Top performance analytics to look for in a marketing automation platform 

Marketing automation makes it possible to engage millions of customers with personalized experiences at scale. But launching campaigns is only half the job. You also need to know whether they're working.

That's where performance analytics come in.

The right marketing automation platform should do more than report how many emails were opened or links were clicked. It should help you understand how people move through campaigns, which messages influence meaningful outcomes, where journeys break down, and what you should optimize next.

And as marketing teams become more accountable for retention, conversion, and revenue, that distinction matters.

So whether you're evaluating a new marketing automation platform or taking a closer look at your existing stack, here are the performance analytics capabilities worth putting at the top of your list.

Marketing automation performance analytics are the metrics, reports, and insights that help you understand how your automated campaigns, messages, and customer journeys are performing.

At a basic level, this includes message-level metrics like deliveries, opens, clicks, bounces, and unsubscribes.

But those numbers only tell part of the story. Strong marketing automation analytics should also help you measure:

• How customers progress through automated journeys
• Which campaigns and messages drive conversions
• Performance across channels
• Trends across campaigns and time periods
• Deliverability and messaging health
• Experiment performance
• Customer and audience behavior
• Business outcomes like purchases, upgrades, or completed onboarding

Think of it as the difference between monitoring activity and understanding impact.

A high click-through rate might look impressive on a dashboard. But if those clicks aren't leading to activation, purchases, renewals, or whatever outcome matters to your business, there's still a missing piece.

That's why the best marketing automation platforms connect engagement metrics with customer behavior and outcomes.

Start with the fundamentals.

Your marketing automation platform should make it easy to see how individual messages and automations perform without exporting data and building your own reports every time you want an answer.

Depending on the channels you use, useful metrics might include:

• Messages sent and delivered
• Delivery rate
• Open rate
• Click-through rate
• Click-to-open rate
• Bounce rate
• Unsubscribe rate
• Spam complaint rate
• Push opens
• SMS deliveries and failures
• In-app message interactions
• Conversions

But access to metrics isn't enough. You also need to be able to put them into context.

Can you compare similar automations? Can you filter performance by channel or timeframe? Can you quickly identify your highest and lowest performers? Can you see whether performance is improving or declining over time?

The goal isn't to collect more numbers. It's to make those numbers useful.

For example, Customer.io distinguishes between message and delivery metrics and automation journey metrics, so you can understand both how individual messages perform and how profiles progress through an automation.

What to look for: Flexible reporting that makes it easy to move between high-level performance and individual automations or messages.

Engagement metrics are useful signals. They aren't always the outcome you're actually trying to create.

Someone clicking on an onboarding email is good, but someone completing onboarding is better.

That's why conversion and goal tracking should be a core part of marketing automation analytics.

Your platform should let you define the actions that matter to your business and understand how messaging contributes to them.

Depending on your business, those goals might include:

• Completing onboarding
• Starting a trial
• Upgrading a subscription
• Making a purchase
• Booking an appointment
• Using a key product feature
• Renewing a subscription
• Returning to an app
• Completing a profile

Ideally, goal measurement shouldn't be confined to one email or even one automation. Customer journeys rarely happen that neatly.

A customer might receive an onboarding email, interact with an in-app message, get a push notification three days later, and then complete the desired action. Looking only at the final touchpoint can obscure the role the rest of the journey played.

That's why it's useful to distinguish between automation-level conversions and broader business goals.

In Customer.io, for example, you can set conversion goals for an individual automation. You can also use Goals to define broader business outcomes, such as completing onboarding or starting a paid plan, and see performance across automations and broadcasts.

What to look for: The ability to define meaningful business outcomes and understand how your messaging contributes to them.

Traditional messaging analytics tend to focus on individual sends. Marketing automation introduces another layer: the journey itself.

Imagine an onboarding automation containing six messages, two delays, multiple branches, and different paths based on customer behavior.

Knowing the click-through rate of email number three is useful. But you may also want to know:

• How many people entered the automation?
• Where are people in the journey?
• Which workflow actions did they encounter?
• Did they reach the intended outcome?
• When did they enter or exit?
• Did certain conditions prevent them from receiving a message?

This is where journey-level analytics become especially valuable.

They give marketers visibility into the automation as a system rather than treating every message as an isolated interaction.

They can also make troubleshooting easier. If an automation isn't producing the results you expected, you can investigate whether the issue is the content itself, the audience, a workflow condition, timing, or the journey structure.

What to look for: Analytics that help you understand both aggregate automation performance and how individual customers progress through automated journeys.

As your lifecycle program grows, evaluating automations one by one becomes increasingly inefficient.

Say your team has 25 onboarding automations, 15 transactional automations, and dozens of recurring broadcasts. Opening each one individually isn't a practical way to answer questions like:

Which onboarding automations have the strongest conversion rates?

Has engagement changed over the past quarter?

Which transactional messages have unusually high bounce rates?

Your marketing automation platform should let you zoom out.

Cross-automation reporting makes it possible to compare related automations and broadcasts, identify trends, and find outliers that deserve attention.

Look for ways to organize and compare automations using attributes such as tags, naming conventions, channels, or time periods.

This turns analytics from an automation-by-automation review process into a more strategic view of your entire messaging program.

What to look for: Reporting that allows you to group, filter, and compare performance across multiple automations and sends.

Your customers don't experience your email strategy, push strategy, SMS strategy, and in-app strategy as separate things.

They experience your brand.

Your analytics should reflect that.

As marketing automation becomes increasingly multi-channel, reporting needs to help teams understand performance across the customer experience rather than trapping insights inside individual channel silos.

At the same time, context matters. An email click means something different from an in-app message interaction. Push and SMS can also generate responses differently from email.

Good cross-channel analytics preserve those distinctions while still helping you understand how different channels contribute to your messaging strategy.

That might mean filtering performance by channel while also understanding how different message types contribute to the same automated journey.

This becomes especially important as your messaging strategy matures. You may discover, for example, that adding a push notification to an onboarding journey improves conversion even though email engagement itself stays relatively flat.

If you're only evaluating each channel independently, that insight is easy to miss.

What to look for: Reporting that gives you channel-specific performance while helping you understand the bigger picture across your messaging program.

You can't optimize a message people never receive.

Deliverability analytics help you understand whether your messages are successfully reaching customers and identify problems before they undermine campaign performance.

For email, that can include monitoring:

• Delivery rates
• Bounce rates
• Spam complaint rates
• Sending volume
• Performance by mailbox provider
• Changes and anomalies over time

Provider-level reporting can be particularly useful. If your overall delivery rate looks healthy, but performance has deteriorated significantly for customers using one mailbox provider, an aggregate metric can hide the problem.

You also want to see these signals over time. A sudden increase in sending volume, combined with a jump in bounces or complaints, can tell you far more than any of those numbers viewed independently.

Deliverability shouldn't be something your team investigates only after engagement drops. Your analytics should make messaging health visible enough to monitor proactively.

What to look for: Deliverability reporting that helps you identify trends, surface potential issues early, and investigate what's behind them.

Optimization requires experimentation.

Most marketing automation platforms offer some form of A/B testing, but the quality and flexibility of those experiments can vary considerably.

It's easy to compare two subject lines based on open rates. More sophisticated experimentation asks bigger questions:

• Which message drives more conversions?
• Does sending a message earlier improve activation?
• Should customers receive one onboarding message or several?
• Which journey approach produces better customer behavior?
• Does sending a message make a difference at all?

That last question is particularly important. Holdout testing, where part of an audience doesn't receive a message, can help you understand whether your messaging is actually causing an incremental lift rather than simply being correlated with a conversion.

Look beyond content testing, too. Customer.io, for example, supports A/B tests for email, push, and SMS messages, while Random Cohorts can be used to test different paths through an automation. That makes it possible to experiment with things like timing, single-touch versus multi-touch journeys, and holdout groups.

The closer experimentation gets to the actual business outcome you're trying to influence, the more valuable it becomes.

What to look for: Testing capabilities that let you experiment with both individual messages and the broader customer journey, then evaluate the results against meaningful outcomes.

Analytics are only valuable when you can trust the underlying data.

That's become increasingly complicated for email marketers.

Privacy features and automated security systems can generate opens and clicks without a human actually interacting with the message. If those interactions aren't identified appropriately, engagement rates can become inflated.

The consequences extend beyond a misleading dashboard.

If your automation uses engagement data to create segments, personalize future messages, or inform your next experiment, artificial interactions can influence what customers receive next. They can also lead your team to optimize toward the wrong behavior.

That's why it's important to understand how your marketing automation platform handles machine-generated engagement.

For example, Customer.io distinguishes between human and machine email engagement. Human open and click metrics help teams separate genuine engagement from activity generated by things like security scanners and other automated systems.

This gives you a cleaner signal when you're trying to understand how real people are responding to your messages.

What to look for: Transparent metric definitions and mechanisms for distinguishing meaningful customer engagement from automated activity.

Your marketing automation platform won't always be the final destination for your analytics.

Your organization might use a data warehouse, BI platform, product analytics tool, or internal reporting system to understand customer behavior across the business.

Marketing performance data needs to fit into that ecosystem.

Look for flexible ways to get performance data out of your marketing automation platform, whether through CSV exports, APIs, webhooks, or connections to the rest of your data stack.

This gives data and marketing teams more freedom to combine messaging activity with product usage, revenue, account, and other business data.

For example, your marketing automation platform might tell you which customers converted after an onboarding automation. Combining messaging data with the rest of your customer data could help you investigate what happened next, such as whether those customers retained or generated more revenue over time.

Customer.io, for example, supports Reporting Webhooks that send real-time message activity, including sends, opens, and clicks, to external systems for further analysis.

Your platform's native analytics should make everyday questions easy to answer. Your data integrations should give you the flexibility to investigate everything else.

What to look for: Easy access to performance data when you need to analyze it alongside the rest of your customer and business data.

There's one final capability that's easy to overlook: usability.

Having hundreds of metrics doesn't automatically make an analytics product powerful.

The real question is how quickly marketers can go from data to a decision.

Can someone identify an underperforming automation without writing SQL? Can they compare performance without creating a spreadsheet? Can they investigate an unexpected metric and understand what happened? Can they turn insight into their next experiment?

AI can shorten that distance even further.

Instead of manually navigating reports every time you have a question, AI can help marketers interrogate performance data and surface insights that matter.

In Customer.io, for example, you can ask the agent questions about your performance metrics, such as which onboarding automations had the lowest deliverability or how a broadcast performed. Routines can take this a step further by automatically analyzing metrics and trends, comparing performance, monitoring things like deliverability or goal conversions, and delivering recurring summaries.

That changes the role analytics can play. Instead of waiting for someone to open a dashboard and spot a problem, teams can make performance analysis an ongoing part of how they operate.

Whether insights are surfaced through dashboards, reports, or AI, the principle is the same:

The best marketing automation analytics don't just make data available. They make it actionable.

The most important marketing automation metrics depend on your goals, but generally include delivery rate, click-through rate, conversion rate, unsubscribe rate, bounce rate, and goal completion.

Engagement metrics shouldn't be evaluated in isolation, though. The most useful marketing automation analytics connect them to customer behaviors and business outcomes, such as purchases, activation, upgrades, and retention.

That gives you a clearer answer to the question that matters most: did your marketing automation influence the outcome you wanted?

Measure marketing automation performance across three levels: message engagement, customer journey performance, and business outcomes.

Message-level analytics tell you whether people received and interacted with your communications. Journey analytics show how customers progress through an automation. Conversion and goal data tell you whether those interactions ultimately resulted in the desired action.

Looking at all three together provides a more complete picture than relying on opens and clicks alone.

Look for marketing automation reporting that lets you analyze individual messages, compare automations, track conversions, understand customer journeys, evaluate performance across channels, monitor deliverability, and access data for deeper analysis.

Just as importantly, reporting should make it easy to move from a high-level trend to the automations and messages responsible for it. The goal is to spend less time assembling reports and more time acting on what they tell you.

Opens and clicks measure engagement, but they don't necessarily tell you whether your messaging achieved its business objective.

An email could have a high click-through rate but a low conversion rate. Conversely, a message with relatively modest engagement could still influence a meaningful number of customers to activate, purchase, upgrade, or renew.

Connecting engagement metrics to downstream customer behavior gives you a more accurate picture of performance and impact.

Marketing automation analytics help you identify which messages, channels, audiences, and customer journeys perform best and where customers encounter friction.

You can use those insights to refine targeting, adjust timing, improve content, redesign journeys, and develop new experiments. Over time, that creates a feedback loop: launch, measure, learn, optimize, repeat.

The result isn't simply better reporting. It's a marketing automation program that continuously improves based on real customer behavior.

Marketing automation analytics have evolved far beyond open and click rates.

Those metrics still have a role to play, but they're signals within a much larger picture.

The analytics that matter most help you understand the relationship between messages, customer journeys, and business outcomes.

When your marketing automation platform can help you answer both questions, analytics stop being something you review after a campaign.

They become part of how you build, test, and continuously improve better customer experiences.