Building a Marketing Dashboard That Drives Decisions

Dan Moen Published January 18, 2026
Building a Marketing Dashboard That Drives Decisions

Most marketing dashboards are useless.

Not because they lack data. Because they’re full of numbers nobody acts on. Revenue is up 12% this month. Great. What are you supposed to do with that information? Trial signups increased. Cool. Why? What caused it? Can you repeat it?

The uncomfortable truth is that most dashboards are rearview mirrors. They tell you what happened, not what to do next. They report outcomes without revealing causes.

Here’s the test, courtesy of Improvado’s research on executive dashboards: Does your dashboard help you make better, faster decisions? If the answer is no, it’s failed. No matter how nice it looks.

This isn’t about having more data or prettier charts. It’s about building a dashboard that actually changes how you run the business.

Why Your Data Is Probably a Mess (And That’s Normal)

I’ve been CMO at two SaaS companies. In both cases, the state of marketing data when I arrived was horrible. Disparate systems. Poor data capture. Little to no integration between platforms.

And here’s the thing: we never fully fixed it. Even at our best, we were working with multiple systems producing numbers that were off but directionally useful. Reports from ad platforms that overcounted. Google Analytics that undercounted. Internal reporting on customers and upgrades that didn’t quite match finance’s numbers.

If this sounds familiar, you’re not alone.

The root cause is usually the same: product teams treat marketing like a bolt-on, not a core function. Data architecture gets designed around the product, and marketing measurement is an afterthought. The result is fragmented systems that don’t talk to each other and customer data that’s nearly impossible to segment for analysis.

Most companies do a decent job tracking the big stuff (revenue, churn, pipeline). Almost none of them do a good job enabling ad hoc analysis. Want to know how customers who came from organic search behave differently than those from paid? Good luck pulling that report.

This isn’t an excuse to give up. It’s context for what you’re actually working with. The goal isn’t perfect data. It’s getting directionally useful information that helps you make better decisions than you’d make with no data at all.

Measure What Causes Results, Not Just Results

An advisor I worked with years ago was obsessed with upstream metrics. He didn’t care about outcomes. He wanted to know what caused them.

Think of it like baseball. He didn’t care if you won or lost the game. He wanted to know how many strikes you threw, what your on-base percentage was, how many quality at-bats you had. Win or lose, those numbers tell you whether you’re doing the right things.

Marketing dashboards should follow the same logic. Revenue went up? Great. But what drove it? Which channels? Which campaigns? What changed in conversion rates or traffic quality? If you can’t answer those questions, you got lucky. And luck isn’t a strategy.

This is the opposite of how most founders think about measurement. They start with outcomes (MRR, pipeline, customers) and work backward. But by the time an outcome shows up on your dashboard, it’s already happened. You can’t change it. You can only learn from it.

Upstream metrics give you something you can actually act on today.

The BAU Trap

A CEO I worked with had a theory about marketing measurement. Last year’s results were “BAU” (business as usual). His logic: if we did everything exactly the same, we’d get exactly the same results. So we’d forecast last year’s numbers as the baseline, then stack new initiatives on top.

The problem? The world changes.

Some of the things you did last year will stop working entirely. Most will get a little worse. Channels get more crowded. Competitors improve. CAC creeps up. Content loses rankings. The algorithm changes.

You have to work your ass off just to stay flat.

The winners aren’t the ones who replicate last year. They’re the ones who find enough new and better to not only replace the decay but beat last year. That’s what killing it actually looks like.

This is why upstream metrics matter. If you’re only tracking outcomes, you won’t see the decay until it hits the bottom line. By then, you’re six months behind. The inputs (traffic quality, conversion rates by channel, CAC by source) show you where things are slipping before the revenue does.

Leading vs. Lagging: What You Can Actually Act On

Here’s a framework that helps: every metric is either leading or lagging.

Lagging indicators tell you what happened. Revenue. Churn. Customer count. They’re easy to measure, but by the time they move, it’s too late to change them.

Leading indicators predict what’s coming. Trial signups. Activation rates. Pipeline velocity. They’re harder to measure accurately, but they give you time to act.

Kalungi’s research on SaaS marketing metrics recommends pairing them: every lagging KPI should have 1-3 leading inputs. Churn rate (lagging) pairs with product usage and support ticket volume (leading). Revenue (lagging) pairs with qualified pipeline and conversion rates (leading).

The pairing matters because it creates accountability. When revenue misses, you can trace it back to which leading indicator broke down. Was it traffic? Conversion? Activation? Now you know where to dig.

The Core Metrics (A Starting Point, Not a Checklist)

I’m hesitant to give you a list. Every “must-track metrics” post ends up with 20+ items that no one actually monitors. But there are building blocks most SaaS businesses need some version of.

Acquisition:

  • Traffic by source (paid, organic, direct, referral)
  • Cost per acquisition by channel
  • Trial or signup conversion rate

Activation:

  • Time to first value (however you define it)
  • Activation rate (users who hit your “aha” moment)
  • Onboarding completion

Revenue:

  • MRR/ARR and growth rate
  • Average revenue per user
  • Expansion revenue vs. new revenue

Retention:

  • Gross and net revenue retention
  • Churn rate (logo and revenue)
  • Customer lifetime value

Efficiency:

  • CAC payback period
  • LTV:CAC ratio
  • Marketing spend as percentage of revenue

A16z’s guide to growth metrics makes the point that good metrics help founders understand “how and why things are working,” not just what happened. That’s the filter. If a metric on this list doesn’t help you understand why, drop it. If something not on this list does, add it.

Patrick Campbell’s research at ProfitWell found that 70% of executives prioritize acquisition metrics over retention and monetization, but the data shows retention and monetization actually drive more growth. Don’t fall into that trap. The metrics that feel most urgent (new customers!) aren’t always the ones that matter most.

Start with 5-7 metrics you’ll actually review. You can always add more once you’re consistently acting on what you have.

The Right Cadence

How often you look at your dashboard matters as much as what’s on it.

Daily: Glance at the business yesterday. Only drill down if something is surprising. This isn’t analysis time. It’s anomaly detection. Did something break? Did a campaign blow up (good or bad)? If nothing’s surprising, move on.

Weekly: Look at all your channels in depth. How’s each performing against expectations? Where are the trends heading? This is where you catch things starting to slip before they become problems.

Monthly: Evaluate all your marketing spend. Discuss test results. Prioritize new initiatives. Reallocate resources based on what’s working. This is decision-making time.

Quarterly: Revise your plans. Adjust budgets. Make big changes if needed. This is strategy time. Are we pointed in the right direction?

Most founders either check dashboards obsessively (daily deep-dives that don’t change anything) or barely at all (quarterly panic sessions). The cadence above gives you the right depth at the right frequency.

The Segmentation Gap

Most companies track the big numbers reasonably well. Revenue, churn, pipeline. You probably have a decent handle on these.

Where almost everyone falls short is segmentation.

Want to know how customers who came from organic search behave differently than those from paid? How enterprise customers retain compared to SMB? Which pricing tier has the best LTV-to-CAC ratio?

Good luck pulling those reports.

This is the ad hoc analysis gap. The data technically exists somewhere, but it’s spread across systems that don’t talk to each other. Stitching it together requires manual exports, spreadsheet gymnastics, and a data person you probably don’t have.

Lenny Rachitsky’s research on SaaS metrics found that Google Sheets is still the most common tool for SaaS dashboards. Not because founders lack sophistication, but because getting data out of siloed systems often requires manual work.

The fix isn’t buying more tools. It’s fighting for better data architecture from the start. Push your product team to treat customer data as a shared resource, not a product-only asset. Build tracking with marketing analysis in mind, not just product analytics.

This is a long game. But every improvement in segmentation capability makes your marketing decisions sharper.

Start Here

If your marketing dashboard doesn’t exist or doesn’t drive decisions, here’s where to begin:

First, apply the decision test. For every metric you’re tracking (or thinking about tracking), ask: “What action would I take if this number changed?” If you can’t answer that, the metric doesn’t belong on your dashboard. Userpilot’s research found that 36% of CFOs cite vanity metrics as a top concern with marketing reporting. Don’t be that CMO.

Second, pair your lagging indicators with leading ones. If you’re tracking revenue, what inputs drive it? If you’re tracking churn, what early signals predict it? Build the pairs so you can trace outcomes back to causes.

Third, set a cadence and stick to it. Daily anomaly detection. Weekly channel review. Monthly spend evaluation. Quarterly strategy adjustment. Put it on the calendar.

Fourth, pick one segmentation battle. You can’t fix the data architecture overnight. But you can pick one question you wish you could answer (customers by acquisition source, retention by pricing tier, whatever matters most) and fight to get that report working. Then pick the next one.

This isn’t an 80s movie. Nobody’s going to clap at the end. Marketing measurement is a continuous fight to improve the information available so you can make better, more efficient decisions. The companies that win aren’t the ones with perfect dashboards. They’re the ones who keep pushing for incrementally better data, month after month, year after year.

Sources and Further Reading