Most teams look at the same email metrics every week. Average response time. Total volume. Response rate. These numbers are useful but they are averages, and averages hide the details that actually matter.
Which specific clients are receiving the slowest responses? Which agents are consistently underperforming on a particular type of query? How has workload distribution changed since the team grew by 30% six months ago? What does email engagement look like for the accounts that churned last quarter compared to the ones that renewed?
Standard email dashboards cannot answer these questions. They were not built to. The Email Meter BigQuery connector was.
Why standard email reports only scratch the surface
The problem with averages
A team average response time of 4 hours tells you very little. It might mean that every team member is responding in 4 hours or it might mean that half the team responds in under an hour while the other half takes 8 hours, and the average lands in the middle. These are two completely different situations requiring two completely different responses from a manager.
The same problem applies to workload distribution. A team average of 80 emails per day per person tells you nothing about whether some people are handling 150 while others handle 20. Or whether the workload imbalance is consistent across the week or spikes on specific days.
Averages are the starting point. The real insight is in the distribution underneath them.
The integration problem
Email data does not live in isolation. Your clients are in your CRM. Your revenue data is in your ERP. Your support tickets are in your helpdesk. But your email analytics are in a separate tool, which means you cannot answer the questions that require joining these datasets together.
Which clients with the lowest email response times have the highest renewal rates? Which agents handling the most emails also have the highest customer satisfaction scores? These questions require combining email data with other business data, something a standard email dashboard cannot do.
What the Email Meter BigQuery connector gives you
The BigQuery connector gives your data team direct access to your raw Email Meter data in Google's enterprise data warehouse. From there, you can build any view, run any query, and integrate with any platform you are already using, Looker, Tableau, Power BI, Data Studio, or your own internal dashboards.
As Laurence from Email Meter's Customer Success team explained in our recent webinar: "The ball is in your court when it comes to how to use this data. Data teams are interested in various parts of communications, and we're really just here to give you the data and make sure you've got it in a usable way."
Here are the most common use cases teams build once they have access to the raw data.
What you can build with Email Meter data in BigQuery
Workload distribution the real picture
The most common first build is a workload distribution view that goes beyond team averages. How many emails is each team member sending and receiving over a given date range? How does that compare to the team average? Who is consistently above the average and who is consistently below?
Email Meter gives you access to two years of historical data from the point you start using the tool, so you can understand not just what the distribution looks like today, but how it has changed over time. If workload has become more unequal since the team grew, the data will show it.
You can filter out internal emails, automated messages, and CC emails to focus only on client-facing communication. You can build contact groups to see workload distribution by client segment. And you can save these filters so the view is ready every time you open it.
Response time tracking at the individual and client level
Rather than a team average, you can build a response time view that shows, for each team member, how many emails were answered within your defined SLA 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 distinguish between a workload problem and a performance problem. If an agent is missing SLA targets because they are handling 40% more emails than their colleagues, that is a distribution issue. If they are missing targets despite a normal workload, that is a different conversation entirely.
Shared inbox analytics and agent scorecards
For teams managing shared inboxes, sales@, support@, info@, Email Meter provides data showing who is doing what inside each inbox. 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 performance over time, including how many emails each agent handles consistently and how many exchanges it typically takes them to resolve a thread. An agent who resolves queries in three emails is more efficient than one who takes nine, a difference that is invisible in a standard dashboard but immediately visible in the raw data.
Inbox action tracking
Beyond response times, you can track whether emails are being actioned at all, per day and per agent, how many emails were received, how many went unreplied, and how many went unread. The definition of "actioned" is up to you and your team's process, a reply, a forward, an archive, or a label.
This view is particularly useful for managers who want to ensure nothing is sitting in an inbox untouched at the end of each day. If something important is unread, a complaint from a key client, a time-sensitive request, the data surfaces it before it becomes a problem.
High-value client views
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 CRM segmentation you import alongside the email data.
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." Teams that monitor this data consistently are also better positioned to spot churn signals 90 days before a client leaves.
BigQuery connector vs custom dashboard: which is right for your team?
Email Meter offers two ways to access your email analytics, a pre-built custom dashboard maintained by the Email Meter team, or direct access to your raw data via the BigQuery connector for teams with their own data infrastructure.
The custom dashboard is the right choice for teams that want Email Meter to build and maintain their analytics. The BigQuery connector is the right choice for teams that already have a data warehouse or BI tool and want to consolidate email data alongside everything else.
What the setup actually looks like
For a data team, the setup is straightforward. 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, along with full schema documentation and a SQL guide to get you started immediately.
As Laurence noted in the webinar: "If you have a data team in your company, all these things will be really bread and butter for them."
Full documentation is available at docs.enterprise.emailmeter.com.
Watch the full webinar replay
In February 2026, Email Meter's Customer Success team ran a live session on turning email data into business intelligence with BigQuery, including live examples of workload distribution views, agent scorecards, response time tracking, and high-value client analytics built directly from raw email data.
FAQ
What is the Email Meter BigQuery connector?
The Email Meter BigQuery connector gives your data team direct access to your raw email analytics data in Google BigQuery. Unlike a pre-built dashboard, it lets you build custom views, run SQL queries, and integrate email data with any other business system, CRM, ERP, helpdesk, or BI tool.
What data does Email Meter provide in BigQuery?
Email Meter provides data on email volume, response times, SLA compliance, workload distribution, shared inbox activity, and individual agent performance. You also get access to two years of historical data from the point you start using the tool, along with full schema documentation and a SQL guide.
How is the BigQuery connector different from a custom dashboard?
A custom dashboard is built and maintained by the Email Meter team, tailored to your specific needs with quarterly reviews. The BigQuery connector gives your own data team direct access to the raw data to build any view they need and integrate it with your existing BI infrastructure. Both are valid options depending on whether you have an internal data team.
What does the setup look like?
You need a Google Cloud account, a project in the Google Cloud Console, and a billing account linked to that project. Email Meter then gives you access to your dataset. Full documentation is available at docs.enterprise.emailmeter.com. For most data teams, the setup is completed in a single session.
Can I combine Email Meter data with other business data in BigQuery?
Yes, that is one of the main advantages of the BigQuery connector. You can join Email Meter data with CRM data, revenue data, support ticket data, or any other dataset in your warehouse. This makes it possible to answer questions that require combining email metrics with other business context, for example, correlating response time data with renewal rates or customer satisfaction scores.
