Rethinking ad performance: the future of advertising attribution

Your ad data lives in silos, each with its own tracking, metrics, and rules. None of them talk to each other, and none give you the full picture. That means you’re often stuck stitching together multiple dashboards, dealing with duplicate conversions, and trying to make sense of conflicting performance signals.

Ad platforms are increasingly locked down. And thanks to privacy changes like iOS 14, even that limited visibility is now more restricted than ever. 

A recent Supermetrics report found that 66% of marketers worry about tracking consumers across channels, and 57% predict that marketing attribution will be more difficult this year due to tightened privacy rules and the fade-out of third-party cookies. 

There are some undeniable upsides to last-click attribution. It’s simple, it’s easy to set up, and it’s totally fine if you’re just getting started or need a quick snapshot of what’s driving conversions. 

But last-click only shows part of the picture. If someone spots your Instagram ad, visits your site a few times, then finally converts after clicking an email, last-click gives all the credit to the email, ignoring everything that came before. 

If you want a fuller picture of what’s actually influencing conversions across the journey, an omnichannel, linear multi-touch model is often more representative of your total marketing impact.

Let’s bring this to life with a real-world scenario.

Imagine this customer journey:

1. Impression on Meta (prospecting ad)
2. Newsletter sign-up via landing page
3. Triggered welcome flow 
4. Abandoned cart after browsing
5. SMS cart reminder with discount
6. Mobile purchase

If you’re using last-click attribution, this entire journey is written off as an SMS win. That means your prospecting ad, the sign-up flow, and every touchpoint that nurtured interest and drove consideration gets zero credit. On paper, Meta looks like it’s underperforming. In reality, it seeded the conversion.

Here’s how it’s different with Klaviyo’s linear multi-touch model in conjunction with our new omnichannel attribution:

• The Meta ad earns partial credit for introducing the customer to the brand and driving list growth.
• The email flow is credited for building trust and intent through educational content and social proof.
• The text message gets its share for providing the final nudge and closing the loop.

This view gives you granular, data-backed insight into what’s really working so you don’t pause that Meta campaign that’s filling your funnel just because it isn’t “converting.”

Marketers have been using audience sync for years to push segments into platforms like Meta and Google for better targeting—think retargeting past purchasers or building high-performing lookalike audiences. But until now, that sync was mostly one-way: helpful for targeting, but not for measuring true performance.

Now, that capability is evolving. You can still sync audiences, but you can also track how those ads influence behavior after the click. That means understanding not just who clicked your ad or triggered an “active on-site” event ad, but whether they browsed, subscribed, purchased, or came back later via another channel.

With omnichannel attribution capabilities, you can track third-party sources like Meta, Google, TikTok, and more alongside your owned channels like email, SMS, and push.

You’re no longer stuck comparing disjointed dashboards or relying on each platform’s version of the truth. Instead, you get a single view of the entire customer journey. You can see, for example, how a Facebook ad drove a newsletter sign-up, which led to an email click, and ended with a purchase after an SMS reminder.

This side-by-side visibility helps you not only spot patterns, but also identify top-performing tactics and channels, then make sharper decisions about where to invest.

Your attribution model should be customizable to reflect how your team evaluates performance. For example, you should be able to:

• Exclude bot traffic or transactional messages from attribution reports.
• Quickly apply linear multi-touch instead of last-click.
• Adjust your model after a campaign goes live. 

This flexibility means your attribution model adapts to your business, not the other way around. 

Just because an ad didn’t close a sale doesn’t mean it didn’t pull its weight. Attribution that shows the assist, not just the conversion, means you can stop cutting top-of-funnel campaigns that look underwhelming in last-click reports but are actually fueling future revenue. 

Here’s how to put that into practice:

• Look at the data by funnel stage. If Meta or TikTok campaigns consistently show up as early touchpoints before email sign-ups or purchases, keep investing. They’re building awareness and feeding your pipeline, even if they’re not the final click.
• Don’t kill a campaign too early. A top-of-funnel ad that assists conversions might not look impressive in last-click reports, but multi-touch shows its true contribution.

Clear attribution also helps you sharpen your creative and placement strategy. You can see which platforms are more likely to drive purchases, and adjust your spend, messaging, and creative accordingly. 

That insight lets you tailor messaging by funnel stage or audience type. For example:

• Tailor your messaging by journey stage. Use bold, curiosity-driven creative on Meta or YouTube to stop the scroll and introduce the brand.

• A/B test channels by audience type. If your analytics show that returning customers convert faster via email while new prospects engage more via display, adjust your creative themes and placements to reflect that.

Whether you’re managing spend yourself or reporting to a broader team, attribution transparency makes your performance easier to defend. You can prove ROI across the full journey, rather than digging up individual conversion metrics or relying on siloed platform data.

Here’s how you can do that:

• Export attribution data to show full-funnel ROI. Break down performance by channel and touchpoint to show how paid and owned work together.
• Use model comparison tools to explain the difference between last-click and multi-touch numbers, especially when justifying top-of-funnel spend.
• Build smarter dashboards. Instead of just reporting “X conversions from ads,” show how ads contribute to the entire purchasing cycle so stakeholders understand how upper-funnel investment leads to revenue down the line.

While your attribution model stays consistent at the account level, your goals should shape how you interpret performance through that lens. This is why it’s so important to get aligned on what your business is trying to achieve overall.

If you’re running time-sensitive or low-consideration offers, for example, a last-click model might be enough. But if your goal is long-term growth and building a reliable acquisition engine, you’ll want to understand how top-of-funnel activity contributes to conversions over time. A multi-touch model (like linear) can help you track those early interactions and see how they feed into the bigger picture. 

Attribution simply doesn’t work if your data lives in silos. To get meaningful insights, your ad and owned-channel data need to live in the same place.

If you’re using Klaviyo, here’s what you’d set up:

• Audience sync: This lets you sync Klaviyo lists and segments with platforms like Meta and Google, to make sure you’re targeting the right people and can track them across journeys.
• UTM parameters: Add structured UTM tags to all paid campaigns. This helps Klaviyo identify the traffic source, campaign, medium, and content so it can track the person’s path from ad to action.
• Anonymous backfill: Enable anonymous backfill to retroactively attribute anonymous site activity to known profiles once someone identifies themselves (e.g., by signing up or purchasing). 
• Extended ID cookie tracking: Use Klaviyo’s extended identity feature to track users across more touchpoints, even when third-party cookies are limited. 
• Shopify web check-out pixel: If you’re on Shopify and using Klaviyo’s integration, install the web check-out pixel to track conversions that happen through Shopify’s native check-out. 

This set-up gives Klaviyo a complete view of cross-channel behavior, making it easy to see how ads contribute to the overall customer journey.

By default, most platforms (and many marketers) rely on last-click attribution. But for anyone running omnichannel campaigns, that model’s a dead end.

In Klaviyo:

• Switch to linear multi-touch attribution to spread credit across all meaningful interactions. This model gives you a truer picture of how channels work together.
• Implement omnichannel attribution. Use Klaviyo’s built-in tracking, UTM parameters, and integrations to capture and connect interactions across email, SMS, paid ads, and on-site behavior. 
• Compare models when necessary. If you’re reporting to a performance-driven team, it can be helpful to show both last-click and multi-touch views side by side to explain spend decisions or uncover hidden channel value.