Tag: First-Party Data

Why My Original Data Gets Cited Only as Benchmarks

In Part 1, I looked at the third-party citation signals that matter so much for AI visibility. In Part 2, I made the case for publishing original data, because it is the strongest single predictor of page originality, and the barrier to earning visibility and authority through this approach is still surprisingly low.

Now I have more evidence for why proprietary data should be part of content creation.

Publishing a number matters, but the number itself is not always what gets cited. I looked at Gauge’s citation data to understand what AI systems actually reward when brands publish first-party data. The answer is narrower, sharper, and more useful than simply saying, “original data wins.” Original data does win, but only when it is packaged in the right way.

The format AI rewards most is the benchmark that answers a clear commercial question: which option is best?

First-party research is scarce and punches above its weight

I worked from Gauge’s cited-URL set: 301 live pages cited by AI systems across 316 unique prompts and 7 verticals. Together, those pages carried 1,075 citations.

After auditing the URLs, I found that only 8 of the 301 pages qualified as primary research. To count, the page had to include the original source of the data and its methodology, rather than simply writing about someone else’s numbers.

That means primary research made up just 2.7% of the cited set. But those same 8 pages earned 90 of the 1,075 citations, or 8.4% of the total citation volume. In other words, first-party research appeared rarely, but when it appeared, it over-indexed by roughly 3x on citation share.

The cleaner way I see this is citation density.

Primary research averaged 11.3 citations per page. Everything else averaged 3.4 citations per page. A primary-research page was 3.3x as citation-dense as a non-primary page.

Primary research is rare, but this Gauge analysis shows it punches above its weight: cited pages with original research averaged 3.3x more citations than everything else.

That is the compounding effect of primary research.

This mirrors the information gain finding I discussed in Part 2, but from the AI citation side rather than the classic 10 blue links side.

There, original data correlated with page originality more strongly than any other trait. Here, original data correlates with citation density. Both findings point in the same direction: the number only you can produce is the lever.

Original research wins when the question has a benchmark

This is where the “original data wins” idea needs more precision.

The 90 primary-research citations were not distributed evenly across the 8 pages. They were not distributed evenly across topics either.

Of those 90 citations, 75 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone earned 44 citations, which was just under half of every primary-research citation in the set.

Once I strip out the benchmark cluster, first-party research barely registers in the citation set. The win is not simply, “I published original data.”

The real win is, “I published a benchmark that answers a buying comparison,” and almost nobody builds those well. By benchmark, I mean a page that measures a set of named things against each other on a specific yardstick and publishes the results as numbers.

A striking citation split: cloud data warehouse benchmarks dominated AI-cited primary research, with Fivetran’s benchmark alone pulling 44 citations from the 90-citation set.

Original research is most powerful when it directly answers commercial comparison queries.

This is also what Google is pushing toward with non-commodity content: new, helpful information that is hard to get elsewhere.

The primary-research citations clustered where prompts asked AI to compare options on measurable specs such as speed, cost, latency, yield, or performance.

That explains the warehouse benchmark spike. The “HR Tech / Compensation” label was noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.

Crypto / Solana showed the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need firsthand ecosystem data, not generic explainers.

The pattern disappeared in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning the data, I found no cited primary-research page in those topics.

A closer look at the content that held 44 citations

Fivetran’s warehouse benchmark took 44 citations from this dataset on its own. Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations. So I wanted to understand why.

The page was published in 2022, but when I examine it, it is easy to see why LLMs still prefer it.

Primary-source visibility is highly concentrated: benchmark-driven topics like HR tech and crypto attract far more AI citations than explainers or listicles.

It answers a measurable comparison head-on. The page names BigQuery, Redshift, Snowflake, and Databricks, then ranks them on speed and cost. It is entity-rich and willing to name the major players directly.

It runs on real first-party data. Fivetran tested against actual customer usage rather than relying on synthetic assumptions, and the page calls that choice out clearly.

It shows the method step by step. The page walks through what data was queried, which queries were used, and how each warehouse was configured and tuned. A reader, or a model, can see how the numbers were produced.

The structure is easy to lift. Descriptive headings such as “Results,” “How much did performance improve?,” and “Why are our results different from previous benchmarks?” help AI map a question to the exact passage that answers it.

It links to raw data and sources. The page footnotes references, including the C-Store paper, and points to the underlying data. That makes the claims verifiable. Few brands put that much work into a data-backed content piece, and even fewer share the full dataset for transparency.

It shows its limits. Dated correction notes from December 2022, named qualitative limitations, and an honest “performance floor” caveat make the claims more credible, not less. The corrections also show care.

The URL never moved. A page from 2022 is still earning citations in 2026 because it stayed live at one canonical address.

The data behind a page like this is easier to pull and analyze than it has ever been. The hard part is everything around the data: the clean method, linked sources, corrections, navigable structure, and willingness to say what the numbers do not prove. That is the craft, and that is the moat.

Fivetran's 2022 benchmark page shows why clear, comparison-led research can become a lasting citation source for AI and search visibility.

This kind of first-party data content is not a thin press release with a few loosely pulled numbers. It requires real work, and it can hold authority for years. My takeaway is simple: AI does not reward “original data” by default. It rewards first-party research when the page gives a clear answer to a measurable comparison and signals depth, expertise, and trust.

The opportunity is to publish a retrievable dataset for a buyer question where AI does not yet have a clean benchmark source. That connects directly to the unanswered-questions finding from Part 2. The opening exists, but in many verticals, nobody has walked through it with a real dataset.

Original data needs a citation-ready package

Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the user’s question.

That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists, but the citation never happens.

The pages that won in this dataset had both ingredients: original numbers and a clean citation shape. They had stable URLs, clear methods, named comparisons, and results that answered buyer questions directly.

Who wins: brands with proprietary product, usage, or pricing data that package it into a comparison a buyer can act on, especially one that can inform LLM-generated recommendations.

Who loses: brands that publish original numbers inside dense narratives, on slow or unstable pages, with no clear comparison frame for AI to retrieve and reuse.

When I think about a citation-ready research page, I look for four parts.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

Lead with the comparison result. The headline finding, such as “X is fastest” or “Y is cheapest at scale,” should appear in the first 30% of the page. Lead with the result, then explain the method and nuance.

Box the methodology. Show the sample, time window, what was measured, and how the measurement happened. Attribution confidence is part of what makes a number citable, so the method needs to be clear on the page.

Frame the comparison explicitly. AI reaches for benchmarks when prompts ask “which is best.” A table comparing named options on named specs is the format most likely to be lifted.

Keep the URL stable. Use one canonical page and keep it live. Do not migrate it or rename it every redesign. The citation earned this quarter only compounds if the page is still there next quarter. In this dataset, 64 of 365 cited URLs were dead, redirected, or otherwise broken, taking 203 citations down with them.

This is the work behind a citable benchmark, and it is more involved than it looks.

HockeyStack documented its own version in a playbook on launching research reports. The company published 18 original reports built entirely on anonymized first-party customer data, the kind of data no competitor could replicate.

Its process includes the same steps the Fivetran page demonstrates: list the data points needed, have a teammate pull them with SQL, define and document the method so the numbers can withstand scrutiny, and structure the report around a real ICP question. HockeyStack calls methodology non-negotiable because without it, someone will always dispute the data.

With AI analysis, pulling the data is often the easier part now. Building the content into something citable, trustworthy, and durable enough to keep earning visibility for commercial queries years later is where the harder work sits.

What sites are already trusted for your topic? When a benchmark you did not publish is earning the citations in your category, the Citation Source Mapper can map that trusted set into a ranked, pitchable target list. It is available in the premium library.

This post first appeared on the author’s website and is republished here with permission.

Inspired by this post on Search Engine Land.

July 8, 2026
How I Make My Marketing Stack Work Harder With AI

I see performance marketing under more pressure than it has faced in a decade. Budgets are flat or shrinking, expectations keep rising, and AI is quickly raising the standard for what “good” performance actually looks like.

For years, I watched performance marketing rely on a familiar playbook. When performance plateaued, teams added another vendor. When targeting weakened, they bought another dataset. When activation became difficult, they layered on more technology. But as budgets tighten and the demand for immediate ROI grows, constantly expanding the stack is no longer sustainable.

The challenge I see for enterprise marketers is not that they lack data. It is that they struggle to operationalize the data they already have.

At the same time, AI is revealing a hard truth about modern marketing architecture. Most AI failures are not really model failures. They are data failures. Even the most advanced agent, model, or automation workflow cannot make up for fragmented customer profiles, disconnected activation systems, or stale audience definitions. Yet much of the customer data platform conversation still centers on launching more AI agents.

I think that misses the point.

The real question is not whether a platform has an AI agent. It is whether my data foundation can support the leap from automating tasks to partnering on strategic outcomes.

For too long, the industry treated self-service as the north star. The goal was to help marketers avoid engineering tickets and data science queues. That made sense for the last decade, but it also turned marketers into manual operators of complex systems. The new bar is not just self-service. It is self-directed performance at scale.

I see a fundamental shift in the marketer’s job-to-be-done. We are moving away from the operational burden of building and managing audiences and toward the strategic work of setting outcomes. Instead of spending the day wrangling segments, I can define the goal, whether that is maximizing customer lifetime value or reducing churn, and let the system suggest the best audience definitions and activation paths. When intelligent agents are connected to a clean data foundation, I move from managing technology to orchestrating outcomes. That is the new blueprint for performance.

At mParticle, we describe this approach as a performance engine: a model where the data foundation and activation layer work as one connected system. The goal is not simply to collect customer data. It is to make that data immediately useful for performance outcomes.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

Audience Agent is one example of that idea in action. I can describe what I want in plain language, such as high-value customers who have not repurchased in 60 days, and the agent proposes the underlying logic for me to review and approve.

For me, the shift is not about handing everything over to automation. It is about working in a marketer-led workflow with an expert collaborator beside me. The longer I work with it, the better it understands the business, the data, the customers, and the patterns that actually move performance. That understanding is only as strong as the data foundation behind it, and ours was built for this long before AI made the need obvious. The marketer leads. The agent elevates and expands the work. Together, they push what is possible.

That same philosophy shows up in capabilities such as Audience Expansion and Household Reach. Audience Expansion helps me identify additional high-potential users directly from first-party datasets, without depending on third-party lookalike audiences or outside data sources. It gives teams more precise control over the balance between scale and quality.

Household Reach addresses one of digital marketing’s most persistent blind spots: buying decisions rarely happen in isolation. By using first-party customer data and enriching it with trusted third-party signals, Household Reach helps marketers engage the full decision-making unit, not only the individual who converted first.

The key distinction is simple. I only need to bring my first-party data. The householding solution handles the rest, helping me reach more of the household without spending extra resources building additional audiences or manually configuring campaigns.

What connects these approaches is a different mindset. Better performance should not require more vendors, more engineering resources, or more external data. It should come from extracting more value from the customer relationships brands already understand.

In this era of intense performance pressure, I believe the advantage will go to marketers who stop looking for more vendors to solve every problem. Success will not come from adding more tools to the stack. It will come from using a stronger data foundation to meet rising expectations and activate more of the data we already own.

Inspired by this post on Search Engine Land.

July 8, 2026
ChatGPT Ads Audience Lists: What Marketers Need to Know

I am seeing OpenAI roll out the ability to upload audience lists inside ChatGPT Ads. The new option appears under the “Tools” section and is labeled “Audiences.”

My read is that this gives advertisers a way to target campaigns based on the audience lists they upload to the platform, which should make ChatGPT Ads more useful for more precise ad targeting.

A new Audiences area appears in ChatGPT Ads Manager, inviting advertisers to upload customer lists for campaign targeting and audience filtering.

More details. I can upload raw or hashed emails and phone numbers and use them as audience filters for campaigns running on ChatGPT Ads.

A ChatGPT Ads audience upload form shows how advertisers can add customer lists, choose identifier type, and submit CSV or TXT files for campaign targeting.

What it looks like. I spotted screenshots of the feature from Craig Graham and Joss Froggatt on LinkedIn. Here is what the Audiences option looks like in the platform:

A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

Why I care. I see this as another sign that OpenAI is continuing to build more customization and targeting controls into its new ChatGPT Ads platform.

For advertisers and marketers, audience uploads could make the platform more practical and more performance-focused. If the targeting works well, it may help improve conversions, strengthen ROI, and make ChatGPT Ads a more serious option in paid media plans.

Inspired by this post on Search Engine Land.

July 7, 2026
How I Turn Proprietary Data Into AI Citations
When I want a page to feel genuinely original, I start with original numbers. They are still one of the most reliable ways to make content stand apart, especially when those numbers come from the business itself instead of a one-off study created just to fill a content calendar.

The old approach was to pay a PR or research firm for a loosely related survey, like a car insurance FinTech commissioning road-trip research to earn a mention in Yahoo. I see that play as increasingly outdated. Almost every product now creates data worth publishing, and extracting that data is easier than it has ever been.

I do not need a full research department to compete here. The bar for standing out is lower than many teams assume.

View embedded content

First-party data: The strongest correlation of originality

On-Page.ai’s recent information gain study scored 150 top-3 Google pages across 50 keywords and 10 verticals. The study looked at how much each page added beyond the rest of its ranking cohort, grading contribution from 0 to 100 by meaning rather than wording.

The median page scored 52. More importantly, original data correlated with that score more strongly than any other page-level trait, including content length.

Pages with at most 1 unique figure averaged an information gain score of 40.2. Pages with 15 or more unique figures averaged 62.1, and the score increased steadily at every step in between.

The good news is that the bar is not especially high. The study found that top organic results usually include only 4 unique data points on average. If I publish a page with more than 4 real original claims, figures, or answers, I create another lever for earning visibility in increasingly competitive organic search.

The analysis also found that almost every search leaves adjacent questions unanswered. On-Page used synthetic reader questions, meaning plausible related questions generated for the study, and found room for new pages to answer those questions more completely. That immediately reminds me of query fan-out.

I saw a similar pattern in an analysis of ChatGPT citations.

“A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity.” – The science of how AI picks its sources

That is why I believe high-intent prompts should be monitored across the full buyer journey. I would map them across the five stages from Reasoning Lift: Problem, Exploration, Comparison, Validation, and Selection. I would also use more accurate AI prompt tracking to understand where those questions emerge, then answer them with the kind of knowledge only the brand can provide.

My main takeaway is simple: most pages are only middling on originality, genuinely original pages are still a minority, and scoring high enough to stand out is achievable without an extraordinary lift.

The limitation is just as important. This study focuses on classic search visibility and rankings, which makes sense because the SEO concept of information gain comes from Google patent language. It does not analyze AI citations or mentions, and it does not appear to include AI Mode or AI Overviews.

Caveat: Being the primary source may not win the citation

This is the part of proprietary data advice I think gets skipped too often. Everyone says to publish original research. Far fewer people test whether AI rewards the brand that created the number or the page that presents it in the clearest, most extractable way.

More data analysis is still coming, but based on analyses completed at Growth Memo over the last year, I already see two patterns worth paying attention to.
- The entity types that predict ChatGPT citations the most are DATE and NUMBER (from The science of what AI actually rewards). Highly cited pages tend to be dense with specific entities, such as a particular methodology, a precise statistic, or a named comparison. Even when another source picks up my proprietary findings and gets cited instead, those external third-party authority signals can still build over time.
- Entity-richness and balanced sentiment matter (from The science of how AI pays attention). Generic advice is vague and risky. Specific entities are grounded and verifiable. Proprietary data can produce, verify, validate, and create entity-rich content at the same time. I can explain why a feature saves a certain percentage of dollars, how many hours clients save, or how performance compares with previous vendors. When I add balanced sentiment to the analysis and explanation, I get a stronger tactic from the same asset.
If the hypothesis holds that first-party data is crucial in the era of AI search, then publishing proprietary data is necessary, but it is not enough. LLM extraction structure, along with the sites AI search engines already trust for a topic, helps decide who actually earns the citation, even when the brand owns the data.

That is the frustrating part: an aggregator can repackage my benchmark into a cleaner, answer-ready page and collect the citation my research earned.
- Who wins: Brands that already have proprietary product, usage, or pricing data and also structure that data for extraction while continuing to build organic brand authority. This connects directly to How to build an AI SEO strategy that outlasts tactics.
- Who loses: Brands publishing opinion content that any tool can replicate, brands ignoring off-site authority, and primary sources that bury their own numbers inside narrative instead of surfacing them clearly.
I do not yet know whether some verticals reward data content more than others. The science series found that citation signals vary sharply by vertical, so I would be surprised by a uniform payoff. Still, I would not claim a pattern without data.

How to structure data for extraction

Owning the data gets me into the visibility race. How I structure that data may decide whether I win the citation.

In an analysis of 18,012 verified ChatGPT citations, we found a ski-ramp distribution: 44.2% of all citations came from the first 30% of a page. The middle 30-70% earned 31.1%, and content buried deep in a long post was roughly 2.5x less likely to be cited.

The follow-up analysis across 7 verticals made the target even clearer. The 10-20% band of a page is where AI reads hardest in every vertical, while the first 10% is usually navigation and intro filler that AI skips. The bottom 10% of any page earns only 2.4-4.4% of citations regardless of vertical.

When I apply that to a data study, the structure becomes straightforward.
- I lead with the headline statistic. My strongest number belongs in the first 30% of the page, ideally right after the title block where the 10-20% band begins. I want the number, the comparison, and the implication visible quickly.
- I define the metric immediately. I include one sentence explaining what the number measures and which population it covers. An undefined statistic is harder to extract with confidence.
- I box the methodology. I make the sample size, time window, and collection method easy to find in a short labeled block. Attribution confidence is part of what makes a number citable.
- I front-load every secondary finding. I rank findings by strength, with the strongest first. A 20-paragraph narrative buildup may help human suspense, but it can cost machine citations.
- I skip the suspense close. AI reads more like a busy editor than a patient student. The payoff-at-the-end structure that worked for ultimate guides often works against extraction.
This post first appeared on the author’s website and is republished here with permission.

Inspired by this post on Search Engine Land.
July 3, 2026
Boost Campaigns with Google’s Automatic Conversion-Based Lists

I recently discovered that Google Ads is elevating the game for advertisers by automatically enabling conversion-based customer lists for those who qualify. This intriguing update aims to start processing data on August 18th.

As part of this change, advertisers who are already utilizing both Enhanced Conversions and Customer Match but have not yet switched on conversion-based customer lists will benefit from this automatic feature enhancement.

Why this matters to us. In a landscape where privacy changes are constantly evolving, advertisers are being gently nudged to lean on first-party data. Conversion-based customer lists offer a fresh approach to build audiences using data collected from existing conversions.

This feature could be a game-changer, allowing advertisers to create highly relevant audience segments and boost campaign performance—and all of this without any extra implementation work.

Here’s the scoop. If you’re eligible, relax; you won’t need to do anything to enjoy the benefits. Starting on August 18th, Google will kick-start data processing and automatically make these lists available within your account.

Advertisers then have the opportunity to decide whether to integrate these audiences into their campaigns and ad groups, molding their targeting strategies accordingly.

But wait, there’s more! If for some reason this auto-feature doesn’t appeal to you, opting out is simple. Just disable conversion-based customer lists in your account settings before August 18th. Otherwise, Google will go ahead with generating these lists automatically.

Heads up. This update was initially discovered by Menachem Ani, the Founder of JXT Group. Menachem shared his insights in communications that he posted on X.

Inspired by this post on Search Engine Land.

June 19, 2026
Unlock Enhanced Ad Performance with Google’s New Conversion Beta
In a significant move, Google Ads has launched a beta feature that allows advertisers like me to connect additional data sources directly to website conversion actions. This innovative step gives us a chance to enhance tag-based measurements using our backend conversion data.

The new feature equips advertisers to merge conversion signals gathered through Google tags with transactional data from various platforms, such as CRMs, order databases, and e-commerce systems.

What’s new. Now, I can append an additional data source to an existing website conversion action via Google Ads Data Manager or through the Data Manager API.

Designed to enhance—not replace—website tagging, this beta allows us to send conversion data from backend systems into the same conversion action utilized for campaign measurement and optimization.

Why we care. This beta is crucial for filling conversion measurement gaps by fusing Google tag data with our first-party data from backend structures like CRMs. It helps us capture conversions that might be overlooked due to browser limits, privacy settings, or ad blockers, providing a fuller view of campaign performance.

Why Google launched it. Google indicates that combining tag-based measurement with backend conversion data allows advertisers to construct a more comprehensive picture of conversions, subsequently boosting campaign performance.

Here’s what this feature helps achieve:
- Recover conversions that may escape website tags.
- Enhance measurement resilience.
- Deliver more exhaustive data for automated bidding.
- Simplify data integration through the Data Manager.
How it works. The system combines website conversion data captured by Google tags with conversion records uploaded from an advertiser’s backend systems.

To avoid duplicate reporting, Google utilizes transaction IDs to identify and de-duplicate conversions between the tag and the supplementary data source within the same conversion action.

What advertisers need to know. The beta is currently restricted to website conversion actions that implement Google tags or Google Tag Manager.

It’s not available for:
- Google Analytics imported conversions.
- URL-based conversion actions.
Google advises attaching an additional data source to an existing conversion action rather than initiating a new one to eschew potential double-counting across campaign goals.

Data requirements. Each upload must encompass:
- Transaction ID.
- Conversion date and time.
Advertisers need to supply at least one attribution identifier, like hashed customer data or a Google click identifier.

Google suggests that I upload conversion data as swiftly as possible and ensure the conversion values match the currency format utilized by website tags.

Bottom line. This beta signifies Google’s ongoing effort to bolster conversion measurement by integrating backend transaction data directly into Google Ads. As we seek more comprehensive performance insights, this feature provides a streamlined means to enhance website measurement using first-party business data.

Inspired by this post on Search Engine Land.
June 18, 2026
Unlocking AI Search and Ads: Insights from Ginny Marvin

After Google Marketing Live, I’m still left with a lot of questions, and I’m sure I’m not the only one. Thankfully, Ginny Marvin, Google Ads Liaison, joined a comprehensive Q&A with Julie Bacchini and the PPC Chat community to tackle big topics like AI Max, AI Search ads, first-party data, and more.

The discussion was enlightening, bringing clarity to AI Search eligibility, reporting challenges, and Google’s increasing focus on data quality.

AI Max: Not a Must-Have for AI Search Ads

A major revelation was that AI Max isn’t required for participating in AI-driven search experiences. This surprised many of us, as we’d assumed AI Max was crucial for tapping into Google’s AI search surfaces.

Ginny highlighted that campaigns with broad match keywords are still eligible for AI Overviews and AI Mode. Even so, AI Max does broaden possibilities by treating phrase and exact match keywords with broad match behavior and enabling keywordless matching.

This means there are still multiple avenues available for us to access AI Search inventory.

AI Search Reporting is Still on Hold

Many of us were eagerly hoping for detailed reporting on AI-powered search results. However, Ginny confirmed that current ads in AI Overviews and AI Mode are reported like other top-of-page ads, with no distinct breakdown. Google’s still figuring out what these reports should eventually look like.

This leaves us with limited insights into how much AI-driven traffic and performance we’re actually seeing.

Google’s AI Brief: A New Layer of Control

A significant part of the discussion circled around AI Brief, set to become the control layer for AI Max campaigns. Advertisers like me will soon be able to provide specific guidance such as “never mention prices” or define target audiences, message themes, and search intents to prioritize.

The rollout will start with English Search campaigns and eventually spread to Performance Max and Shopping campaigns.

For those of us worried about automation reducing our control, AI Brief offers a promising solution.

The Core of Effective Advertising: First-party Data

If there’s anything I walked away with, it’s the emphasis on data quality, particularly first-party data. Google’s focus is what they call “Data Strength,” and tools like Enhanced Conversions and Google Tag Gateway are pivotal.

It’s clear: better data enhances AI performance and outcomes.

Exploring New Metrics: Qualified Future Conversions

Another fascinating development is Qualified Future Conversions (QFC). This metric estimates potential conversions occurring within 180 days post-ad interaction. It’s especially useful if you’re in B2B or lead generation sectors with lengthy sales cycles.

Currently, it’s in testing with select advertisers, and I’m keen to see it roll out further later this year.

Key Areas of Excitement at Google

When asked about her personal highlights from GML, Ginny shared three areas: the new ad formats for AI Search, measurement innovations like QFC, and YouTube Creator Partnerships.

This truly illustrates where Google is investing: AI discovery, advanced measurement, and creator-driven advertising.

Putting It All Together

This Q&A has definitely filled in some gaps left by the GML presentations. I’ve realized that broad match terms still provide a pathway to AI Search, AI-specific reporting is evolving, and Google’s vision continues to be centered on automation, powered by first-party data.

Most importantly, it’s about balancing automation with new controls like AI Brief to shape Google’s AI systems to our advantage.

Inspired by this post on Search Engine Land.

June 10, 2026
Unlock Your Google Ads Potential with Customer Match

Every time I run Google Ads campaigns, one thing I never skip is conversion tracking. It’s essential for measuring success. But here’s a question: why would I ever run ads without uploading my customer list? That’s a key part of gaining an edge in today’s digital landscape.

With third-party cookies fading away and privacy regulations tightening, I’ve noticed how much of the traditional tracking capabilities we relied on are becoming less effective. That’s where my own first-party data comes in, standing strong as the best tool I have to guide Google’s automation processes.

Think about it with me: if everybody has the same access to Google’s Smart Bidding and AI algorithms, relying on the same shared data won’t set me apart. The real advantage is in offering unique data that I alone hold—my customer list.

The $50,000 Threshold Myth for Customer Match

Let’s tackle the primary hurdle first. To leverage Customer Match for direct campaign targeting or exclusions, Google asks for a few things: good account standing, at least 90 days of spending history, and a lifetime spend of US$50,000.

If my account hasn’t reached that point, it doesn’t mean Customer Match is off the table for me. I still upload my customer list into Google Ads right away. Here’s why: even without direct targeting, that list becomes a crucial AI signal. Google Ads then uses it to enhance Smart Bidding and optimized targeting efforts by learning from my customer base’s traits and identifying similar high-converting prospects.

Plus, uploading a list gives me access to Audience Insights in Audience Manager. It’s amazing! I can dig into demographic data to see which Google audience segments my customers belong to—at no cost. This insight sparks new ideas for Demand Gen audience targeting and creative ad strategies, such as adjusting landing pages or ad creatives.

Customer Match Campaign Compatibility

I’ve observed that once my account surpasses the lifetime spend threshold, Customer Match becomes a natural fit for campaigns on Search, Shopping, Gmail, YouTube, and Display. It allows me to seamlessly apply my customer list for targeting or exclusion across various campaign types.

Though Performance Max lacks audience targeting capabilities, my strategy involves excluding data segments, including my customer list. This way, I achieve similar benefits via Customer Lifecycle goals.

Customer Match Unlocks Customer Lifecycle Goals

In my experience, Customer Lifecycle Goals have been invaluable in Search, Shopping, and Performance Max campaigns. It allows me to better prioritize different user segments according to campaign needs.

For instance, with “New Customer Only” mode, the customer list acts as a strict exclusion so I focus solely on acquiring new clients. Meanwhile, the “Customer Retention” mode does the opposite, concentrating only on my customer list to promote repeat purchases. There are other modes too, like New Customer Value and High Value Customers, all made possible through Customer Match.

Now, you may wonder when to prefer this over direct targeting or exclusion. Here’s my 1% Rule for lifecycle goals: if my active customer list doesn’t represent 1% of my target geographical location’s population, using lifecycle goals may not be necessary. For instance, in the US with its 340 million population, I’d need around 3.4 million users for these goals to be impactful, according to my rule.

Conversion-Based Customer Lists: Another Customer Match Feature

When paired with Enhanced Conversions, Customer Match introduces another valuable feature: Conversion-Based Customer Lists. I’ve found that this bridges the gap between isolated conversion actions and ongoing data segment management.

While a conversion may be a momentary action, a data segment is a dynamic list of users—like a customer list or website remarketing list. Conversion-based lists automatically generate a list of users who’ve completed specific conversion actions like purchasing, making this process effortless and continuously updated.

Technical Execution: How to Upload Your Customer List

Securing my customer data in Google Ads is simple once I head to Tools > Data Manager for checking direct integrations. Platforms like Shopify, HubSpot, and Salesforce link directly, keeping my data synced effortlessly. Otherwise, I can always opt for a manual upload via CSV through Tools > Shared Library > Audience Manager.

The key is to keep this data fresh. One mistake I’d often seen is not updating lists, leaving them outdated. For those with regular leads or transactions, a daily update makes sense. In contrast, those with a slower pace might only need bi-weekly or monthly reminders to refresh data.

It’s crucial to remember that user consent is a must for uploading data on Google Ads. Using bought lists from third parties can breach Google’s policy and local privacy laws. My website’s privacy policy must clearly disclose sharing user data with third parties like Google for advertising.

The Exception: Who Shouldn’t Use Customer Match

If I operate within sensitive industries, such as healthcare or finance, unfortunately, Customer Match isn’t an option due to restrictions that prevent data misuse.

However, if my field is less sensitive, Customer Match is invaluable. My proprietary data is one of the most powerful competitive advantages, offering Google’s AI the precise framework it requires to identify my next top customer.

This entry is part of an ongoing series on Search Engine Land, ‘Everything You Need to Know About Google Ads in Under 3 Minutes.’ Through each installment, Jyll introduces a different Google Ads feature, delivering insights to maximize results in just three minutes.

Inspired by this post on Search Engine Land.

June 4, 2026
Master Google Ads: Boost High-Value Customer Acquisition & Retention

I recently dove into Google Ads to explore their new customer acquisition goals. With fresh capabilities like high-value customer bidding and retention targeting, I was curious about how they could boost my marketing efforts.

Many strategies still assume new customers are the most valuable, but this breaks down rapidly. Not every new customer is worthwhile, and ignoring existing ones can be a mistake. The crux is Google’s high-value customer and retention bidding goals.

Google uses predictive bidding to pinpoint high-value customers, but the key is the customer match list I upload. To tweak settings, I venture into the customer lifecycle optimization section under Goals > Summary and select Edit Goal.

Here, I set a higher new customer value to bid aggressively for high-value clients. Google usually suggests a value based on higher LTV, but I ensure it aligns with my strategy before making adjustments.

Once adjusted, Google’s reports reflect the added conversion value alongside the actual sale or lead value. If using cost-per-conversion models, the discrepancy is less impactful. However, it can skew ROAS in a ROAS-based model. Luckily, Google introduced a column to separate true and additional values for clarity.

Dig deeper: Google Ads quietly rolls out a new conversion metric

Building high-value customer audiences means adding an audience list of high-value customers. I think about what makes my customers valuable, whether due to high order values or interest in premium services.

Once I compile and upload the list, I need at least 1,000 active members on YouTube or Search networks to serve effectively. Including additional data like phone numbers and addresses improves my match rates.

If I want a streamlined approach, tools like Klaviyo can integrate audiences directly into my Google Ads account, often yielding high match rates.

With everything set in the customer lifecycle optimization section, it’s time to optimize my campaigns. I can’t apply both bidding goals to the same campaign, so I tailor my targeting and ad copy to different customer types.

For campaigns focusing on high-value new customers, I expand the Customer Acquisition segment and choose a bidding option to target specifically new customers.

It’s critical that my ad content resonates whether I’m aiming for new clientele or re-engaging past customers.

When it comes to re-engaging lapsed customers, I set bidding parameters for retention back under Goals. There, I find lists for lapsed and high-value lapsed customers, if I have the data to support them.

Google suggests values or lists, but accuracy is key before saving adjustments. In Performance Max campaigns, lapsed customers may see a variety of ads, making it essential my messaging speaks to them effectively.

See the complete picture of your search visibility.

Everything hinges on having reliable inputs like quality customer match lists and performance metrics. Used right, lifecycle bidding can prioritize valuable customers and revive lapsed ones, but careless usage just skews data without driving real results.

Inspired by this post on Search Engine Land.

April 23, 2026
Transform Your Marketing Measurement from Basic to Brilliant
I’ve discovered that measurement is truly the cornerstone for all we achieve in performance marketing. Without precise measurement, everything I recommend, implement, and optimize becomes mere speculation. Today, maintaining accurate measurement is more challenging than ever—and it’s only getting more difficult.

With regulatory crackdowns and growing privacy concerns, paired with elongated multi-touch journeys, we face a measurement crisis. Brands that still rely on outdated tactics are missing the mark when it comes to modern measurement challenges.

If your brand falls into this category, it’s time I help you rebuild your measurement foundation—from integrating first-party data (crawl), to creating cross-channel reporting for actionable insights (walk), to advanced media mix modeling (MMM) and incrementality testing for true media lift (run).

The crawl: Building a first-party data foundation

By integrating first-party data into our performance marketing channels, I can move beyond reliance on third-party signals. While those metrics offer surface-level insights, they don’t reveal how channels impact our business goals.

Audience integration

The first step involves integrating CRM data into our paid media platforms. This includes:
- Remarketing to abandoners.
- Creating exclusion lists for current subscribers or recent purchasers.
- Compiling priority contact lists.
I might be uploading lists today, but integration enhances targeting by connecting to up-to-date audience lists for media platform targeting.

Offline-conversion tracking

For lead-gen businesses like ours, setting up offline conversion tracking (OCT) is crucial. It reveals the bottom-line impact of our media on sales, passing sales data back to platforms for campaign attribution.

Once OCT is in place, we can optimize for lower-funnel, higher-quality conversion steps in the sales cycle or even begin optimizing toward revenue to enhance our return on ad spend.

Server-side tracking and consent mode

To progress from crawl to walk, I need to move from client-side to server-side tracking.

By adopting server-side tracking, we bypass browser-based tracking and instead rely on our first-party data. This approach ensures data accuracy and resilience as privacy restrictions increase and cookies become obsolete.
- Partner integration uses pre-built connectors for setup through platforms like Shopify or Google Tag Manager.
- Direct API requires a development team to handle complex data or custom backends.
The walk: Cross-channel reporting integration

With a robust measurement foundation, my next step is breaking down platform silos to understand the full ecosystem.

Going beyond last click

After implementing server-side tracking, I created a clean data pipeline. Yet, traditional attribution models neglect the full-funnel customer journey.

To address this, I recommend using data warehousing solutions like BigQuery to centralize your data and apply custom logic, thereby gaining insights across the ecosystem.

Unified reporting dashboards

Integrating evolved attribution with unified reporting dashboards, like Looker Studio, allows me to visualize data across the funnel and obtain actionable insights into what platforms are truly driving volume and conversions.

The run: Media mix modeling and incrementality testing

With a comprehensive, everyday view of performance, significant questions persist about growth potential and offline performance measurement.

By employing media mix modeling and incrementality testing, I can discern the full impact of media investments at a macro level to make informed decisions.

The holistic view through MMM

I view MMM as my compass, providing a holistic, quantitative guide for paid media investments, helping me analyze the relationship between inputs and business outcomes.

Pulse checks with incrementality testing

Incrementality testing offers validation for MMM and helps evaluate if specific tactics or channels are driving true incremental lift by comparing test and control groups.

The sprint: Clean, integrated, and validated first-party data

With first-party data integrated through server-side tracking and cross-channel reporting, I’ve built a robust measurement foundation. Guided by MMM and validated by incrementality testing, I’m now ready to sprint towards a more informed and successful marketing strategy.

Inspired by this post on Search Engine Land.
April 10, 2026