Ruler Analytics

Your GA4 Direct traffic may be hiding the impact of other channels, with as much as 20% potentially misattributed.

Someone sees a TV ad or scrolls past a social post with no link, and later types the brand straight into the address bar. There’s nothing for GA4 to connect that back to, so by default, it lands in the Direct bucket.

Based on what we’ve seen across client data, this can make brand and awareness spend look like it’s doing far less than it actually is, because none of that influence gets acknowledged anywhere in the reporting.

It’s not that Direct traffic is a bad sign. Usually the opposite, people know the brand and are coming back intentionally. It’s more about understanding how much of it was earned through awareness work that never got the chance to show up.

A few things worth a look:

→ Does Direct traffic spike near brand or awareness activity, a new campaign, an OOH push, a TV spot?
→ How does branded search volume move alongside it?
→ What do new customers say when you simply ask how they first heard of you?

The goal should be to understand what’s actually creating direct traffic, because that’s where the real value of your marketing activity often sits.

Have you found a reliable way to understand what’s really sitting behind Direct traffic?

AI agents are everywhere right now, but we built ours with a clear purpose 👇

Our Analyst Agent and Media Planner Agent exist to answer two simple questions:

- what’s working?
- what should we do next to improve?

They bring together your marketing data to surface useful insights, highlight opportunities, and help guide your next move.

The goal is to give customers ongoing, consultant-level insight and practical recommendations, without the extra workload, complexity, or cost of relying on additional support.

Last click isn’t broken, it’s just being asked a question it was never built to answer.

It tells you through which channel someone found you when they were already ready to buy. What it doesn’t tell you is what made them ready to buy in the first place, and mixing those two things up is usually where the trouble starts.

Most CRMs, revenue systems and ad platforms default to last click because it’s simple to implement and easy to explain. So marketers end up making budget decisions based on a model that is, by design, blind to everything that happened before that final touchpoint.

Brand awareness, upper funnel activity, the channels that built intent over weeks or months, none of it gets credit. What gets credit is the branded search someone did once they were already convinced.

So teams cut awareness spend, double down on bottom of funnel, and then wonder why new demand dries up. Every planning cycle ends up reinforcing the same conclusion, bottom of funnel gets more, top of funnel gets less, and revenue growth slows.

The plateau is fairly recognisable once you know what to look for. Everything looks fine at a channel level, but total revenue flatlines. Acquisition costs creep up because you’re fighting over a shrinking pool of in market buyers. ROAS looks stable but volume is quietly shrinking, because you’re harvesting demand rather than creating it.

What actually helps is a unified view of the full funnel, one that maps everything back to real revenue outcomes. Not because any single model is perfect, but because that fuller picture asks a much better question than who got the last click.

How do you make the case for upper-funnel investment when the last click gets the credit?

A lot of budget planning still runs mostly on last year’s numbers, gut feel, and platform data that never ‘quite’ lines up.

Our Budget Scenario Planner works from something more accurate, performance and revenue data pulled into one view. From there, you can build a few models side by side, current, growth, efficiency, each showing the projected revenue impact based on what’s actually happened in the business.

→ Need to cut 10%? See where it costs the least.
→ Board wants 30% growth next quarter? See what that actually takes at current efficiency, before anyone commits to a number.

Compare scenarios in real time, then export something that’s ready for the room.

Built on verified, revenue-linked data, not the platform-reported numbers that tend to run a bit optimistic.

Queries that used to go straight into Google are increasingly going into LLMs like Claude, and the people doing that are often your highest intent prospects.

Google’s answered with AI Overviews, which have pushed organic results further down the page. The search results page marketing teams have spent years optimising for looks fundamentally different now.

For a while, this demand existed somewhere paid ads simply couldn’t reach. That’s already starting to shift, OpenAI’s introduced ads inside ChatGPT, though it’s still early days, and brand authority is still what carries the most weight there.

You earn that authority through visibility, being mentioned, cited and discussed in the right places, press, directories, industry publications, so you’re more likely to surface in AI generated answers.

The good news is this is more measurable than most people assume. When someone clicks through from an LLM, that referral source can be captured in your first party data. And because first party tracking follows the user across their whole journey, you’re not just seeing that AI search sent a visit, you’re seeing how it fits alongside every other channel that contributed to the conversion.

Layer self reported attribution from lead forms on top of that and the picture gets even clearer. Last year, we found 35% of leads were attributing themselves directly to AI tools.

The traffic is there and the signals exist, you just have to know where to look.

Are you currently tracking any traffic coming through from AI tools, or is it still a blind spot?

One thing keeps coming up when we talk to marketers about measurement → a lot of the week is spent pulling data together.

Checking dashboards, cross-referencing channels, and trying to figure out what’s actually changed and why. Not because anyone’s doing anything wrong, but because that’s simply how the workflow has evolved for most teams.

That gap, between collecting data and actually acting on it, is what led us to build our AI Analyst and AI Media Planner.

AI Analyst → monitors your full marketing mix, surfaces what’s shifted, and explains why, connecting the channels that normally live in separate dashboards.

AI Media Planner → turns those insights into budget decisions and helps see the likely impact on pipeline and revenue before making a move, with the reasoning to back it up in the next budget meeting.

Together, they provide ongoing monitoring, modelled ROAS and marginal ROAS at channel and campaign level, budget scenarios you can stress-test, and a model that keeps refreshing as your marketing mix changes.

That’s less time collecting data and more time planning what to do with it.

👉 How much of your week goes to analysing data vs. acting on it?

Most of the time, marketers come to us because their reporting lives inside individual ad platforms, and each one is telling a slightly different story. So instead of one source of truth, you end up with several competing sources.

From what we’ve found working with marketers on this, there are five steps that tend to resolve it.

1️⃣ Start with your first party data, your CRM or ecommerce backend, wherever revenue is actually recorded. Leave the platform reports and estimated conversion values to one side for now. That real data is the foundation everything else needs to be built on.

2️⃣ Connect that marketing activity to the outcomes that actually close. This is the bit most attribution setups miss, teams can usually tell you what generated the click, but not what generated the revenue.

3️⃣ Tackle deduplication. Once you’re pulling data from multiple sources into one place, the same conversion will often show up more than once. You need some logic in there to collapse those overlapping claims down into a single view.

4️⃣ Bring your cost data into the same space. Metrics like ROAS and CPA don’t mean much on their own, you need spend sitting right alongside revenue so you’re comparing like for like.

5️⃣ Build one unified dashboard that everyone reads from, marketing, finance, leadership, all looking at the same numbers. When everyone’s working off something different, decision quality tends to suffer without anyone quite realising why.

Once these five are in place, you stop debating whose numbers are right and start talking about what to do next to improve.

What does your reporting setup look like right now, one dashboard or several?

We speak to marketing teams every week who are juggling different ad platforms and genuinely aren’t sure which numbers to trust. It’s one of the most common conversations we have.

Each platform only measures what happens inside its own walls. So if someone sees your ad on Facebook and then clicks a Google ad a few days later, both platforms will claim that conversion, because neither one can see what the other did.

View through attribution muddies things further. Someone scrolls past your ad, doesn’t click, then converts the next day through a branded search. The platform that served that impression still logs it as a win as the influence was probably real. However, now that same conversion is sitting on two separate balance sheets at once.

The one place that shows what actually happened is your revenue backend. Conversion secured, customer acquired, no ambiguity.

From what we’ve seen, it’s not really about picking one platform to trust over another. It’s about building a measurement layer that sits above all of them, one that strips out the duplicate claims and anchors everything back to real revenue. That’s the number you can actually take into a budget conversation.

Which platform do you find yourself trusting the least, and why?

A client came to us recently running paid media across both online and offline channels, paid search, paid social, display, radio, and print. Their setup included the standard analytics tools, advertising platforms, and a CRM. Each was reporting performance, but none of it reconciled.

Every platform attributes conversions to itself, by design, which means each was overclaiming credit independently, and there was no methodology in place to reconcile the discrepancies across them.

The team couldn’t state with confidence where performance was actually originating, and budget decisions defaulted to caution as a result.

We addressed this by helping them build a unified marketing mix model, one methodology applied consistently across every channel, online and offline, removing platform self-attribution from the equation entirely.

What we found that Meta and TikTok were delivering higher ROAS than Google Ads, despite Google receiving roughly double the budget allocated to paid social.

That comparison is not something siloed platform reporting can surface, it only becomes visible once every channel is measured against the same standard.

As a result, this client had been under-investing in paid social for approximately a year, with no mechanism in place to identify it. From there, our budget scenario planner allowed them to model reallocation outcomes before committing any spend.

That’s the core shift we see repeatedly, moving from gut feel and platform-reported benchmarks to a mathematically grounded view of channel performance.