Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
OK so here's what drives me crazy about SEA vs SEO reporting - everyone's building these beautiful dashboards that tell you absolutely nothing about what to do next. You know those ones, right? Charts everywhere, metrics out the wazoo, but when it comes to actually making a decision about where to put your budget next month? Crickets.
I learned this the hard way when I was helping a B2B SaaS client figure out why their paid ads were showing great ROI while their organic traffic was converting like garbage. Their existing dashboard showed clicks, impressions, all the vanity metrics. But it didn't tell them the one thing they needed to know: which channel was actually bringing in customers who stuck around.
The thing is, most businesses are drowning in data but starving for insights. They're comparing apples to oranges, tracking the wrong metrics, and making budget decisions based on incomplete pictures. After building dozens of these dashboards for clients, I've figured out what actually works and what's just pretty noise.
Here's what you'll learn from my real-world experiments:
Why traditional SEA vs SEO dashboards fail to drive decisions
The 5 metrics that actually matter for channel comparison
How I structure dashboards that prevent budget waste
Real examples from e-commerce and SaaS implementations
The attribution model that finally made sense of our data
Reality Check
What every marketer has already tried
Walk into any marketing team meeting and you'll hear the same story: "We need better visibility into our SEA vs SEO performance." The solution is always the same - build a dashboard. Google Data Studio, Tableau, whatever the flavor of the month is.
The industry standard approach goes like this:
Traffic Metrics: Sessions, users, bounce rate from each channel
Cost Metrics: CPC for paid, "free" for organic (spoiler: it's not free)
Conversion Metrics: Form fills, downloads, trial signups
Revenue Attribution: Usually last-click, sometimes first-click
ROAS Calculations: Revenue divided by ad spend (ignoring all other costs)
This approach exists because it's easy. Google Analytics gives you most of these metrics out of the box. Your boss understands "paid ads brought in $10 for every $1 spent." It fits nicely into quarterly reports.
But here's where it falls apart in practice: these dashboards optimize for the wrong outcomes. They treat SEA and SEO like separate universes when they're actually part of the same customer journey. They ignore the fact that someone might click your ad, leave, then come back through organic search to convert. They measure activity, not impact.
The biggest problem? These dashboards make you feel like you're making data-driven decisions when you're actually making decisions based on incomplete data. And in a world where every click costs money and organic rankings take months to build, incomplete data kills businesses.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So I'm working with this B2B SaaS client - they're selling project management software to mid-market companies. They'd been running Google Ads for about eight months, decent ROAS on paper, around 3.5x. Their SEO was bringing in solid traffic, good rankings for their target keywords.
But here's the weird thing that made me dig deeper: their paid search was converting trial users at 12%, while organic was only hitting 3%. Most consultants would say "great, double down on paid ads." But something felt off.
I started looking at their user behavior data and found this pattern that their existing dashboard completely missed: users who came from organic search were actually using the product way more during their trials. The paid traffic would sign up, maybe create one project, then disappear. The organic users were building out their entire workflow.
Their dashboard showed paid ads as the winner because it tracked trial signups. But when I followed the users through to actual paid conversions after the trial ended, the story flipped completely. Organic users were converting to paid plans at almost 40%. Paid users? About 8%.
This is when I realized their attribution model was broken. Their dashboard was giving full credit to the last touchpoint, but most of their best customers had this journey: saw a paid ad, didn't convert, researched the category, found them again through SEO, then converted. The ad did its job by creating awareness, but SEO closed the deal.
The existing dashboard made it look like they should kill their SEO budget and pour everything into ads. In reality, they were about to destroy the channel that was bringing in their highest-value customers. That's when I knew I had to build something completely different.
Here's my playbook
What I ended up doing and the results.
OK, so here's exactly what I built for them, and why each piece matters for making actual decisions rather than just looking at pretty charts.
Step 1: Multi-Touch Attribution Tracking
First thing I did was throw out last-click attribution. Instead, I set up a system that tracked the full customer journey using UTM parameters and customer IDs. Every touchpoint got credit, weighted by proximity to conversion. So if someone saw a Google ad, came back through organic search, and converted - both channels got credit, but organic got more since it was the closer action.
The technical setup: I used Google Analytics 4's data-driven attribution model, but supplemented it with custom tracking through their CRM. Every user got a unique identifier that followed them across sessions. When someone converted to a paid plan, I could trace back every touchpoint in their journey.
Step 2: The 3-Layer Dashboard Structure
Instead of one massive dashboard, I built three focused views:
Executive Layer: Just four metrics - CAC by true channel (including content costs for SEO), LTV by acquisition channel, payback period, and net contribution margin. That's it. No vanity metrics, no traffic numbers, just what matters for budget decisions.
Channel Layer: This is where I tracked channel-specific health. For SEO: keyword rankings, content performance, technical issues. For SEA: impression share, quality scores, auction insights. But here's the key - I connected these operational metrics to business outcomes. Dropping keyword rankings weren't just bad for SEO pride, they correlated with decreased trial quality three weeks later.
Campaign Layer: Granular performance by individual campaigns, ad groups, and content pieces. This is where the optimization happened day-to-day.
Step 3: The Game-Changer - Cohort Revenue Analysis
This is what made the dashboard actually useful for decisions. I tracked users by acquisition month and channel, then measured their revenue contribution over time. Suddenly we could see that organic users acquired in month 1 were generating 60% more revenue by month 6 compared to paid users from the same period.
But here's what really blew their minds: paid ads were actually contributing more to organic conversions than the organic channel itself. The data showed that 70% of their "organic" converters had previous exposure to their paid ads. The ads weren't converting directly, but they were making the organic conversions possible.
Step 4: Budget Allocation Recommendations
Instead of just showing performance, the dashboard made recommendations. If organic CAC was trending up due to increased competition, it would suggest increasing paid spend to maintain overall acquisition volume. If paid quality scores were dropping, it would flag the need to improve landing pages, which would help both channels.
True Attribution
Track the full customer journey, not just the last click. Most high-value customers touch multiple channels before converting.
Revenue Cohorts
Group users by acquisition month and channel to see long-term value differences, not just initial conversion rates.
Channel Synergy
Measure how channels support each other - paid ads often make organic conversions possible and vice versa.
Actionable Metrics
Focus on CAC, LTV, and payback period by channel rather than vanity metrics like clicks and impressions.
Within three months of implementing the new dashboard, my client made some major strategic shifts that completely changed their marketing ROI.
First, they didn't kill their SEO budget like they were planning to. Instead, they increased it by 40% because they could finally see its true contribution. The dashboard showed that their organic-acquired users had a 180% higher LTV than paid users - $3,200 vs $1,800 average revenue per customer.
But here's what was really interesting: they also increased their paid ad spend by 60%. Why? Because the dashboard revealed that their paid ads were doing something their attribution model hadn't captured - they were pre-qualifying users for organic conversions. Users who saw a paid ad first, even if they didn't click, were 3x more likely to convert when they later found the company through organic search.
The overall result: total customer acquisition cost dropped by 35% while lead quality (measured by trial-to-paid conversion) increased by 50%. But the biggest win was that they could finally make confident budget decisions instead of guessing based on incomplete data.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After building similar dashboards for six different clients, here are the lessons I wish I'd known from the start:
Attribution is everything, but it's also broken by default. Every platform wants to take credit for conversions. Google Ads says Google Ads drove it, Google Analytics has its own story. Build your own attribution model or you'll always be optimizing for the wrong metrics.
Time horizons matter more than you think. Organic traffic often has a longer conversion cycle but higher lifetime value. If you only look at 30-day windows, you'll systematically undervalue SEO.
Channel synergy is real but invisible in most dashboards. Paid ads make your organic listings more credible. Good SEO content improves your ad quality scores. Measure the combined effect, not just individual channel performance.
Cohort analysis beats point-in-time metrics. Don't just look at this month's performance - track how users acquired in different months perform over their entire lifecycle.
Automate the insights, not just the reporting. A dashboard that just shows data is useless. Build in recommendations and alerts that tell you what to do with the information.
Start simple, then add complexity. My first dashboard had 47 different metrics and nobody used it. The version that actually drove decisions had 12 core metrics and clear action items.
Different stakeholders need different views. Your CEO doesn't need to see keyword rankings. Your SEO manager doesn't need to see board-level ROI metrics. Build layered dashboards that serve different decision-making needs.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
SaaS Dashboard Essentials:
Track CAC and LTV by channel with proper multi-touch attribution
Monitor trial-to-paid conversion rates by acquisition source
Measure time-to-value for users from different channels
Include content production costs in SEO channel attribution
For your Ecommerce store
E-commerce Dashboard Focus:
Prioritize ROAS over traffic metrics, but include full customer journey
Track repeat purchase rates by acquisition channel
Monitor seasonal performance patterns for budget planning
Include assisted conversions to capture channel synergy effects