Marketing has never had more data – and never been more blind.
Third-party cookies are disappearing, ad platforms guard their insights, and reports are riddled with blind spots.
Privacy regulations like GDPR and CCPA, browser updates, and iOS changes have fractured tracking into pieces too small to give a full picture.
Meanwhile, the customer journey is anything but linear. Google calls it the “messy middle” – a web of touchpoints across search, social, email, ads, events, and more.
An ecommerce customer might have six meaningful interactions before buying. In B2B, that number can climb to 60+ across multiple channels, per Dreamdata.
Yet many attribution systems still distill all of this complexity into one lazy metric: the last click.
The result?
Upper- and mid-funnel efforts that actually drive demand get sidelined, leaving PPC teams making budget calls with an incomplete picture.
Last-click attribution: The habit that won’t die
We’ve long known that last-click attribution is flawed, yet it still dominates in many organizations.
It survives because it’s simple, familiar, and easy to explain to clients. Forecasts are built on it, and it’s woven into daily workflows.
But giving 100% of the credit to the final touchpoint, often branded search or retargeting, is a warped view of reality.
If someone discovers you via a podcast, reads a blog post, sees a LinkedIn ad, then finally Googles your brand and clicks a search ad, last-click crowns that search ad as the hero.
Over time, this bias quietly drains budget from awareness and consideration campaigns.
Demand creation slows, but the reports never show the warning signs.
In B2B, where deals are rarely sparked by a single click, this short-term thinking is especially dangerous.
Worse still, last-click undervalues what’s often called “dark social.” Think:
- Word-of-mouth.
- Community discussions.
- Events.
- The influence that never shows up in click reports.
One marketer even found Facebook undervalued by 90% using last-click metrics, yet convincing stakeholders to shift budget was a battle.
Dig deeper: 7 must-know marketing attribution definitions to avoid getting gamed
Why new analytics models haven’t saved attribution (yet)
Data-driven attribution models like GA4’s default DDA were supposed to rescue us from last-click thinking.
In theory, they spread credit across touchpoints using machine learning rather than rigid rules.
In practice, they come with their own problems.
- They’re opaque – a black box of fractional numbers with little explanation of why each touchpoint gets the credit it does.
- They’re also incomplete – largely confined to the Google Ads and Analytics universe – and blind to offline, cross-device, and many third-party interactions.
These models fall under the broader category of multi-touch attribution (MTA), which assigns proportional credit to every marketing interaction a user has before converting.
But even if MTA were perfect at distributing credit, it wouldn’t prove causality.
It also won’t tell you whether campaigns are driving incremental results.
It works backwards from a conversion, assigning credit based on correlation. There is no way to tell if the ad actually caused the sale or if it would have happened anyway.
That’s a big problem in an era when research shows many platform-attributed conversions simply capture people who were going to buy regardless.
Ad platforms often optimize for the “low-hanging fruit” – users most likely to convert – which inflates reported results without creating net-new demand.
With cookies disappearing and user-level tracking eroding, these models are also losing the very signals they rely on.
MTA models built on granular tracking are disappearing fast due to privacy regulations and browser changes.
Even with GA4 and data-driven algorithms, most marketers are still not in a much better position to confidently identify what truly drives value.
Dig deeper: Marketing attribution models: The pros and cons
How to evolve your PPC measurement approach
If traditional attribution is broken and new models haven’t fully fixed it, how should you adapt?
The answer is not to give up, but to change your approach.
In a world of patchy data and complex journeys, you need to shift from chasing perfect attribution to focusing on pragmatic, business-driven insights.
Here are several strategies to consider.
Leverage first-party data and CRM integration
With third-party data drying up, your own first-party data is now gold.
Every marketing team should invest in capturing and integrating as much customer data as possible from:
- Analytics.
- CRM system.
- Other customer touchpoints.
Collect known user data (with consent) through:
- Gated content.
- Newsletter signups.
- Free trials can help.
Just as important is tying that data together. Connect your ad platforms to tools like HubSpot or Salesforce and track post-click actions in your CRM pipelines.
By importing offline or downstream conversions (e.g., leads that turn into sales) back into Google Ads or Analytics, you close the loop on which clicks lead to real revenue.
This deeper integration lets you move beyond vanity metrics.
If a PPC campaign produces leads that rarely close, a CRM-integrated view will show it, even if last-click attribution looked healthy.
Measure incrementality, not just conversions
Perhaps the most important mindset shift is to prioritize incrementality over simple attribution.
Instead of obsessing over which ad or channel “gets credit” for a conversion, ask yourself: Would this conversion have happened if we hadn’t run those ads?
In other words, what portion of conversions are truly incremental additional sales generated solely thanks to your marketing?
As one expert put it:
- “The most important question isn’t ‘What drove the conversion?’ It’s ‘What drove conversions that wouldn’t have happened without the media?’”
Measuring incrementality requires experimentation.
Savvy marketers use lift tests and holdout experiments to directly gauge cause and effect.
You might, for example, hold out a random portion of your audience (or geographic regions) from seeing your ads and compare conversion rates with the exposed group.
This kind of testing isolates the true lift from your media spend.
Major platforms now offer built-in tools (e.g., Facebook’s Conversion Lift, Google’s geo experiments), or you can design your own.
The key is to make testing a regular part of measurement, not a one-off project.
By making incrementality your north star, you focus the team on net-new results.
You may discover that retargeting is cannibalizing organic conversions you would have gotten anyway, while a modest LinkedIn campaign is actually driving a new pipeline.
Dig deeper: Incrementality testing in advertising: Who are the winners and losers?
Use marketing mix modeling for a holistic view
Attribution tools work bottom-up (assigning credit to touchpoints), whereas marketing mix modeling (MMM) works top-down.
MMM analyses aggregate spend and results across channels over time to reveal each channel’s contribution.
It doesn’t rely on cookies, can incorporate offline channels, and shows insights platform reports miss like cross-channel synergies or diminishing returns.
For example, it might reveal that:
- Display advertising is driving valuable assists even though last-click shows few conversions.
- Or radio and paid search together are more effective than either alone.
Think of MMM as a strategic planning tool. It’s not for daily optimization, but running it quarterly or annually helps set budgets with confidence.
In one analysis, MMM showed that some channels with excellent platform ROAS actually had marginal returns below $1 when incremental contribution was measured.
Dig deeper: MTA vs. MMM: Which marketing attribution model is right for you?
Adopt a unified and flexible measurement framework
No single method will perfectly capture today’s customer journey.
The smartest marketers blend platform-reported data, first-party analytics, qualitative insights, and experimental results.
You might combine:
- GA4’s data-driven attribution for quick insights.
- MMM for validation.
- Lift tests for direct measurement.
You can also supplement this with sales feedback or customer surveys.
This multi-source approach overcomes the blind spots of any one method and forces internal alignment.
Instead of PPC, SEO, and social teams fighting for credit in silos, everyone focuses on metrics that matter:
- Incremental revenue.
- Cost per new customer.
- Pipeline contribution.
Finally, accept there’s no such thing as perfect tracking – and that’s OK.
The goal now is clarity you can act on. Patterns, trends, and informed judgment will guide the smartest investments.
Attribution doesn’t have to be perfect
Marketers who combine first-party data, incrementality testing, and MMM will get close enough to make confident, revenue-driven decisions.
The goal isn’t to crown one channel as “the winner” but to understand which activities truly grow the business and invest more in them.
In the end, the marketers who thrive will be the ones who measure what matters and put their budget where it counts, even when the path isn’t perfectly clear.