Ruler Analytics

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.

Different funnel stages need different metrics.

Measuring awareness by leads generated isn’t the right lens, it’s built for something else entirely.

What to measure instead:
→ Impressions (is reach growing)
→ Content engagement
→ Remarketing pool size
→ Branded and direct search

Then for consideration, layer in your traditional metrics.

And to connect it all → journey measurement, impression modelling, and incrementality testing.

So you can see how earlier activity contributes to later conversions, not just last-click.

Results follow a recognisable pattern:
→ Reach grows
→ Retargeting pools expand
→ Branded search rises
→ Direct traffic builds

Then leads, efficiency and business outcomes follow.

It takes patience. But with leading indicators alongside conversion metrics, you can see it working before the pipeline catches up.

📌 Link in the comments on how to build a measurement framework

Conversion plateaus are a common challenge, and they’re often less about content quality and more about reach.

When marketing consistently targets in-market audiences, people already close to buying, growth can stall.

Not because the work isn’t good, but because the pool isn’t expanding.

Two adjustments tend to make a meaningful difference:

1. Moving upstream to reach problem-aware audiences ➝ people who have the pain your product addresses but aren’t yet comparing solutions. They represent a much larger opportunity and build pipeline for the future.

Expanding measurement beyond last-click metrics ➝ Branded search trends, remarketing audience growth, and direct visits all signal whether awareness is developing.

And journey measurement connects the dots further, linking touchpoints across the funnel to actual conversions, including impression-level modelling for activity that influences outcomes without ever generating a click.

Together, these create a more sustainable path to growth that reflects the full picture of what marketing is contributing.

Many marketing teams notice their reports don’t always reflect what’s happening in the real world.

That’s understandable.

Privacy changes, walled gardens, and fragmented customer journeys mean a single platform like GA4 can only show part of the picture.

Unified marketing measurement was created to address this. It brings together multiple methods in one framework:

→ Multi-touch attribution for click-level journeys
→ Impression modelling to capture top-of-funnel influence
→ Marketing mix modelling for online and offline coverage
→ Incrementality testing to validate and benchmark impact

By combining these approaches, you can fill in gaps and reduce channel bias.

Instead of comparing conflicting reports across different platforms, you get one consistent view of marketing performance.

Strategy teams gain a clear view for budget planning, while channel managers get the detail needed to optimise campaigns.

It’s the difference between guessing and making informed decisions.

Budgets are under pressure, targets continue to rise, and channels that once felt dependable aren’t delivering in the same way they used to.

For years, marketers have been encouraged to optimise around efficiency metrics that were easy to track and explain, like platform ROAS, cost per lead, and click-through rates.

As a result, spend naturally concentrated in lower-funnel channels that showed clear, short-term returns.

Brand search, retargeting, and last-click conversions kept dashboards looking healthy and made performance easier to defend.

Over time, that focus came with trade-offs.

Investment at the top of the funnel was reduced, awareness activity slowed, and demand creation became harder to prioritise.

The impact often shows up later: thinner pipelines, slower growth, and less flexibility in how budgets can be reallocated.

In one of our surveys, 35% of respondents said more budget would improve their marketing output.

That may be true in some cases, but budget alone doesn’t always address the underlying challenge.

What often helps more is clarity:

✔️ Understanding true incrementality

✔️ Distinguishing between channels that create demand and those that capture existing intent

✔️ Reallocating spend based on contribution to growth rather than reported efficiency can change how teams make decisions

When resources are tight, measurement and prioritisation matter more than ever.

The systems you build now are what shape how resilient your growth will be later 🌱

Not every ad gets clicked, but that doesn’t mean it has no impact.

A customer might see your social ad, then later search for your brand and convert. Traditional tracking often misses this first impression.

Impression modelling uses probabilistic machine learning to estimate the value of ads that are seen but not clicked, linking impressions to eventual conversions.

The result? A clearer view of your full marketing impact:

→ Give credit to campaigns that build awareness
→ Value brand and display campaigns alongside lower-funnel clicks
→ Capture the real customer journey, which often starts with that first impression

Stop undervaluing your brand-building efforts. Measure the influence of every impression and uncover your campaigns’ true impact 📈