"Which reps have the highest ratio of calls to opportunities created, by segment?"

"Flag accounts that had more than 3 support calls last month and are up for renewal in the next 60 days."

We generate the SQL behind every query, so you can see exactly what's happening and adjust it if needed. The setup is minutes, not weeks. You authenticate with each source and start querying immediately against live data.

It also solves the ad hoc problem. When a sales leader asks in a Monday morning meeting "how did outbound call volume track against pipeline generation this quarter?", anyone on the team can open Fabi and get the answer without filing a ticket or scheduling an analyst.

What to look for in an Aircall analytics tool

If you're evaluating options, a few things that separate useful tools from ones that collect dust:

Aircall data access. The tool needs a way to pull Aircall call records, either via the Aircall API or by accepting CSV exports. Confirm this before committing to a setup.

CRM and billing connectors. Aircall data alone isn't the goal. You need the tool to join call records with HubSpot or Salesforce contacts and deals, and ideally with billing data from Stripe or similar. If you can only analyze one source at a time, you're not solving the core problem.

Self-service for non-technical users. The RevOps lead or sales manager who needs answers shouldn't have to wait on a data engineer. Look for natural language querying or a low-SQL interface that the people with questions can actually use.

Shareable dashboards. Insights that live in one person's browser aren't useful to leadership. Look for shared dashboards with live data, not screenshot-based reporting.

Transparency. When a tool tells you a rep's "conversion rate," you should be able to see how it calculated that number — what data it used, how it defined a conversion, what the denominator is. Especially for metrics that feed into comp decisions.

Aircall is a great calling tool. It's not a business analytics platform.

The limitation isn't Aircall's fault. Calling platforms are built to manage calls, not to analyze business outcomes across multiple systems. But if call data is central to your sales or support motion, you need analytics that can answer the questions that actually drive decisions.

The gap between "how many calls did my team make?" and "which calls drove revenue, and why?" is where most teams are stuck. The data exists. It's just spread across Aircall, your CRM, and whatever else you use. Connecting those sources is the problem worth solving.

You don't need a warehouse project to fix it. Connect your data sources, ask the questions your Aircall dashboard can't answer, and build the call analytics view your team actually needs.

Try Fabi free and connect Aircall alongside your CRM and other data sources to start querying across everything in minutes.