The Halo Effect: Your Paid Media Went Offline, Can You Survive Without It? via @sejournal, @jonkagan

Hello, my fellow digital marketers! This study was born out of a question that gave me a combination of irritation and renewed curiosity: “If I turned off all paid media, would my business actually suffer?”

This is a question that is as old as time (in digital marketing time that is), and just like swallows returning to Capistrano, I am posed this question every Q1, when a brand reviews my annual paid media budget recommendation.

What I thought was going to be a four-week test, actually turned out to be a three-month test with a one-month analysis.

The Scenario

The analysis was done for a fast-casual dining restaurant chain that operates 50+ restaurants across 10+ U.S. States that I was asked to do some auditing on. But, honestly, this repeats for most brands and verticals (much less so in B2B, I will note).

As alluded to earlier, the brand had a noticeable disconnect between the restaurant deciphering the impact of its website revenue and the media dollars spent, vs. wondering if it was just cannibalizing its name recognition and organic efforts. It challenged the belief that media was not contributing, and wanted to turn it off in a trial period. But more so, it just didn’t think the paid media was contributing, and it wanted to save some cash. So, we obliged.

Due to the disconnect in restaurant dining revenue being passed back to digital media, we elected to focus on its direct on-site online order of food to validate the data.

Before and after the test period, it was using search, Performance Max, paid social, digital OOH, and Display. All channels covered both prospecting and retargeting efforts.

It ran limited email efforts to its customers registered for rewards, but it does not have a mobile app, so the customer list is quite small, while its annual digital media investment is around $1.1 million.

For the analysis, we planned to pause media for five weeks in the middle of its low season (which is about four months long), and then compare the overall impact on the site before it was paused and after we brought it back.

Thrilling? Well, let’s just say some folks do get all hot and bothered around a mid-to-low impact media holdout aggregate site activity analysis.

The Important Parameters

So, there are some important things to note around this test:

  • Traditional media was never stopped, but always ran at a low level, mostly billboards and radio.
  • For unexplainable reasons, they never hooked up Search Console.
  • They run a consistent SEO effort.
  • The analysis was done on the same restaurants for all three time periods (they had a couple shutdown and one open in this time period, so that data was removed from the assessment).
  • Their primary key performance indicator is in-restaurant visits, but they struggle to connect the source back to media initiative (we use three different foot traffic tracking vendors to measure it, but they don’t have the ability to pass back the in-restaurant sales data to the visit).
  • Foot traffic leads are actually worth 15-25% more than online order sales, but we do not have true pass-through revenue for them.

Against the recommendation to do this test on an isolated market was not taken, and they did a full blackout.

Breakout of Online Orders vs. Store Visits 4/14/25 to 5/18/25 (Image from author, January 2026)

Hypothesis

In my typical (and often inappropriate snarckastic manner, or so I am told), I referred them to my 2021 article, “How Paid Search Incrementality Impacts SEO (Does 1+1=3?),”  and told them that this should be their baseline for anticipated impact. For those who don’t want to click on the link and read the article, my stance was that removing paid media and running organic only would have a grand net loss for the brand in terms of traffic and sales.

To give you a sense of performance, prior to the test, paid media accounted for ~28% of incremental site traffic and ~23% of online orders. Which in turn supports the following beliefs:

  • With paid search engine-driven traffic exiting, we expect organic to rise, but not enough to offset what paid drove.
  • With paid social out, we expect a net loss of overall social traffic, in addition to any halo impact driven by social awareness (i.e., direct to site, organic search).
  • With programmatic traffic out, we expect a decrease in aggregate search traffic and direct to site traffic.

Net-net, the loss of paid media will result in a net loss of site traffic, leading to a net loss of online sales, which will be greater than media cost that would’ve been used to generate those sales.

Data trends (Image from author, January 2026)

The Pre-Test Data

Having selected a five-week period as our control period, we reviewed the initial data upfront:

Spend Impressions Clicks/Site Visits Online Orders Revenue
Search $30,000 395,000+ 57,000+ 6,000+ $250,000+
Performance Max $20,000+ 9 million+ 27,000+ 275+ $11,000+
Social $23,000+ 12 million+ 38,000+ 40+ $500
Programmatic Display $450 19,000+ 100+ 1 $13
DOOH* $5,000 62,000+ 0 0 $0.00
Total $80,000 21 million+ 123,000+ 6,000+ $262,000+

*Digital Out-of-Home advertising (DOOH)

Additionally, organic search had 131K+ site visits (42% of total), along with 12K+ online orders (46% of total) and $532K+ of revenue (47% of total).

While direct to site traffic had 78K+ site visits (25% of total), along with 8K+ online orders (29% of total) and $315K+ of revenue (28% of total).

Based on pre-test data, every site visit (from all traffic sources) was equal to $3.61 in online order revenue.

The Test Itself

  • Organic search site visits rose 14% (+18K), orders rose 31% (+4K), and revenue rose 30% (+$161K)
  • Direct to site visits dropped 4% (-3K), orders dropped 3% (-277) and revenue dropped 5% (-$15K)
  • The single largest channel loss of traffic was social (organic+paid), which dropped 98% (-39K) in visits, and dropped 55% in orders (note this was from 80 to 36) and 27% in revenue (a loss of $400)
  • All other site non-paid media traffic channels remained relatively flat
  • Overall site visits dropped 22% (-68K+), orders dropped 9% (-2,500) and revenue dropped 9% (-$105K)
    • Since total site visits decreased by 68K+ and not 123K+, this means the halo effect from paid media of site visits is ~55K
Visits between test periods
Visits between test periods (Image from author, January 2026)

This means that, despite organic search growing, as it was not being “cannibalized” by paid media, it could not offset the traffic or sales volume that paid search and performance max contributed.

Additionally, the lack of paid awareness media (i.e., display, social, etc.) leads to a contraction of total related searches to the brand name, as illustrated by the aggregate drop in total search traffic to the site, but also a drop in direct to site traffic as well.

“But Jon, they saved on ad spend, that should be helping them come out ahead?”

Wrong.

While they didn’t spend $80K on ads. Thus, the paid media cost per paid site visit dropped from $0.64 to $0. But they lost an aggregate 68K+ visits. On average a visit to the site in the pretest period (for all traffic sources) had a revenue value back to the brand of $3.61, during the test that rose to $4.20 (increased as more direct to site and organic search took a bigger piece of the traffic contribution pie).

This means, the actual Sales Value Impact=((Avg Revenue per Paid Media Site Visit)*Direct Paid Media Visit Lost/Gained)-/+Ad Spend Saved or Spent

Another way to write that formula is SVI=((ARPMSV)*DPMVLG)-/+Ad Spend)

Meaning on the conservative side it would be:

(($2.12)*-123,572)+$79,626.40)= -$182,346.32

But the reality is, organic search rose as it was no longer being cannibalized by paid. But not by enough to offset the loss of paid, so it requires separating the loss into direct impact and halo impact.

The direct impact is similar to the formula above, but you swap paid media visits out for total site visits change (68,652), which then brings the net loss down to $65,915.84.

Then there is a halo effect of paid media on organic. Which is why non-paid visits couldn’t offset the total loss in visits when paid was out. To calculate out the halo effect impact, we would do a formula of:

Halo Sales Value Impact= (((Avg Revenue per Paid Media Site Visit)*((Paid Media Traffic Lost or Gained-Total Traffic Loss or Gained in Test Period)) 

Or, written as HSVI=(((ARPMSV)*((PMTLG-(PPMT-TTLGTP)))

Meaning on the conservative side, it would be:

((($2.12)*((123,572-68,652)))=$116,430.40

Combine the 2 outcomes together, and you get your loss of $182,346.24 explained.

This means, that by not running paid media, full impact was a net missed revenue opportunity of $182,346.32, between direct and halo.

This goes to an extremely conservative method, and does not account for store visit revenue, or any shifts in revenue per visit over time.

In addition to the traffic that would’ve stood to gain additions to their email/audience lists.

Bringing paid media back

After the 5 weeks offline, we brought media back, in fact we increased investment by 48% (no change of channels, but all incremental went to social awareness and display).  This generated 29% fewer clicks from pre-test, but increased impressions 107%.

In the post-test period, vs the test period, the return of media, at the increased investment, lead to a 21% decrease in organic search traffic. But with paid search and performance max generating enough gain to have a net positive in all of search of +6% in site visits. Overall search driven online orders and revenue both saw a 2% increase when paid was reintroduced.

The only true net loss in growth was direct to site (which was believed that it would rise when media was back in market), which decreased in traffic by 6% and orders by 10%. Overall saw a 38% lift in total site visits, but a drop of 1% in online orders (revenue was flat). The loss in online orders was exclusively direct to site traffic.

What Does This All Mean?

A variety of things.

No matter what way you cut it, the presence of paid media had a halo effect on all activity, most notably, the aggregation of paid and organic search.

Visits (Image from author, January 2026)

But the post-click impact on the site may not follow the same path.

Online orders (Image from author, January 2026)

Which means, a larger view must be taken to examine additional impact (i.e., foot traffic, loyalty club sign-ups, and LTV).

It also reinforces the concept of 1+1=3, the theory of incrementality. While no other changes were made beyond the exiting of paid digital media, and the brand remained in low season the whole way through, the actual impact of inbound traffic lost (not covered by organic) was considerable.

It also stands as a reminder: Just because a site visit doesn’t generate immediate sales/revenue, it does not mean it doesn’t serve a purpose (i.e., foot traffic).

The Takeaway

Any brand that has more paid media site traffic than non-paid site traffic, and thinks they can turn off paid and coast equally on just non-paid traffic alone, has the same mindset as any NY Jets fan (the inability to accept a very harsh reality).

But, despite my writing, I am an optimist, and I encourage brands to do a similar study, if for no other reason than to have the data on hand for when the CMO comes in saying they want to turn off paid because they don’t think they should pay for it.

But word to the wise: Don’t do what we did here, do a market holdout, so that if things go south, it isn’t system-wide.

More Resources:


Featured Image: Roman Samborskyi/Shutterstock

Microsoft’s Publisher Marketplace, Google Tag Update & Multi-Party Approvals – PPC Pulse via @sejournal, @brookeosmundson

Welcome to PPC Pulse. This week’s PPC updates come from both Microsoft and Google, all dedicated to more “behind the scenes” work.

Microsoft announced a new Content Publisher Marketplace, where it is starting to rethink how content is compensated amid the increased use of AI.

On the Google front, Google now says the standard tag is no longer the recommended setup. And in a rare security upgrade, Google Ads rolled out multi-party approvals to protect accounts from unauthorized activity.

Here’s what matters for advertisers and why.

Microsoft Ads Announces Publisher Content Marketplace

On February 3, Microsoft Ads and Microsoft AI introduced the Publisher Content Marketplace. The platform is designed to keep high-quality content publishers at the forefront of AI-driven experiences. The marketplace creates a new, transparent licensing system between content publishers and AI builders.

In the blog announcement, Tim Frank, corporate vice president of Microsoft AI Monetization, explained the need for this:

“The open web was built on an implicit value exchange where publishers made content accessible, and distribution channels – like search – helped people find it. That model does not translate cleanly to an AI-first world, where answers are increasingly delivered in a conversation. At the same time, much of the authoritative content lives behind paywalls or within specialized archives. As the AI web grows, publishers need sustainable, transparent ways to govern how their premium content is used and to license it when it makes the most sense.”

The platform allows publishers to define their own licensing terms and get paid based on how their content is used in AI responses. AI builders, in turn, get scalable access to licensed content without needing individual agreements with every publisher.

According to the announcement, Microsoft’s testing with Copilot showed that premium content “meaningfully improves response quality.” The marketplace includes usage-based reporting so publishers can see where their content is being used and how it’s valued.

Why This Matters For Advertisers

The launch of Publisher Content Marketplace matters less for what it does right now and more for what it signals about where AI advertising might be headed.

If premium content becomes a differentiator for AI platforms, the quality of the information feeding those systems could directly impact things like ad relevance and targeting.

For advertisers, that means the platforms with better content licensing deals may end up with better-performing ad products. It also suggests that Microsoft is betting on a future where AI answers aren’t just pulling from the open web but from curated, licensed content sources that have economic incentives to keep their information accurate and current.

Additionally, if Microsoft can differentiate Copilot’s ad inventory based on content quality while Google is still negotiating those types of relationships, it creates an opportunity for Microsoft to position itself as the premium option for certain verticals.

What PPC Professionals Are Saying

Navah Hopkins, Microsoft Ads liaison, also shared the announcement on LinkedIn and highlighted how “content ownership and respect for human autonomy are foundational to getting the AI web right.” Her perspective emphasized content quality over volume, which aligns with Microsoft’s positioning against competitors who may prioritize reach over accuracy.

Christoph Waldstein, senior client director Strategic Sales at Microsoft, also showed his support for the marketplace, stating, “Great to see so many premium partners join us to keep content quality high in an Agentic world!”

The marketplace is voluntary to join, so it will be interesting to see how many publishers opt in and whether the content licensing creates improvements in customer quality for advertisers running on Microsoft.

Google Says Standard Tag Is No Longer The Recommended Setup

Google communicated through various channels, including YouTube Shorts and LinkedIn, that the standard tag setup is no longer the recommended configuration for advertisers.

From the sounds of it, it appears that standard client-side tagging is being phased out in favor of Google Tag Gateway or full server-side tagging setups.

Tag Gateway works by serving Google tags from your own domain instead of from Google’s servers. This approach improves data accuracy by reducing the impact of browser privacy features and ad blockers, extends cookie lifespans in restrictive browsers like Safari, and positions the tracking infrastructure as first-party rather than third-party.

The platform is also promoting Tag Gateway through partnerships and integrations like Webflow, which automate much of the configuration that previously required technical expertise.

With Google Ads for Webflow, marketers can now  connect campaign performance to first-party data, as well as launch and optimize campaigns inside the Webflow dashboard.

Google stated that they’re bringing in more integrations to other platforms soon.

Why This Matters For Advertisers

The practical implication is that advertisers who haven’t upgraded their tagging infrastructure are likely seeing degraded data quality without realizing it. As browsers continue tightening privacy restrictions, that gap is likely going to widen.

Looking at Google’s choice of communication channels for this update, it feels like right now this is more of a technical “recommendation” to get more advertisers on board. My assumption is that it will become mandatory in the future.

To me, it signals that accounts that choose to run on outdated tag configurations won’t have the best data signal strength to compete in automated bidding environments where data quality has a huge impact on performance. That was also echoed in the first episode of Ads Decoded last week, where they talked a lot about data strength.

Google also touts that the upgrade to Tag Gateway is “effortless,” where advertisers can set this up with the CDN or CMS of their choice directly in Google Ads, Google Analytics, or Google Tag Manager. They’re removing a barrier for many small businesses, hoping to get more advertisers on board quicker.

What PPC Professionals Are Saying

Most comments on Google’s LinkedIn post are in agreement with the move to Google Tag Gateway.

Alexandr Stambari, performance marketing specialist at ASBC Moldova, gave good feedback, but also provided some critical potential gaps in transparency that I’m sure many advertisers would also ask:

“The move toward first-party tagging and Google tag gateway makes sense in today’s environment, especially with increasing cookie restrictions and a stronger focus on AI-driven optimization.

At the same time, it would be great to see more transparency on where the actual uplift comes from — the technology itself versus overall improvements in models and media mix. For many advertisers, the entry barrier (infrastructure, resources, and implementation clarity) is still not entirely clear.”

However, some PPCers are against using Google Tag Gateway and have been talking about it before Google posted their videos about it.

In a post last week, Luc Nugteren, tracking specialist, said he’s not using Google Tag Gateway because “server-side tagging offers more benefits” and because SST “isn’t restricted to Google and enables you to use a custom loader, it will help you measure more.”

Google Ads Introduces Multi-Party Approval For Account Changes

Google Ads rolled out multi-party approval (MPA), a security feature that requires a second administrator to verify high-risk account changes before they take effect. The feature was first spotted by Hana Kobzova, founder of PPCNewsFeed.com, who shared the update on LinkedIn.

Multi-party approval applies to actions like adding new users, removing existing users, or changing user roles within an account. When someone initiates one of these changes, all eligible administrators receive an in-product notification to approve or deny the request. There are no email notifications currently, which means administrators need to check the platform directly to see pending approvals.

Requests expire after 20 days if no action is taken. The system automatically blocks expired requests, and the person who initiated the change needs to restart the process if the action is still necessary. Read-only roles are exempt from the approval process.

Why This Matters For Advertisers

This seems like the right move from Google after multiple reports of account owners or agency owners have had their Google Ads accounts hacked.

While it may add some extra friction in operations, it’s more of a justified annoyance in the name of security.

For agencies managing multiple client accounts, the operational impact could be significant. If every user addition or role change requires coordination between two administrators, that adds time to onboarding processes and makes emergency access requests more complicated.

The lack of email notifications is a notable gap. Administrators who don’t log into Google Ads regularly may not see pending approval requests until they’ve already expired, which could create delays for legitimate account changes. Google will likely add email support based on user feedback, but for now, it’s a manual check-in process.

The other consideration is what happens when the only other administrator is unavailable. Google’s support documentation makes it clear that support teams can’t approve or deny requests on behalf of account owners, which means if your backup admin is on vacation or no longer with the company, you’re stuck until they respond or the request expires.

What PPC Professionals Are Saying

Many advertisers seem to be in favor of this move by Google.

Dan Kabakov, founder of Online Labs, stated:

“About time Google addressed this. The account hijacking attacks over the past few months have been brutal for agencies.”

Ana Kostic, co-founder of Bigmomo, said that “it’s a bit annoying but it’s much better than the alternative,” while in the comments Fintan Riordan, founder of VouchFlow.ai said he is “glad to see Google taking this seriously.”

Theme Of The Week: Infrastructure Upgrades May Become Requirements

This week’s updates share a common thread: What used to be optional infrastructure improvements are likely becoming baseline requirements for running competitive advertising campaigns.

Microsoft’s Publisher Content Marketplace is building the foundation for how content gets licensed in an AI-first ecosystem. Google’s push away from standard tags toward Tag Gateway is (not quite) forcing advertisers to upgrade their measurement infrastructure. And multi-party approval is adding procedural safeguards that change how account administration works.

In each case, the platforms are signaling that the old way of doing things is no longer sustainable.

More Resources:


Featured Image: beast01/Shutterstock

Breaking Into The Black Box: Unlocking Meta’s Product-Level Ad Data

Ecommerce and Meta often go hand in hand. You can give Meta a 20,000-item catalog and a budget, and with its AI-powered Advantage+ campaigns, it’ll try to pair the right person with the right product, whether that’s a new customer or someone who’s already viewed those products before.

But what’s actually happening inside that ad? And is there a way to optimize this “black box” Dynamic Product Ad (DPA) format?

Advertisers can see ad-level performance, but have no platform-native insights on which specific products are being shown, clicked, or ignored within a broad DPA.

Is The Algorithm Making The Right Decisions?

That’s exactly the question we wanted to answer.

There are three common traps brands fall into:

1. Over-segmentation: Brands that want more insight break apart their catalog into niche product sets with tons of DPAs.

  • Pros: You can give each ad a bespoke name, which tells you exactly what’s being served. Nice!
  • Cons: This reduces data density and can kill ROI. There’s also a tendency to try to predict which audiences will respond to which products, which is no longer effective for most brands since Meta’s improved Andromeda updates

2. Convoluted reporting: Brands try to infer what products Meta is prioritizing by pairing Google Analytics 4 session data (sessions by product) to Meta ads data (the campaigns/ads that sent these users).

  • Pros: Enables some analysis without falling into the “over-segmentation” pitfall.
  • Cons: Time-consuming to set up, and incomplete. This method doesn’t tell us anything about product-specific engagement within Meta; we would only be guessing at click-through-rate, spend, and impressions.

3. “Set it and forget”: Brands give up all control and let Meta take the wheel.

  • Pros: Avoids over-segmentation issues.
  • Cons: There’s a big risk in trusting the algorithm. You might be pushing products that get high impressions but low sales, effectively burning your budget and losing efficiency.

Trying to make decisions from just Meta Ads Manager UI data is a risk. Many marketers are still not confident in AI-powered campaigns.

At my agency, we created technology to solve this challenge, but fear not, I can walk you through the exact steps so you can do the same for your brand.

Our pilot client for the new technology was a major bathroom retailer investing heavily in DPAs within conversion campaigns.

Let’s go through the three phases in our journey to overcoming this ecommerce challenge.

Phase One: Surfacing Engagement Data

The first stage was visibility: understanding what was happening now within these “black box” DPA formats.

As I said above, Meta doesn’t directly report which specific product led to a specific purchase within a DPA in the Ads Manager interface. It’s simply not an available breakdown in the same way that age, placement, etc. are offered.

But the good news is that a treasure trove of insight is buried in the Meta APIs:

  1. Meta Marketing API (specifically the Insights API) is the main API we use to get all ad performance data. It’s how we’re pulling the key metrics like spend, impressions, and clicks for each ad_id and product_id.
  2. Meta Commerce Platform API (or Catalog API). This API provides the list of all product_ids and their associated details (like name, price, category, etc.).

Here are the steps:

  1. You first need to pipe API data into a data warehouse (we used BigQuery). Make sure you’re pulling the following metrics from the Insights AP: impressions, clicks, spend, ad_id, product_id. If you aren’t a developer, you can use ETL connectors (like Supermetrics, Funnel.io) to get this data into BigQuery or Google Sheets, or use Python scripts if you have a data team.
  2. Once you have these two data streams, join these APIs in a table, using a specific Join Key. We used Product ID; this is the common thread that must exist in both the Ad data and the Catalog data to make the connection work.

Once you’ve done this, you can view your ad performance data (clicks, impressions), but now with a breakdown by product.

This new, combined dataset was then visualized in a Looker Studio report template. Again, other reporting options are available.

To make sense of the data, we needed an easily navigable report rather than pages of raw data. We built the following visualizations:

Screenshot of Product scatter chart from Impression DPEx tool
Product Scatter Chart, Impression Dynamic Product Explorer (DPEx), (Image from author, December 2025)

Product Scatter Chart: Separating each product into four distinct categories:

  • “Star Performers”: High impressions and high clicks.
  • “Promising Products”: Low impressions but a high click-through rate.
  • “Window Shoppers”: High impressions but very low clicks.
  • “Low Priority”: Low clicks and impressions.
Screenshot of DPEx chart
Top 10 Product Types Chart (Image from author, December 2025)
Screenshot of DPEx chart
Bottom 10 Product Types (Image from author, December 2025)

Top/Bottom Products Bar Charts: See at a glance the top 10 and bottom 10 products by engagement.

Product Details Table: View detailed metrics for each product.

This could all be filtered by product name, product type, availability, and any other metrics we wanted (color, price, etc.).

We produced our first-ever client report for product-level ad engagement, and even with just engagement data, we learned a lot:

Creative: We used the data to improve creative briefs.

  • In our client data report, it was interesting to see how much Meta was pushing non-white products (orange sinks, green baths), despite the fact that 95% of their product sales are traditional white variations.
  • We hadn’t prioritized these products initially for the client, but have now created lots more video and creator content featuring these highly clickable variations.

Product Segmentation: We built powerful, data-driven product sets based on real engagement metrics.

  • For example, we tested showing only our most engaging “Star Performer” products in feed-powered collection ads in our upper funnel campaigns, where usually the algorithm has fewer signals to optimize towards

Efficiency: This automated a complex analysis that was previously unwieldy and time-consuming.

Crucially, for the first time, we had enough evidence to challenge Meta’s “best practice” of using the widest possible product set.

Pitfalls & Key Considerations

This was a great first step, but we knew there were some key areas that just tapping into Meta’s APIs won’t solve:

  • Engagement Vs. Conversions: The major downfall with this is that product-level breakdowns are only available for clicks and impression data, not revenue or conversions. The “Window Shoppers” category, for example, identifies products that get low clicks, but we couldn’t (in this phase) definitively say they don’t lead to sales.
  • Context Is Key: This data is a powerful new diagnostic tool. It tells us what Meta is showing and what users are clicking, which is a huge step forward. The why (e.g., “is this high-impression, low-click item just a high-value product?”) still requires our team’s analysis.

Phase Two: Evolving Meta Engagement Data With GA4 Revenue Data

We knew the above Meta-only data just explores one part of the journey. To evolve, we needed to join with GA4 data to find out what customers are actually buying after they’re interacting with our feed-powered dynamic product ads.

The Technical Bridge: How We Joined the Data

While Phase One relied on ETL connectors to pull Meta’s API data, Phase Two requires a different stream for GA4. We tapped into the native GA4 BigQuery export specifically for purchase events. This provides the raw event-level data, revenue and units sold, for every transaction.

The join isn’t a single step – but relies on two primary keys to connect the datasets:

  • The Ad ID Bridge: To link a GA4 session back to a specific Meta ad, we captured the ad_id via dynamic UTM parameters. By setting your URL parameters to utm_content={{ad.id}}, you create a magic bridge between the click and the session.
  • The Item ID Match: Once the session is linked, we use the Item ID. This must be perfectly aligned so that your Meta product_id and GA4 item_id are identical; otherwise, the model breaks.

Pitfalls & Key Considerations

Joining Meta and GA4 data sounds easy enough, but there were some key blockers to overcome.

Clean Data. The whole model breaks if your Meta ID doesn’t cleanly match your GA4 IDs. You must ensure your product catalogs and your GA4 tagging are perfectly aligned before you start.

However, our second issue is harder to overcome: attribution issues. The GA4 data will almost always show lower conversion numbers than Meta’s UI.

This is because, in our experience, Meta often “over-credits.” It benefits from longer attribution windows, including view-through conversions, and it gives itself full credit for each conversion it measures (rather than spreading out across multiple channels).

GA4 often “under-credits” channels like Meta. It uses data-driven attribution to try and give credit to multiple touchpoints. However, it is unable to completely follow user journeys, especially those that don’t include clicks to the site. This means GA4 doesn’t know to credit a social ad, even if that ad was the deciding factor in the purchase journey.

Although we’d love to be able to get a 1:1 match from each product purchase back to a specific product interacted with on Meta, neither GA4 nor Meta can achieve this insight easily. However, there’s still value in the relative insights and trends.

Here’s an example:

  • Meta’s UI: Reported our “Luxury Bath – Green” product was our top performer last month, with high volumes of clicks and impressions in our dynamic ads.
  • The Problem: When we joined our GA4 data, we saw no sales for that specific bath last month, at all, from any channel!
  • The Assumption: If we only used ad engagement data, we’d assume this product is wasting spend by generating low-quality traffic

But, by looking at all items purchased in those GA4 sessions that originated from the “Luxury Bath – Green” product, we discover that many users who clicked the bath went on to convert, just for the white variation instead.

The Insight: The “Luxury Bath” ad wasn’t a failure; it was a highly effective halo product for our client. As a result, it drew in aspirational customers who then converted to buy other products.

The Action: We can confidently commission creator content, focusing on the green bath, to draw in new users even if we know users are likely to buy a different color when it comes to purchase.

Phase Three: Performance-Enhanced Feeds

Once we had this data at our fingertips, the temptation was to focus on it purely for insights and data.

The next level was even better, using this data to create automated supplementary feeds.

It was time to bring back those four product performance segments from our scatter charts.

Using our feed management tools, we pushed the product performance segments into our Meta product feed as new custom labels. This means we were able to dynamically set new product sets based on product performance, for example, a rule was created to Product Set where Custom Label 0 equals Star Performer.

We could then conduct the following product set tests:

  • “Window Shoppers”: (High impressions, low clicks/sales). Feed these into an exclusion set to understand if efficiency improves when we remove from the feed.
  • “Promising Products”: (High CTR, high CVR, low impressions). Feed these into a scaling set with more budget to understand if demand is hidden.
  • “Star Performers”: (High impressions, high clicks). Feed these into a retargeting set to recapture engaged users with our signature ranges.

Pitfalls & Key Considerations

The tests above are simply examples of hypotheses. However, your mileage will vary! We strongly recommend structured experimentation to understand impacts on overall performance.

Is Your Brand Ready To Break Out Of The ‘Black Box’?

You can partially break out of Meta’s “black box,” and this can be a strategic move for ecommerce brands.

The journey moves from surfacing basic engagement data (Phase One) to joining it with sales data for true, profit-driven insights (Phase Two), and ultimately, to automating your strategy with performance-enhanced feeds (Phase Three).

This is how you move from trusting the algorithm to challenging it with evidence. If you’re a decision-maker wondering where to start, here are the three questions to ask:

  1. “Can you show me which specific products in our catalog are being prioritized by Meta?”
  2. “Are our Meta product_ids and GA4 item_ids identical?”
  3. “Are we capturing the ad.id in our UTM parameters on every single ad?”

If the answers to these questions are “I don’t know,” you’re probably still operating inside the black box. Breaking it open is possible. It just requires the right data, the right technical expertise, and the will to finally see what’s truly driving performance.

More Resources:


Featured Image: Roman Samborskyi/Shutterstock

The Smart Way To Take Back Control Of Google’s Performance Max [A Step-By-Step Guide]

This post was sponsored by Channable. The opinions expressed in this article are the sponsor’s own.

If you’ve ever watched your best-selling product devour your entire ad budget while dozens of promising SKUs sit in the dark, you’re not alone.

Google’s Performance Max (PMax) campaigns have transformed ecommerce advertising since launching in 2021.

For many advertisers, PMax introduced a significant challenge: a lack of transparency in budget allocation. Without clear insights into which placements, audiences, or assets are driving performance, it’s easy to feel like you’re flying blind.

The good news? You don’t have to stay there.

This guide walks you through a practical framework for reclaiming control over your Performance Max campaigns, allowing you to segment products by actual performance and make data-driven decisions rather than hope AI figures it out for you.

The Budget Black Hole: Where Your Performance Max Ad Spend Actually Goes

Most ecommerce brands start by organizing PMax campaigns around categories. Shoes in one campaign. Accessories in another. That seems logical and clean but can completely ignore how products actually perform.

Here’s what typically happens:

  • Top sellers monopolize budget. Google’s algorithm prioritizes products with strong historical performance, which means your star items keep getting the spotlight while everything else struggles for visibility.
  • New arrivals never get traction. Without performance history, fresh products can’t compete, so they never build the data they need to succeed.
  • “Zombie” products stay invisible. Some items might perform well if given the chance, but static segmentation never gives them that opportunity.
  • Manual adjustments eat your time. Every tweak requires you to dig through data, make changes, and hope for the best.

The result? Wasted potential, uneven budget distribution, and marketing teams stuck reacting instead of strategizing. You’re already doing the hard work; this framework helps that effort go further and helps you set and manage your PPC budget efficiently and effectively.

How To Fix It: Segment Campaigns By What’s Actually Working

Instead of organizing campaigns by category, segment by how products actually perform.

This approach creates dynamic groupings that automatically shift as performance data changes with no manual reshuffling.

Step 1: Classify Your Products into Three Groups

Start by categorizing your catalogue based on real performance metrics: ROAS, clicks, conversions, and visibility.

Image created by Channable, January 2026

Star Products

These are your proven winners, with high ROAS, strong click-through rates, and consistent conversions. Your goal with stars is to maximize their potential while protecting margins.

  • Set higher ROAS targets (3x–5x or above based on your margins).
  • Allocate budget confidently.
  • Monitor to ensure profitability stays intact.

Zombie Products

These are the “invisible” items that haven’t had enough exposure to prove themselves. They might be underperformers, or they might be hidden gems waiting for their moment.

  • Set lower ROAS targets (0.5x–2x) to prioritize visibility.
  • Give them a dedicated budget to gather performance data.
  • Review regularly and promote graduates to the star category.

New Arrivals

Fresh products need their own ramp-up period before being judged against established items. Without historical data, they can’t compete fairly in a mixed campaign.

  • Create a separate campaign specifically for new launches.
  • Use dynamic date fields to automatically include recently added items.
  • Set goals focused on awareness and data collection rather than immediate ROAS.

Step 2: Define Your Performance Thresholds

Decide what metrics determine which bucket a product falls into. For example:

  • Stars: ROAS above 3x–5x, strong click volume, goal is maximizing profitability.
  • Zombies: ROAS below 2x or insufficient data, low click volume, goal is testing and learning.
  • New Arrivals: Date-based (for example, added within last 30 days), goal is building visibility.

Your thresholds will depend on your margins, industry, and historical benchmarks. The key is defining clear criteria so products can move between segments automatically as their performance changes.

Step 3: Shorten Your Analysis Window

Many advertisers’ default to 30-day lookback windows for performance analysis. For fast-moving catalogues, that’s too slow.

Consider shifting to a 14-day rolling window for better analysis. You’ll get:

  • Faster reactions to performance shifts
  • More accurate data for seasonal or trending items
  • Less wasted spend on products that peaked two weeks ago

This is especially important for fashion, home goods, and any category where trends move quickly.

Step 4: Apply Segmentation Across All Channels

Your segmentation logic shouldn’t stop at Google. The same star/zombie/new arrival framework can (and should) apply to:

  • Meta Ads
  • Pinterest
  • TikTok
  • Criteo
  • Amazon

Cross-channel consistency compounds your optimization efforts. A product that’s a “zombie” on Google might be a star on TikTok, or vice versa. Unified segmentation helps you connect products to the right audiences on the right channels and distribute budget accordingly.

Step 5: Build Rules That Move Products Automatically

Here’s where the real efficiency gains come in. Instead of manually reviewing every SKU, create rules that automatically shift products between campaigns based on performance.

For example:

  • If ROAS exceeds 3x–5x over your analysis window – Move to Stars campaign
  • If ROAS falls below 2x or clicks drop below your average (for example, 20 clicks in 14 days) – Move to Zombies campaign
  • If product was added within a set time limit (for example, the last 30 days) -Include in New Arrivals campaign

This dynamic automation ensures your campaigns stay optimized without requiring constant manual intervention.

Get Smart: Let Intelligent Automation Do the Heavy Lifting

Image created by Channable, January 2026

The steps above work—but implementing them manually across thousands of SKUs and multiple channels is time-consuming. Product-level performance data lives in different dashboards. Calculating ROAS at the SKU level requires combining data from multiple sources. And building automation rules from scratch takes technical resources most teams don’t have.

This is where the right use of feed management and the right use of PPC automation really helps. For example, it can merge product-level performance data into a single view and let you build rules that automatically segment products based on criteria you define.

To see what this looks like in practice, Canadian fashion retailer La Maison Simons offers a useful reference point. They faced the same challenges-category-based campaigns where top sellers consumed the budget while newer items never gained traction.

After shifting to performance-based segmentation, they saw measurable improvements without increasing ad spend:

  • ROAS nearly doubled over a three-year period
  • Cost-per-click decreased while click-through rates improved
  • Average order value increased by 14%
  • Their dedicated new arrivals campaigns consistently outperformed expectations
  • Perhaps most notably, their previously “invisible” products became some of their strongest performers once they received dedicated visibility

The takeaway isn’t about any single tool, it’s that performance-driven segmentation works. When you stop letting one popular item take all the budget and start giving every product a fair shot based on data, the results tend to follow.

Learn more about the success story and the full details of their approach here.

Quick Principles to Keep in Mind

Image created by Channable, January 2026
  • Segment by performance, not category: Budget flows to what works, not what’s familiar
  • Use 14-day windows for fast-moving catalogues: Capture fresher signals, reduce wasted spend
  • Give new products their own campaign: Build data before judging against established items
  • Automate product movement between segments: Save time and stay responsive without manual work
  • Apply logic across all paid channels: Compounding optimization across Google, Meta, TikTok, and more

Your Next Step

Performance Max doesn’t have to feel like handing Google your wallet and hoping for the best. With the right segmentation strategy, you can restore control, surface overlooked opportunities and make smarter decisions about where your budget goes.

Curious whether your product data is ready for this kind of optimization? A free feed and segmentation audit can help you find gaps and opportunities, no commitment, just clarity.

Because better data leads to better decisions. And better decisions lead to results you can actually control.


Image Credits

Featured Image: Image by Channable Used with permission.

In-Post Images: Images by Channable. Used with permission.

How Much Of Your Paid Media Budget Should Be Allocated To Upper Funnel?

Determining a budget split between upper and lower-funnel is a recurring topic in paid media.

Upper-funnel campaigns (typically awareness and interest) create future demand, while lower-funnel campaigns capture existing demand and are built to drive action.

Knowing where the sweet spot is with budget allocation is a skill, and requires a sound knowledge of incrementality and how to balance immediate efficiency with long-term demand creation.

In this post, I’m going to explore the data, strategies, and channel considerations to help you find an optimal mix.

The Importance Of Upper Funnel Investment

Within paid media, it’s very tempting to pour the majority of budget into the quickest wins that yield the highest returns. It makes sense on many levels, especially when teams are budgeting (and working to) strict forecasts and targets.

However, neglecting upper-funnel spend can hurt your long-term growth, with research showing that cutting brand awareness campaigns to save money or simply avoiding this type of activity can backfire.

For example, a BCG analysis found companies that slashed brand marketing saw significantly worse outcomes, having to regain their lost market share later, requiring $1.85 in spend for every $1 saved from cutting back.

In a roundabout way, suggesting that saving a dollar today on branding can (in some cases) cost nearly two dollars tomorrow.

And it’s not just efficiency; the growth impact of neglecting brand building can be detrimental, too.

In the same study from BCG, bottom-quartile brand spenders had sales growth rates 13% lower than top-quartile brand spenders, indicating brands that underinvest in awareness suffer from lower sales growth in the long term.

They also converted aware consumers to buyers at a lower rate (a 6% weaker conversion from awareness to purchase than top-brand spenders).

Studies like this prove that upper-funnel activity isn’t just a nice-to-have, or a place to use budget left over from lower-funnel spending; it directly influences revenue trajectory, market share, and even shareholder returns.

At this point, you’re probably thinking, “What do you mean by upper-funnel activity?” So let’s have a top-level run-through.

Upper-funnel campaigns plant the seeds by reaching new audiences and generating interest in audiences who may not yet be familiar with your brand.

Think Meta or Pinterest campaigns serving ads to new users as part of broad audiences, interest-based cohorts, or lookalike lists, all excluding your current customer base and/or users who have interacted with your brand.

Think YouTube or GDN campaigns serving ads to in-market, affinity, or custom audiences, again, all while excluding your current customer base.

For this post, we’re focusing specifically on paid search and paid social, with a supporting role from display advertising served through Google and Microsoft.

While programmatic, out-of-home, TV, connected TV, PR, and other channels can all be effective for upper-funnel advertising, they fall outside the scope of this piece.

My aim here is to focus on how to allocate budget toward top-of-funnel activity, specifically through paid search and social platforms.

Balancing Short-Term Performance And Long-Term Brand Building

While the exact percentage will vary by business, a number of frameworks and studies offer guidance on balancing upper vs. lower-funnel spend.

The most well-known being Les Binet and Peter Field’s research into marketing effectiveness, which suggests roughly a 60/40 split.

This translates into 60% of ad budget for brand building (upper-funnel) and 40% to direct activation (lower-funnel) as a rough starting point.

This 60/40 rule isn’t rigid, but it underscores that at least half (if not more) of your spend should typically go toward awareness and brand in order to maximize long-term growth.

Other models follow suit and emphasize a hefty allocation to upper-funnel activities.

For instance, many marketers use a 70-20-10 rule  (adapted from a learning model) to diversify marketing investments: 70% on proven “always-on” channels, 20% on new or emerging channels, and 10% on experimental ideas.

Often, those proven channels include your core lower-funnel performers, while a portion of the 20% and 10% go toward upper-funnel initiatives.

Another approach, specific to paid media funnel stages and widely used in paid social campaign structuring, is a 60-30-10 funnel split: about 60% of budget for prospecting and awareness, 30% for mid- to lower-funnel retargeting, and 10% for closing at the bottom of the funnel.

This model ensures the majority of spend focuses on feeding the funnel with new prospects, while still dedicating budget to nurture them down to conversion.

Is every business other than yours running these exact models? Nope.

Does every business ensure it allocates sufficient media budget for upper funnel? Nope.

A 2024 CMO survey, found that only 31.2% of budget was allocated to long-term brand building vs. 68.8% to short-term performance on average, the opposite of what we’re told from industry leading studies, and this imbalance shows how pressure for quick ROI can overshadow brand investment and from working within paid media for a decade and a half, this is something I see time and time again.

Studies and guidelines are great, but in reality, there really isn’t a one-size-fits-all answer to the exact percentage of budget to allocate for upper-funnel, and it depends on factors like your industry, growth goals, and brand maturity.

For example, a new market entrant or a brand in a highly consideration-driven category (like automotive or B2B tech) may need to invest heavily in awareness and education since customers won’t convert without multiple touches and trust-building.

In contrast, a well-known brand in a transactional ecommerce vertical might get by with a lower percentage on upper-funnel, especially if it already benefits from high awareness.

Evaluate your current situation: If you’re in a crowded consumer goods market (e.g., retail fashion), strong branding and broad reach can differentiate you, whereas in a niche B2B service, thought leadership content and awareness efforts might be what fills the pipeline for your sales team.

The one certainty with this topic is that completely ignoring upper-funnel advertising with paid media is not good.

Even if short-term conversion pressures are high, dedicate a healthy portion of your budget to feeding the funnel.

A useful mindset is to treat awareness spend as an investment in future revenue.

As marketing effectiveness veteran Mark Ritson advocates, you must balance “the long and the short of it,” fund the brand for long-term growth and performance marketing for short-term sales.

Many successful companies treat brand marketing as “always-on” (continuous) rather than a luxury to add when times are good.

In practice, this could mean making sure, say, 20-30% of your paid search and social budget is consistently reaching new cold audiences at any given time, even if attribution for those dollars is not immediately obvious (more on that later).

What Does Upper-Funnel In Paid Search And Paid Social Look Like?

Translating budget allocation into channel strategy requires understanding how each paid media channel fits into the funnel.

Paid media is not one-size-fits-all; channels like paid search, paid social, video, and display each serve distinct roles across the funnel, from awareness to conversion.

Here are a few approaches to upper-funnel budget allocation across key channels:

Paid Search (Google & Microsoft Ads)

Paid search is typically considered a lower or mid-funnel channel; the reason being, this channel is often seen as a place to capture users who are actively searching for a product/service, often indicating intent.

Advertisers frequently split their campaign groupings into brand and non-brand, driving visibility in line with query types across search and shopping networks.

Imagine you run an ecommerce store for sneakers, you may want to serve brand ads to tailor messaging, control, brand protection, incrementality, etc., and for non-brand, you may want to serve ads for queries like “black Nike GT Blazer low” or “Asics Novablast 5,” the sole purpose being to drive direct sales.

There’s arguably an element of upper funnel in non-brand search as advertisers enter auctions for queries that do not contain their brand, and in many cases exclude their website visitor lists, so when a user searches for a query like “black size 10 running shoes” and click through, the advertiser will be getting their brand in-front of new audiences, however, the objective of the campaign is not one of awareness.

Read More: Tips For Running Competitor Campaigns In Paid Search

Display (Google & Microsoft Ads)

While not always front of mind for upper-funnel strategy, the Google Display Network (GDN) is great for reaching new audiences at scale as it spans over 35 million websites and apps, including YouTube, Gmail, and top-tier publisher inventory.

This breadth gives advertisers the ability to serve visually engaging ads across a vast portion of the open web, tapping into contextual, affinity, and in-market audiences.

For upper-funnel campaigns, display is often used to spark interest through static or video creative, product banners, or lifestyle-led visuals that introduce the brand to users in relevant contexts.

With options like responsive display ads, you can dynamically test creative combinations and reach a broad but targeted audience, saving time and money as resources can be freed up that would have been spent on creative development.

When allocating budget, display may not command as much as social or video initially, but it serves a valuable supporting role in prospecting and awareness.

Brands in verticals like consumer goods, travel, or SaaS can use Display as a cost-effective way to expand, reach new audiences, and drive visibility and traffic to site.

Read More: What Are Display Ads: A Complete Guide For Digital Marketers

Paid Social (Meta, Instagram, TikTok, LinkedIn & More)

Paid social is one of the most common types of advertising for upper-funnel marketing.

Platforms like Facebook/Instagram (Meta), TikTok, Pinterest, LinkedIn, and others offer rich targeting options to get your message in front of people who have never heard of you, but who fit the profile of your target customer.

Nearly three-quarters of the U.S. population (73%) were active social media users. For advertisers, this means the audience they want to reach is likely out there scrolling a feed.

For upper-funnel campaigns, social ads shine by allowing you to target based on interests, demographics, behaviors, lookalike audiences, and more, pushing visually engaging content to users who aren’t actively seeking your product yet.

When allocating budget, a significant chunk of your prospecting (new customer) budget will likely go into paid social.

You could use short-form video ads showcasing your brand story or product in use, carousel ads with inspirational lifestyle imagery, or interactive polls that get people interested.

The goal at this stage is not an immediate sale (though it’s great if it happens, and it does), but to introduce your brand, value proposition, or content to a relevant audience as efficiently as possible.

Read More: How Brands Are Measuring Social Media Impact

YouTube And Digital Video

No discussion of upper-funnel paid media budget allocation is complete without YouTube and online video platforms.

YouTube is effectively the new prime-time TV for many demographics, blending reach and targeting with the storytelling power of video.

YouTube ads can achieve massive scale, with 53% of marketers using YouTube to achieve various objectives such as reach, awareness, and conversions.

With YouTube’s advanced targeting (by interests, demographics, in-market intent, topics, etc.), you can home in on relevant audiences for your brand messaging at scale, and drive reams of valuable data.

Recent forecasts bolster advertisers’ confidence in YouTube’s ROI, with 44% of marketers planning to increase their YouTube marketing budget.

The momentum is driven by video’s effectiveness in lifting awareness and brand favorability.

Kantar research, for instance, has shown YouTube ads can substantially boost unaided brand awareness and other brand metrics, underlining the platform’s upper-funnel impact.

For practical budgeting, treat YouTube similarly to how you’d treat television in a media mix, a primary reach vehicle.

The difference is, YouTube allows flexible budgets (you can start small and scale) and measurable results (you can track views, clicks, and even use Brand Lift surveys to measure ad recall and brand interest).

If you’re in a consumer-facing vertical like electronics, fashion, or automotive, you might allocate additional budget to YouTube for big awareness pushes around new product launches or campaigns, too, in addition to always-on brand building.

Even in B2B or niche markets, consider using YouTube for educational top-of-funnel content (e.g., explainer videos, industry thought leadership) targeted to relevant audiences.

Read More: 10 New YouTube Marketing Strategies With Fresh Examples

Measuring Upper-Funnel Impact And Winning Buy-In

One reason many companies double down on lower-funnel spending is that it’s directly measurable; you see clicks and conversions, which please the performance dashboard and finance team.

Upper-funnel efforts often lack that immediate clarity on attribution, making it harder to justify budget to skeptics.

This is why measuring the impact of upper-funnel campaigns is crucial to determining the right budget allocation (and getting organizational buy-in to maintain and/or scale that spend).

Start by defining key performance indicators (KPIs) for upper-funnel campaigns that tie to your objectives.

These will be different from pure conversion metrics. Common upper-funnel KPIs include:

  • Reach and Impressions: How many unique people saw your ads? How many people did you reach?
  • Engagement Metrics: For example, video views (and view-through rates), social shares, comments, likes, or clicks on content. If people are engaging, your message is resonating at least enough to spark interest.
  • Click-Through Rate (CTR): While upper-funnel ads often have lower CTRs than the likes of Search Ads, a healthy CTR indicates the creative and targeting are attracting interest among a cold audience.
  • Brand Search Lift: Track the volume of searches for your brand name and/or direct traffic to your website during and after campaigns. An increase can signal that awareness efforts are causing more people to seek you out.
  • New User Acquisition: Look at the percentage of new visitors or new customers acquired. Upper-funnel campaigns should feed new people into the pipeline.
  • Brand Lift Studies: Use tools like Facebook’s Brand Lift or YouTube Brand Lift surveys, which can directly measure ad recall, brand awareness, and consideration among those exposed vs. a control group.

It’s also important to measure impact on a wider scale, taking a step back and analysing exactly how your upper-funnel spend impacted the business.

For example, you might find that regions where you ran a heavy awareness campaign see higher conversion rates in the subsequent weeks or months.

Techniques like marketing mix modeling or incrementality testing can help connect the dots.

Incrementality is essentially determining how much extra business an upper-funnel campaign drove that would not have happened otherwise.

You can test this by using holdout groups (e.g., show ads to 90% of your target audience but withhold them from 10% as a control, then compare behaviors), or by pausing campaigns and seeing if sales dip.

That means reporting beyond vanity metrics. For instance, instead of just saying, “Our video ad got 100,000 views,” translate that into, say, “Our brand lift study indicates an 8-point increase in awareness in our target market, which correlates with a 20% lift in branded search volume the following month.”

By connecting awareness metrics to leading indicators of sales, you make a case that those dollars are working hard.

And finally, adopt a test-and-learn approach.

If uncertainty is high, start by allocating a modest portion (say +5-10% shift) of your budget to upper-funnel campaigns for a period, then measure results.

If you can show that leads or branded searches grew, or cost per acquisition improved downstream, it will be easier to argue for maintaining or even increasing that allocation.

On the flip side, if an upper-funnel tactic isn’t performing, refine the creative or targeting rather than immediately cutting the budget, optimization is usually the answer, not abandonment, when it comes to new funnel initiatives.

Key Takeaways

Determining how much of your paid media budget to devote to the upper-funnel is a strategic decision that should be informed by both evidence and your unique context.

The data is clear that brand awareness and prospecting deserve a significant share of spend, even though many firms today allocate far less to it than they once did.

The exact figure will depend on your goals, industry, and growth stage, but the guiding principle is to invest enough in upper-funnel marketing to continually feed your future customer pipeline.

Underinvesting in awareness may boost short-term efficiency, but it eventually leads to stagnation and higher costs to reignite growth later.

In practice, this means making room in your plans for campaigns that build brand equity, engage new audiences, and create demand, even if they don’t convert immediately.

Whether it’s a YouTube video campaign reaching millions of potential customers, a series of TikTok ads riding the latest trend to put your brand on the map, or a broad Display campaign educating people about a problem your product solves, these efforts ensure your lower-funnel tactics have a steady stream of interested prospects to convert.

The upper-funnel and lower-funnel are interdependent; success comes from funding both appropriately and making them work in tandem.

So, how much of your budget should go to upper-funnel?

Enough that you’re confident you’re driving robust awareness and demand generation, not just scraping the bottom of the barrel.

For many, that will be a considerably larger portion than they currently allocate.

Aim for a balanced mix grounded in research and test data, adjust to your business needs, and then track the results.

With the right allocation, your paid media can both capture the immediate sales and expose your brand to new audiences, fueling both immediate performance and sustainable growth.

More Resources:


Featured Image: Anton Vierietin/Shutterstock

Paid Media Marketing: 8 Changes Marketers Should Make In 2026 via @sejournal, @brookeosmundson

Paid media didn’t slow down last year. If anything, the platforms made sure we stayed busy.

Google rolled out more AI-assisted ad creation features, new Performance Max reporting updates, and continued refining how AI-influenced results shape visibility across search.

Microsoft pushed forward with its own set of AI tools inside Ads and Copilot, along with quality updates that changed how some advertisers measure performance. Meta expanded Advantage+ capabilities and tightened its recommendations for creative structure.

We also saw strong momentum from platforms that used to sit on the sidelines. TikTok introduced more search-focused ad placements. Reddit continued improving its targeting and creative tools.

Privacy shifts kept moving as well. Targeting options continued evolving, and some long-standing measurement assumptions started to feel less reliable. Marketers had to adjust how they test, track, and validate results across every channel.

As we head into 2026, the message is familiar but still true. You can’t always rely on what worked a year ago, and you can’t assume the platforms will keep things the same. This list focuses on the changes that matter most right now. These are practical adjustments that help teams stay competitive without rebuilding everything from scratch.

Let’s walk through the strategies worth prioritizing this year and why they deserve your attention.

1. Embrace The Shift To Conversational AI In Ad Creation

Conversational AI tools like Google’s Gemini and Microsoft’s Copilot enable ad creation and optimization in a more fluid, interactive way.

They’re becoming essential for marketers who want to scale ad variations without exhausting creative resources.

If you’re looking to test and scale how this can work for you, start small with AI-generated ad copy tests. Use the conversational AI tools within the Google Ads platform to create a few new ad variations that differ from your standard copy.

For instance, if your current ads are heavily CTA-focused, let the AI suggest more storytelling or benefits-driven language and test these versions in a limited campaign to gauge performance.

Another tip is to start experimenting with ad personalization at scale. AI tools allow you to input audience insights, such as location or interests, to create tailored ad variations.

Create segmented ads that appeal to different demographics or psychographics and use split testing to identify which approach resonates best.

Lastly, whenever you’re using AI-generated content, make sure to set aside time to review those suggestions monthly. Take note of recurring suggestions that could highlight hidden opportunities or adjustments you may not have initially considered.

2. Refine Ad Targeting With Data Privacy In Mind

With the unreliability of third-party cookies, the upcoming year marks the need for refined targeting strategies that balance effectiveness with privacy.

Tools like Google’s enhanced privacy features and Microsoft’s predictive audience segmentation help ensure you’re reaching the right users in a compliant way.

Now’s the time to develop a robust first-party data strategy. Start by auditing your first-party data to identify gaps and potential sources for future data.

You can also utilize your customer relationship management (CRM) tools and website data collection to capture behavior-based insights and create audience segments you own.

Additionally, because both Google and Microsoft allow Customer Match solutions, it’s a great time to review those policies.

Use tools like cookie consent managers and transparency banners to build trust and ensure you’re gathering data responsibly. If you don’t, you’re at risk of not being able to use first-party data solutions by the ad platforms.

When creating a consent-based tracking strategy, it’s also a good idea to proactively share with users how you use their data and offer clear opt-out options. Transparency is key in this two-way buyer and seller relationship journey.

3. Optimize For AI-Driven Search Ad Placements

AI-generated search summaries, especially in Google’s AI Overviews, are creating new ad placements and impacting traditional ad performance. This trend requires close monitoring and proactive adjustments to stay competitive.

As these new ad placements continue to roll out, here are a few tips to make sure your PPC ads are optimized for this new wave of AI content.

  • Monitor CTRs On AI-Influenced Placements: Start tracking the click-through rates of ads appearing in AI-generated results versus traditional SERPs. This insight can help you understand whether AI-generated placements impact user engagement and identify areas for improvement.
  • Create Specialized Assets For AI Overviews: Use images, headlines, and descriptions designed for short attention spans. For instance, include a compelling image and a clear, concise CTA in your ad to boost appeal in this new placement.
  • Review Performance Max Insights Regularly: Google’s Performance Max campaigns, which include AI-driven placements, provide insights into what combinations work best across channels. Use this data to refine ads in other campaigns where similar placements are available.

4. Lean Into Multi-Channel Campaign Integration

With consumers using multiple platforms interchangeably, paid media strategies must embrace an integrated, omni-channel approach.

Platforms like TikTok and Reddit have built out more robust ad offerings, providing marketers with more cross-platform synergy.

Start by mapping out a cross-platform customer journey. Outline your audience’s touchpoints across different platforms.

For instance, if your customer typically discovers products on TikTok but purchases through Google Shopping, ensure you’re present and active on both channels with consistent messaging.

Another item to keep in mind is utilizing platform-specific metrics to refine your strategy.

Each platform has unique engagement metrics. For example, on TikTok, you can monitor completion rates and engagement (likes, comments) to assess content effectiveness.

LinkedIn, on the other hand, is a place to focus on connection and message response rates.

Tailor your content based on what performs best on each channel. Each channel should have a different content strategy, not just putting the same ads across all platforms, hoping that one of them will click with a user.

5. Optimize Creative Customization With AI Image Editing

AI-powered image editing allows for rapid customization across visuals, which is critical for multi-audience campaigns.

Canva’s integration with Google Workspace and Microsoft’s AI image generator simplifies the creative process, enabling customization without extensive design resources.

To make the most of these AI editors and integrations, start with creating templates for faster customization.

Design or download templates on Canva that match your brand guidelines, making it easy to adjust colors, fonts, and messages for different audiences with minimal effort.

The templates can help you maintain visual consistency while catering to different segments.

To take it up a notch, try running A/B tests on custom visuals. Create two or more variations of AI-edited images to test different elements.

When testing creative, make sure to test differences that are noticeable enough. Track which visual styles drive the most engagement, and use those insights to guide future designs.

If you’re targeting multiple locations in your ads, use AI tools to adjust visuals for regional appeal.

For example, if you’re running an ad in New York and California, you can use AI to create images that feature landmarks or seasonal elements relevant to each location.

6. Enhance Attribution Tracking And Adjust KPIs Accordingly

A multi-device world demands better attribution tracking to understand the entire customer journey.

Google’s Enhanced Conversions and Microsoft’s Customer Insights provide more reliable data across touchpoints, helping marketers adjust KPIs to reflect complex engagement patterns.

To start, review enhanced conversions for first-party tracking to determine if this makes sense for your account.

Enhanced Conversions capture data from form fills or purchases to match offline actions back to Google Ads. When setting this up, make sure your campaigns reflect actual conversions, not just clicks, allowing for more accurate reporting.

Additionally, if you’re still using Last Click attribution models, you will be left in the dust.

It’s time to move beyond last-click attribution to track the impact of each customer touchpoint. You can use Google Analytics or Microsoft’s attribution reports to assess the role of each ad in a customer’s journey, and allocate credit accordingly.

Lastly, when it comes to measurement, it’s time to evolve your key performance indicators (KPIs). Not every channel in your marketing mix should be measured by direct purchases.

Just last year, in North America, the average person owned 13 devices – a 63% increase from 2018.

Users leverage multiple devices during their purchase journey, accounting for more visits but fewer conversions. No wonder conversion rates are decreasing!

For example, if you’re running a brand awareness campaign on TikTok for an audience who’s never heard of you, your KPIs should not be measuring purchases.

Track meaningful metrics like engagement rates, increase in branded search queries, or time on site to understand how those platforms contribute to long-term brand growth and loyalty.

7. Make Influencers Part Of Your Marketing Model

Influencer marketing still has value. But the rules have changed. What used to feel like a side bet now needs to operate with the same discipline you apply to any other channel.

One of the biggest shifts in 2025 was the rollout of Creator Partnerships inside Google Ads. The new tool lets brands find YouTube creators who already mention or align with their products, request to link their content directly in Ads, and then promote that content as ad assets.

That matters because it addresses many of the traditional challenges of influencer marketing.

Brands no longer have to manage a separate workflow or use external tools to run creator campaigns. Everything can be done natively inside Google Ads. Finding creators, getting permission, promoting videos, building remarketing audiences, and tracking performance – it all happens in the same place as your other media.

This integration changes what influencer marketing should be. Instead of treating creator content as a loose “boost,” treat it as another media channel that you plan, test, track, and optimize.

When you find a creator whose audience overlaps yours, link their video, promote it via “Partnership Ads,” and compare performance against other video or display placements. Use the same ROI expectations, the same reporting discipline, the same budget scrutiny.

That does not mean every influencer partnership needs to run through Creator Partnerships. But for brands that want to take creator content seriously, this is now the clearest path forward.

Influencer marketing can still introduce your brand to new audiences, but only if it becomes part of a broader, data-driven media mix rather than a side experiment.

8. Invest In Brand-Owned And Emerging Media Channels

Paid platforms can shift without much warning, which is why brands need more stability built into their mix. That stability comes from channels you control and channels that offer predictable reach without relying entirely on algorithm changes.

Brand-owned channels like email, SMS, and your CRM audience lists continue to grow in value as privacy rules tighten. These channels help you stay connected with people who have already shown interest, and they support every other part of your media strategy. When your first-party data is strong, your targeting improves across search, social, and display.

At the same time, emerging media channels are becoming easier to test and measure.

Connected TV, podcasts, retail media networks, and social commerce have grown into meaningful sources of reach and intent. Many brands are now seeing that a small, well-planned investment in these channels helps lift branded search, engagement rates, and assisted conversions across their entire account.

You do not need to adopt every new channel. You only need to choose a few that match your audience and test them with clear goals.

Look for indicators like uplift in search demand, stronger remarketing pools, or improvements in cross-channel efficiency. When these channels support your paid campaigns, they earn a long-term place in your strategy.

The brands that put effort into these areas now will be less dependent on any single platform. They will also see more consistent performance as auctions change, costs fluctuate, and targeting evolves throughout the year.

Your 2026 Plan Should Be Evolving

Paid media will keep shifting this year, but the path forward does not need to feel overwhelming.

The changes outlined above reflect what marketers are running into every day across search, social, retail media, and emerging channels.

None of these updates requires a complete rebuild. They simply call for a more intentional approach to testing, measurement, creative, and channel mix.

The advertisers who stay close to the data, spend time understanding how each platform is evolving, and make steady adjustments will see the most consistent results. The year ahead is less about chasing every new feature and more about choosing the changes that actually strengthen performance.

If you focus on the areas that matter, you’ll be in a strong position to keep improving your campaigns as the platforms continue to evolve.

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Featured Image: Anton Vierietin/Shutterstock

Google-Engaged Audience: Worry-Free Remarketing, Or A Waste Of Money?

Are you tired of remarketing headaches in your Google Ads account? In a time when we’re all facing increasing privacy restrictions, browser setting changes, and complex tracking setups, building reliable audiences can feel overwhelmingly difficult. What you may not know is that Google quietly launched a new type of “Your data segment” called the Google-engaged audience last year – and it’s still so underrated.

Available to every Google Ads account, this segment represents an elegant solution to a complicated problem. But for advanced Google Ads specialists who typically demand granular control and deep data insights, the simplicity of this audience raises a pivotal question: Is this worry-free segment a reliable source of high-quality traffic? Or will the Google-engaged audience potentially waste your time and budget?

In this article, I’ll share exactly what the Google-engaged audience is and how it works, original data comparing the Google-engaged audience to other website-based remarketing solutions, and when this segment may (or may not) make sense for your Google Ads strategy.

What Is The Google-Engaged Audience?

The Google-engaged audience is the newest type of “Your data segment” available in Google Ads. I love using and recommending this audience segment because it elegantly solves many of the complex implementation issues associated with traditional remarketing solutions.

Here’s how it works: Every Google Ads account is automatically populated with one Google-engaged audience segment. You can find yours under Tools > Shared Library > Audience Manager > Your data segments. Critically, the Google-engaged audience requires no Google tag, no account linking, and no data uploads.

Instead, this segment populates whenever a user clicks to your website from a Google property. For example, when they click from:

Why The Google-Engaged Audience Is So Powerful

The Google-engaged audience is helpful for small business owners because they don’t need to install the Google tag, connect Google Analytics, or sync their CRM with Google in order to start remarketing. It’s just there, there’s just one, it just works.

But small business owners aren’t the only ones who should be looking into using this audience type. Since users join this list when they click to your website from a Google-owned property, Google knows exactly who these users are (most are signed in to Google). Google has a first-party relationship with these users.

Because Google handles user consent and tracking within its own ecosystem, and “captures” those users for you before they leave the Google ecosystem, you get a high-quality audience that is generally more reliable and robust than third-party solutions, which suffer from challenges around browser settings, privacy controls, and consent management frameworks.

In short, it’s an easy-to-use, high-quality audience of people who visited your website from Google.

Where The Google-Engaged Audience Falls Short

Despite its clear benefits in data quality and ease of implementation, the Google-engaged audience does have some limitations that may make it unsatisfying for you to use.

The first constraint is the obvious one: This audience segment only tracks people who click to your website from Google-owned properties. This means that your Google-engaged audience will not capture everyone who visits your website from other sources, such as:

  • Direct traffic.
  • Social media traffic.
  • Non-Google paid ads (Meta, TikTok, etc.).
  • Email traffic.
  • etc.

If a significant portion of your website traffic is not coming from Google, then your Google-engaged audience may not be as useful for your campaigns.

Next, the Google-engaged audience is not compatible with the Google Display Network (GDN). This is because the GDN is mostly made up of non-Google-owned properties, so Google doesn’t have as robust audience data about those users. This means that you can’t use this audience in a standard Display campaign, and you can’t use it on Display inventory within other campaign types, such as Search, Demand Gen, or Video campaigns. Keep this in mind if a significant portion of your Google Ads investment is going towards the GDN.

Finally, while the simplicity of one single Google-engaged audience may be welcomed by small business owners, it doesn’t afford the granularity that large advertisers may crave. Since every account receives only one Google-engaged audience segment, there is no built-in mechanism to create specific segments based on when the user visited, what pages they visited, what actions they took, etc., unlike the granular options available with tag-based lists or Google Analytics lists.

How Does The Google-Engaged Audience List Size Compare To Other Types Of Remarketing Lists?

To provide a data-driven perspective on the usefulness of the Google-engaged audience, I conducted an original study comparing the size of the Google-engaged audience to two other types of “Your data segments” across a dozen advertisers: the “All Visitors” list from the Google Ads tag, and the standard “All Users” list from Google Analytics 4 (GA4). When comparing all three lists for the same advertiser, which list was the largest? Which was the smallest? How did this vary across Google’s inventory?

Google-Engaged Audiences Are Generally Larger Than Google Tag-Based Audiences

In my study, I found that the tag-based All Visitors list was usually significantly smaller than the Google-engaged audience, across all eligible inventory.

On average, the Google tag-based remarketing audience was:

  • 62% smaller than the Google-engaged audience for Search inventory.
  • 61% smaller on YouTube inventory.
  • 90% smaller on Gmail inventory.

The takeaway: If you’re relying exclusively on the Google tag for your remarketing, you are likely missing out on a lot of users. This issue is likely exacerbated if you are not using data-preserving solutions like enhanced conversions or Consent Mode.

Google-Engaged Audiences Are Generally Smaller Than Google Analytics Audiences

My study found that the Google-engaged audience was smaller in size than the Google Analytics default “All Users” list, but not on Gmail.

On average, the GA4 “All Users” audience was:

  • 28% larger than the Google-engaged audience for Search inventory.
  • 46% larger on YouTube inventory.
  • 10% smaller on Gmail inventory.

This is more in line with what I would have expected, since GA4 captures audiences from all sources, not just from Google-owned properties. In fact, I would have expected the difference to be even larger, and was surprised by how robust the Google-engaged audience is on Gmail inventory.

Remember, the “size by inventory” looks at how many active matched records Google can find on Search, on YouTube, on Gmail, and on the Display network. While Google seems to match users quite nicely in Search and YouTube, it seems more difficult for the system to match users on Gmail – unless, of course, they’re coming from the Google-engaged audience, where Google already knows exactly who they are. I call this the “Gmail dropoff.”

The takeaway: The Google Analytics audience makes a good default for website-based remarketing. If you are running a Demand Gen campaign, however, and are explicitly looking to remarket to users via Gmail, consider adding the Google-engaged audience to your audience targeting alongside your Google Analytics audience.

Is The Google-Engaged Audience A Waste Of Money?

Absolutely not! I’ve seen dozens of my Google Ads coaching clients see great results when targeting the Google-engaged audience, specifically in Demand Gen campaigns where the focus is on Google-owned inventory. I’ve seen this audience segment be especially useful for freelancers and agencies working with local service providers, since they can just check a box and get remarketing live without having to worry about tags or integrations.

You can also consider targeting, observing, or excluding your Google-engaged audience in Search and Shopping campaigns, as either a complement or replacement for how you would use a website-based remarketing list in your strategy.

I would not, however, recommend using the Google-engaged audience in your Performance Max audience signals, or as the seed list for a Lookalike, as it is too broad to be useful in those scenarios. In fact, I don’t recommend using any website-based remarketing in these scenarios; in my opinion, for an audience signal or seed list, you should only use an actual customer list.

To conclude, the Google-engaged audience is a clear example of worry-free remarketing. It is built on a durable foundation of Google’s own first-party data, bypassing the technical headaches and privacy challenges associated with traditional tag-based remarketing. It is especially useful for small business owners, but can also be helpful for all practitioners running Demand Gen campaigns due to its advantages on Gmail inventory. When in doubt, layer the Google-engaged audience alongside your existing Google tag or Google Analytics-based website remarketing segments in your Search, Shopping, Demand Gen, or Video campaigns.

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Featured Image: Roman Samborskyi/Shutterstock

How To Maximize Paid Ads Profitability With A Strategic Landing Page Audit

Your campaigns are only as strong as the pages they lead to. You can have the most targeted ads, the sharpest copy, and a budget that makes your CFO nervous. But if your landing page doesn’t deliver on what the ad promised, you’re leaving money on the table and feeding poor signals back into your campaign algorithms.

Landing pages are where intent meets experience. When they align, conversion rates increase. When they don’t, even high-quality traffic bounces, and your cost-per-acquisition (CPA) spirals upward.

This post walks through the core elements of a high-performing landing page strategy. This strategy is one that not only converts visitors, but also strengthens your ad campaigns. Whether you’re running Google Ads or Meta campaigns, these landing page strategies apply.

Why A Landing Page Audit Matters To Advertisers

Most advertisers focus heavily on the ad itself: the creative, the targeting, the bid strategy. That makes sense. But the landing page is where the actual conversion happens. It’s the final step in the funnel, and it has a direct impact on campaign performance.

Here’s why landing page audits should be a regular part of your paid media workflow:

Better Landing Page Conversion Rates Mean Lower CPAs

When more visitors convert, your cost per conversion drops. That gives you more room to scale or reinvest budget into other channels.

Stronger Signals Improve Algorithm Performance Every Click, Scroll

Platforms like Google and Meta rely on conversion data to optimize your campaigns. If your landing page isn’t converting, the algorithm receives weak or misleading signals, which limits its ability to find high-intent users.

User Experience On The Landing Page Influences Quality Score

Google rewards landing pages that are relevant, fast, and user-friendly. A higher quality score can lower your cost-per-click (CPC) and improve ad placement.

In short, your landing page isn’t just a conversion tool. It’s a feedback loop that shapes how well your campaigns perform over time.

Audit Point 1: Deliver On Intent And Relevance

The first rule of landing page optimization is simple: Match the message.

If your ad promises “free shipping on running shoes,” your landing page should immediately confirm that offer. If the ad targets “B2B marketing automation tools,” the page should speak directly to that audience and use case.

Message match builds trust. When a visitor clicks an ad and lands on a page that looks, feels, and sounds different, they bounce. Fast.

Here’s how to ensure relevance:

  • Mirror your ad copy. Use the same language, tone, and offer in your headline and subheading. If the ad says, “Save 20% on winter gear,” the landing page headline should reinforce that exact promise.
  • Align visuals with the ad creative. If your ad shows a specific product or service, feature it prominently on the landing page. Consistency across creative and page design reduces cognitive load.
  • Match the user’s stage in the journey. A top-of-funnel awareness ad should lead to educational content, not a hard sell. A retargeting ad for cart abandoners should take them straight to checkout.

The fewer mental leaps a visitor has to make, the more likely they are to convert.

Audit Point 2: Use Your CTAs Effectively

Your call-to-action (CTA) is the most important element on the page. It’s where intent turns into action.

But too many landing pages bury the CTA, use vague language, or overwhelm visitors with multiple competing actions. That creates friction and kills conversions.

Here’s how to get CTAs right:

  • Be specific and action-oriented. “Get Started” is vague. “Start Your Free Trial” or “Download the Guide” tells the visitor exactly what happens next.
  • Apply contrasting colors. You want your CTA button to stand out from the rest of the page. High contrast draws the eye and signals importance.
  • Limit choices. Every additional option on the page reduces the likelihood of conversion. Remove navigation menus, sidebars, and secondary CTAs that distract from your primary goal.
  • Test button copy. Small changes in wording can have a big impact. “Claim Your Discount” might outperform “Shop Now” for a price-sensitive audience.

Your CTA should feel like the natural next step, not a sales pitch.

Example: Zoho CRM’s Landing Page

Zoho CRM’s website is an excellent example of a landing page leveraging these points:

Specific offer: The header “Get started with your 15-day free trial” is highly specific, clarifying the duration and type of offer, addressing the vagueness of a simple “Get Started.”

Visual contrast: The primary CTA button, “GET STARTED,” is a high-contrast, bright red that immediately draws the eye away from the surrounding white and blue elements.

Action-oriented copy: While the button copy is “GET STARTED,” the text immediately below it clarifies the action as a free trial sign-up, maintaining clarity. Furthermore, the page limits distractions, focusing the user on the single action of signing up for the trial.

This approach effectively guides the user toward the intended conversion.

landing page example for Zoho CRM
Screenshot of Zoho CRM, November 2025

Audit Point 3: Use Imagery That Supports Your Message

Visuals aren’t just decoration. They communicate value, build trust, and guide the visitor’s attention.

The right images can make your offer feel tangible and desirable. The wrong ones create confusion or undermine credibility.

Here’s what works:

  • Show the product or outcome. If you’re selling software, show the interface in action. If you’re promoting a service, show the results or benefits your customers experience.
  • Use real people, not stock photos. Authentic imagery builds trust. Generic stock photos do the opposite. If you’re featuring testimonials or case studies, include real customer photos whenever possible.
  • Optimize for mobile. Images should load quickly and display properly on all devices. Slow load times can increase bounce rates and hurt quality scores.
  • Avoid clutter. Every visual element should have a purpose. If an image doesn’t reinforce your message or guide the visitor toward the CTA, remove it.

Strong visuals support your copy. They don’t compete with it.

Example: Superside’s Graphic Design Services

Superside’s landing page demonstrates using a portfolio of images to support the message that they can handle diverse creative needs for clients across different industries:

Show the outcome: Instead of a single generic image, the page prominently features a collage of actual client deliverables (app interfaces, product packaging, social media graphics) for brands like Amazon, Reddit, and Zapier. This directly illustrates the quality and range of the service’s outcome.

Communicate value and trust: By showing recognized brand logos and diverse project types, the imagery instantly builds credibility and reinforces the claim that they can “Scale your in-house creative team with top global talent.”

Avoid clutter (in context): While it’s a collage, the consistent presentation style and the grouping of images in a grid are purposefully designed to communicate a broad portfolio quickly, which directly reinforces the main headline: “Your creative team’s creative team.”

This strategy uses visuals to provide immediate, tangible proof of the service’s capability.

landing page visuals example
Screenshot of Superside, November 2025

Audit Point 4: Clearly Answer: “Why Choose You?”

Your landing page needs to answer one critical question: Why should I choose you over the competition?

This is where you articulate your unique value proposition (UVP). It’s not just about listing features. It’s about showing how your product or service solves a specific problem better than the alternatives.

Here’s how to communicate your UVP effectively:

  • Lead with the benefit, not the feature. “24/7 customer support” is a feature. “Get help anytime, without waiting” is a benefit.
  • Address objections upfront. If price is a concern, highlight flexible payment options. If trust is an issue, showcase security certifications or money-back guarantees.
  • Differentiate yourself. What makes your offer unique? Is it faster, easier, more affordable, or more comprehensive? Make that distinction clear.

Your UVP should be immediately visible, ideally above the fold. If a visitor has to scroll to understand what you’re offering, you’ve already lost some of them.

Audit Point 5: Leverage A Variety Of Social Proof

Social proof reduces risk. It shows visitors that other people (ideally, people like them) have chosen your product or service and been satisfied.

But not all social proof is created equal. The key is to use a mix of formats and place them strategically throughout the page.

Here are the most effective types of social proof to look for when you are doing a landing page audit:

Customer Testimonials

Short, specific quotes from real customers carry more weight than generic praise. Include the customer’s name, title, and company (if B2B) to increase credibility.

Case Studies Or Results

“We increased conversions by 30%” is more compelling than “Great service!” Quantifiable outcomes resonate, especially with data-driven buyers.

Logos Of Recognizable Clients Or Partners 

If well-known brands use your product, feature their logos. Recognition builds instant trust.

Ratings And Reviews

Aggregate ratings (e.g., “4.8/5 stars from 1,200+ customers”) provide quick validation. Link to third-party review sites like G2, Trustpilot, or Capterra for added credibility.

Trust Badges And Certifications

Security seals, industry certifications, and compliance badges (e.g., SOC 2, GDPR) that are visible on landing pages reassure visitors that their data is safe.

Place social proof near your CTA. That’s where hesitation peaks, and reassurance matters most.

Example: Reddit Ads’ Landing Page

The Reddit Ads landing page demonstrates the effective use of logos of recognizable clients or partners to build instant trust and social proof:

Client credibility: At the bottom of the page, a prominent line on the landing page reads, “Trusted businesses across all industries and sizes use Reddit Ads to meet their goals.” This statement is immediately backed up by a scrolling horizontal display of recognizable brand logos, including Mars, GameStop, Capital One, and Maybelline.

Instant trust: For a potential advertiser, seeing global, established brands using the platform reduces the perceived risk of signing up. If major companies trust Reddit Ads with their budget, a new user can be reassured the platform is legitimate and effective.

Strategic placement: The logo section is placed below the main registration form and the tool to explore audience, providing reinforcement just before a user might scroll away or hesitate. It offers a final, compelling piece of proof that supports the core message of reaching a “niche audience.”

This visual list of successful clients serves as powerful validation for the service.

Redit Ads landing page showing social proof
Screenshot of Reddit Ads, November 2025

Audit Point 6: Ensure Strong Technical Performance And Responsive Design

A beautiful landing page means anything if it doesn’t load quickly or breaks on mobile devices.

Technical performance directly impacts conversion rates and campaign quality scores. Google prioritizes fast, mobile-friendly pages, and visitors abandon slow-loading sites within seconds, noting that 53% of visits are likely to be abandoned if pages take longer than three seconds to load.

Here’s what to audit:

  • Page speed. Use tools like Google PageSpeed Insights or GTmetrix to measure load times. Aim for a load time under three seconds. Compress images, minimize code, and leverage browser caching to improve speed.
  • Mobile responsiveness. 41% of all web traffic comes from mobile devices. Your landing page should look and function perfectly on smartphones and tablets. Test across multiple devices and screen sizes.
  • Forms and functionality. If your CTA involves filling out a form, make sure it works. Test every field, button, and error message. Reduce the number of required fields to minimize friction.
  • Browser compatibility. Your page should render correctly in all major browsers (Chrome, Safari, Firefox, Edge). Cross-browser testing tools like BrowserStack can help identify issues.

Technical problems aren’t just annoying. They cost you conversions and damage your campaign performance.

Audit Point 7: Strategically Place Your CTAs

Where you place your CTA matters just as much as what it says.

Most landing pages include a primary CTA above the fold, and that’s a really good start. But high-converting pages use multiple CTAs placed at natural decision points throughout the page.

Here’s a strategic approach:

  • Above the fold. This is your first opportunity to convert visitors who are ready to act immediately. Make it prominent and impossible to miss.
  • After explaining value. Once you’ve outlined your UVP and key benefits, offer another CTA. This targets visitors who need a bit more context before committing.
  • After social proof. Testimonials and case studies reduce hesitation. Follow them with a CTA to capture visitors who’ve just been reassured.
  • At the bottom of the page. For visitors who scroll through all your content, include a final CTA. By this point, they’ve consumed everything you’ve shared and are ready to decide.

Each CTA should feel contextual, not pushy. It should align with where the visitor is in their journey down the page.

Conclusion: Making Your Landing Page Audit A Habit

Your landing page isn’t just a conversion tool. It’s a data generator.

Every click, scroll, and form submission sends signals back to your ad platform. These signals teach the algorithm which audiences convert, which creatives work, and how to allocate budget more efficiently.

When your landing page converts well, those signals are strong and accurate. The algorithm learns faster and optimizes better. When your landing page underperforms, the data becomes noisy. The algorithm struggles to find patterns, and your campaigns stagnate.

This is why landing page audits are essential. A small improvement in conversion rate doesn’t just boost revenue. It improves the quality of data feeding back into your campaigns, creating a compounding effect over time.

Start by identifying your lowest-performing landing pages. Run A/B tests on headlines, CTAs, and imagery. Measure the impact not just on conversions, but on downstream metrics like CPA, return on ad spend (ROAS), and customer lifetime value (LTV).

The better your landing pages perform, the smarter your campaigns become.

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Featured Image: one photo/Shutterstock

Paid Ad Scheduling Across Time Zones That Actually Works via @sejournal, @brookeosmundson

Scheduling ads in Google or Microsoft Ads sounds simple until you realize how many hours you’re wasting showing them at the wrong time.

A campaign that performs well in one market might fall flat in another, not because your targeting or creative is off, but because of when your ads appear.

Managing time zones is one of the easiest ways to improve efficiency and stop unnecessary spend. Yet, many PPC managers still rely on default settings or assume their ad platform will “figure it out.”

In reality, effective ad scheduling requires strategy, testing, and an understanding of how local behavior differs across regions.

This guide breaks down how to identify true peak hours, segment campaigns by region, and use automation tools to make scheduling work in your favor, no matter where your audience is.

Understanding Time Zone Challenges In PPC

When advertising across multiple regions, time zone discrepancies can create challenges that impact ad delivery, engagement, and conversions.

A common pitfall is assuming that a single campaign schedule will work universally. In reality, what works in one location might be completely ineffective in another.

For example, if your Google Ads account is set to Eastern Time but your target audience is primarily on the West Coast, your ads might be running during off-hours, leading to suboptimal performance.

International campaigns require even more diligence to consider local business hours and consumer behavior patterns.

Another factor is peak engagement hours. While lunchtime or evening hours may be prime time in one country, those same hours could be completely irrelevant in another.

Understanding these nuances is essential for optimizing your ad scheduling strategy.

Advanced Strategies For Scheduling Ads Across Time Zones

Successfully managing ad scheduling across time zones requires a thoughtful approach that goes beyond the basics.

While many advertisers set simple schedules and hope for the best, the real wins come from leveraging automation, data-driven insights, and strategic segmentation.

Whether you’re running campaigns domestically across U.S. time zones or managing international PPC efforts, applying advanced techniques can help ensure your ads are served at the right time for the right audience.

Segmenting Campaigns By Time Zone For Better Control

If you’re running campaigns across multiple time zones, one of the best ways to stay in control is by creating separate campaigns for different regions.

This lets you adjust ad schedules, budgets, and bidding strategies based on local peak performance times rather than forcing a single schedule to work for every location.

For example, an ecommerce brand serving customers in the U.S. and Europe might run separate campaigns for each region.

The U.S. campaign can focus on morning and evening hours when engagement peaks, while the European campaign targets prime shopping hours in local time zones.

While this approach adds complexity, the benefits far outweigh the extra management effort. Automating adjustments with rules and scripts can help streamline this process, ensuring each campaign is optimized without constant manual oversight.

Leveraging Automated Bidding Over Fixed Schedules

Manual ad scheduling has its place, but automated bid strategies like Target ROAS or Maximize Conversions allow you to optimize bids dynamically rather than setting fixed hours.

These AI-driven approaches adjust bids in real time, ensuring ads appear when conversion probability is highest, regardless of time zone differences.

For instance, if data shows that users in one region convert at a higher rate between 9 a.m. and 11 a.m. but another region performs better in the evening, automated bidding will allocate more budget when it matters most.

Instead of manually adjusting bids every few weeks, let machine learning do the heavy lifting.

Optimizing Scheduling Based On Market-Specific Peak Hours

Different markets have different user behaviors, so it’s crucial to base your scheduling decisions on actual performance data rather than assumptions.

Google Ads’ ad schedule reports and Microsoft Ads’ time-of-day insights can help you identify when users in each region are most active.

For example, if analytics reveal that North American users are most engaged in the evening while European users peak in the morning, your campaigns should reflect that.

Instead of blanketing all markets with a generic ad schedule, tailor your approach based on real-time engagement trends.

Using Labels To Manage And Adjust Scheduling

One often overlooked yet powerful feature in Google and Microsoft Ads is the use of labels.

Labels let you group campaigns, ad groups, or keywords into easily manageable categories, making it simpler to track and adjust schedules.

For example:

  • Tagging campaigns by region allows for easy bulk adjustments when shifting schedules due to seasonal changes or promotional events.
  • Labeling time-sensitive ads ensures that you can quickly pause or resume campaigns as needed without sifting through dozens of settings.
  • Using automation scripts with labels enables automatic bid adjustments or scheduling changes based on real-time performance.

By applying labels effectively, you can streamline scheduling changes without manually editing each campaign, saving time and reducing errors.

Automating Scheduling Adjustments With Scripts

If you’re managing multiple time zones, Google Ads scripts can be a game-changer.

Rather than manually adjusting schedules, scripts can dynamically modify bids based on real-time performance data.

For example, a script could be set up to boost bids by 20% during high-converting hours and reduce them by 10% when conversions drop. This keeps campaigns optimized while freeing up time to focus on strategy rather than daily bid adjustments.

Scripts also work well with labels. You can program scripts to modify bid strategies for campaigns tagged with specific labels, ensuring changes are applied only to relevant ads.

Adjusting For Daylight Saving Time Changes

Another scheduling headache is Daylight Saving Time (DST), which varies by country and can cause misalignment in ad schedules.

A campaign that ran perfectly last month might suddenly be off by an hour if a region switches to DST.

To avoid this, maintain a calendar of DST changes in key markets and adjust schedules proactively.

Another option is using automated rules or machine learning-based bid adjustments to handle these shifts without manual intervention.

Budget Allocation Based On Regional Performance Trends

Rather than splitting your budget evenly across all time zones, consider allocating more spend to the highest-performing regions based on historical data.

By analyzing performance reports, you can determine which locations deliver the best ROI and adjust budgets accordingly.

For instance, if your data shows that conversions peak in the late evening for Pacific time zone users but decline in the early morning for Eastern time users, shift more budget toward the stronger-performing time periods.

This approach ensures ad spend is being used effectively rather than wasted on time slots that don’t generate conversions.

Turning Time Zones Into An Advantage

Ad scheduling is just one of many levers that can make or break your campaign performance. When your ads align with local customer behavior, your budget works harder, and engagement improves.

Use data to pinpoint when conversions actually happen, then adjust delivery windows to match those trends.

Lean on automation to keep schedules consistent, especially across multiple markets, and review reports often enough to spot shifting patterns.

Treat time zone planning as part of your optimization routine, not a one-time setup. The more precisely your ads reflect when people are active, the stronger your results will be.

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Featured Image: Roman Samborskyi/Shutterstock

PPC Pulse: PMax Expands, Clarity Now Mandatory & AI Max Data Debate via @sejournal, @brookeosmundson

This week, the paid media world focused less on new tools and more on what’s changing beneath the surface.

Google expanded Performance Max into a new channel and offered long-awaited reporting visibility. Microsoft took a firm stance on brand safety by requiring Clarity across its publisher network. And one viral LinkedIn post questioned the effectiveness of Google’s newest “AI-powered” campaign model.

Each of these stories points to the same theme: Platforms are redefining what control and accountability mean for advertisers.

Performance Max Expands To Waze And Adds Channel Reporting

Google confirmed two changes for Performance Max campaigns.

The first notable update is that for PMax campaigns using “Store Visits” as a campaign goal, your business can now show up on Waze ads inventory. The business will show up as a “Promoted Places in Navigation” pin for users.

This update is for all advertisers in the United States, and no additional setup is required.

The second update is that Google rolled out Channel Reporting for all PMax campaigns. While this has been rolling out for a few months now, not every advertiser had this available.

Why Advertisers Should Pay Attention

Local intent now includes the navigation moment. If your brand depends on foot traffic, showing up while someone is driving near a location adds a fresh, real-world touchpoint.

The channel reporting update matters just as much. It helps shift PMax conversations from “trust the system” to “here’s where the system actually worked.”

In my opinion, this is progress on transparency and reach. It also adds variables you’ll be asked to explain.

The win isn’t “more placements.” The win is being able to connect surfaces to outcomes with fewer leaps of faith.

Microsoft Clarity Now Mandatory For Third-Party Publishers

Microsoft Ads Liaison, Navah Hopkins, shared an important announcement for all 3P publishers on Microsoft:

Screenshot taken by author, November 2025

In her post, she mentions that all Microsoft Ads clicks need to make sure those pages have Microsoft Clarity enabled.

Her post got attention from the PPC industry, where she clarified in the comments that an official announcement from Microsoft will be coming out shortly. All Microsoft Ads partners have already been notified via email.

The post also sparked some questions and potential confusion about how Microsoft Ads wouldn’t be charged if they weren’t running Clarity.

Andy Hawes asked:

Thanks for this Navah Hopkins, but when you say “Any Microsoft Advertising clicks that do not have Clarity will be filtered out and result in nonbillable impressions/clicks.” Are you suggesting that if you don’t run clarity then you’re Microsoft Ads won’t cost anything? I’m assuming that is not the case? So could you explain that part please?

Hopkins clarified during the exchange:

Screenshot taken by author, November 2025

Why Advertisers Should Pay Attention

Microsoft seems to be taking a quality stance, not just making a tracking footnote.

Based on the conversation on LinkedIn, Microsoft is tying billable media to verifiable on-site experience. In theory, that should reduce questionable placements and give brands greater confidence that their ads appear in environments that meet baseline standards.

I see this as Microsoft is trading raw reach for higher trust. Advertisers should expect fewer gray-area placements and stronger conversations with brand-safety teams.

It also nudges the market toward a new normal where “transparency” includes a window into on-site behavior, not just a placement report.

The Industry Reacts To AI Max Performance Data

AI Max was another hot topic on LinkedIn this past week.

Xavier Mantica shared four months of results comparing AI Max to traditional match types.

Screenshot taken by author, November 2025

His data showed AI Max at $100.37 per conversion versus $43.97-$61.65 for most non-AI setups (and $97.67 for phrase close variants). His view: AI Max behaves like broad match with a new label, expanding beyond intended relevance and driving up cost.

As of this writing, the post has 991 engagements with over 170 comments from the PPC industry.

How Advertisers Are Reacting

Looking at the comments, it appears that many PPC pros agree that AI Max isn’t living up to the hype that Google made it out to be when originally announced.

Collin Slatterly, Founder of Taikun, shared his skeptical optimism by not just dismissing AI Max entirely, but shared it may just not be ready for its full potential:

Give it a year, and it’ll probably be ready to deploy. Feels like PMax all over again.

One of the top comments to Xavier’s post came from Mike Ryan, who agreed after analyzing 250 campaigns of his own:

Screenshot taken by author, November 2025

There were others in the comments that had the opposite take of Xavier. Denis Capko replied in the comments, stating:

Screenshot taken by author, November 2025

Why Advertisers Should Pay Attention

This debate goes beyond one account. It reflects a wider tension between volume and control.

“AI increases conversions” is only persuasive if cost, relevance, and repeatability hold up under scrutiny.

While the comments seemed overly negative to AI Max, I see it as AI Max feels more like growing pains than failure.

Automation continues to move faster than the frameworks we use to evaluate it, and advertisers are still learning how to guide it effectively.

When data quality, conversion accuracy, and negative signals are strong, AI Max can deliver meaningful scale. But without clear visibility into how the system interprets intent, results can vary widely.

Posts like Xavier’s highlight the need for transparency as much as performance. Google also benefits from that same openness: It builds trust, helps advertisers use automation more responsibly, and ultimately makes the technology stronger for everyone.

Theme Of The Week: Accountability

The updates and discussions this past week all share one thread: accountability.

Google is expanding where automation can go, Microsoft is tightening the standards for who gets to monetize it, and advertisers are rethinking how much control they’re willing to trade for convenience.

As platforms lean further into automation, the real advantage won’t come from who adopts it first. It will come from who understands it best.

Are you confident in what your automation is doing, or just comfortable letting it run?

Top Stories Of The Week:

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Featured Image: Roman Samborskyi/Shutterstock