What is the Email Meter BigQuery connector?

Most analytics tools require you to change the way your team works, moving emails into a new platform, training everyone on a new system, and accepting a drop in productivity during the transition. Email Meter takes the opposite approach: it builds around your existing workflows, giving you the insights you need without disrupting your team.

The BigQuery connector is one of two ways to access Email Meter data. Rather than receiving a pre-built dashboard, you get direct access to your raw email data in BigQuery, Google's enterprise data warehouse. This means you can integrate Email Meter data with any other platform you're already using, build your own custom views, and consolidate all your analytics in one place.

As Laurence explained in the webinar: "If you want to have everything in one place, but you still want a way to get access to that data, we can provide that for you."

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What can you build with Email Meter data in BigQuery?

Workload distribution across your team

One of the most common problems customer-facing teams face is not knowing whether work is distributed fairly. Some team members feel overloaded while others have capacity, but without data, it's almost impossible to verify.

With Email Meter data in BigQuery, you can build a view that shows exactly how many emails each team member is sending and receiving over any date range. You can filter out internal emails and automated messages to focus only on client-facing communication, and sort by volume to identify who is carrying the heaviest load.

Email Meter also gives you access to two years of historical data from the moment you start using the tool, so you can understand how workload distribution has changed over time, not just what it looks like today.

Response time tracking and SLA compliance

Monitoring whether your team is meeting response time goals manually is time-consuming and unreliable. Email Meter data in BigQuery lets you build a response time view that shows, for each team member, how many emails were answered within your defined goal and how many were not.

You can click through to the raw data behind any breach to see exactly when the email came in, who it was from, what the subject line was, and how long it ultimately took to respond. This makes it straightforward to identify whether a response time problem is a workload issue, a training issue, or a process issue, before it affects your client relationships.

Shared inbox analytics

For teams managing shared or delegated inboxes, sales@, support@, info@, Email Meter provides a layer of data that shows who is doing what inside each inbox. Without this visibility, managers are essentially blind to how shared inboxes are actually being handled.

With BigQuery, you can build a shared inbox view that shows each agent's email volume, workload percentage, and response times, broken down by inbox. You can also build an agent scorecard that tracks an individual agent's performance over time, including how many emails they handle consistently and how many exchanges it typically takes them to resolve a thread. An agent who consistently resolves queries in three emails is more efficient than one who takes nine, a difference that is invisible without this data. For more on shared inbox analytics, see our guide on shared mailbox vs distribution list.

Inbox action tracking

Beyond response times, Email Meter data lets you track whether emails are being actioned at all. You can build a view that shows, per day and per agent, how many emails were received, how many went unreplied, and how many went unread.

This is particularly useful for teams that want to ensure nothing is sitting in an inbox untouched, whether that means a reply, a forward, an archive, or a label. The definition of "actioned" is up to you, and the view can be built to reflect whatever your team's standard process requires.

High-value client views

For customer-facing teams, not all clients are equal. Email Meter data in BigQuery lets you build a dedicated view for your most important accounts, filtered by domain, contact group, or any other segmentation that matters to you.

This view shows how much time your team is spending on these clients, how quickly they are responding, how often SLA targets are being breached, and which specific team members are responsible for each account. As Laurence put it: "If 5 clients make up 80% of the revenue you're making, it's really important that you have a special focus on them."

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BigQuery connector vs custom dashboard: which is right for your team?

Email Meter offers two ways to access your email analytics, a custom dashboard built by the Email Meter team, or direct access to your data via the BigQuery connector.

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What does the setup look like?

Setting up the BigQuery connector requires a Google Cloud account. The steps are straightforward for a data team:

Create a Google Cloud account if you do not already have one. Go to the Google Cloud Console and create a project. Set up a Google Cloud billing account and link it to the project. Email Meter then gives you access to your dataset.

Full documentation is available at docs.enterprise.emailmeter.com. If your company has a data team, this process is standard — the steps are well-documented and the setup is typically completed in a single session.

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Questions from the audience

What does the setup of the BigQuery Direct Connector look like?

You need a Google Cloud account, a project in the Google Cloud Console, and a billing account linked to that project. Once those are in place, Email Meter gives you access to your dataset. Full documentation is available in the Email Meter Enterprise docs. For most data teams, the setup is straightforward and completed in one session.

Why is it important to monitor how my team engages with customers?

If customers feel like they are being ignored or receiving slow responses, it directly damages the relationship and ultimately leads to churn. Monitoring email engagement gives managers the visibility they need to intervene before problems escalate, identifying which team members need support and which clients are at risk before they say anything. For a deeper guide on how email response time predicts churn, see The 90-Day Warning Sign Your Best Clients Are About to Leave.

What is the benefit of using the direct connector versus a custom dashboard?

The main benefit of the BigQuery connector is consolidation, you can integrate your email data with any other platform or dashboard you are already using, so everything is in one place. If your team already has a BI tool or data warehouse, the connector lets you add email analytics without creating a separate reporting silo. The custom dashboard is better suited for teams that want Email Meter to build and maintain the analytics for them.

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