Tag: Measurement

Boost PPC Performance by Measuring Paid Social Impact
I sometimes find it challenging to measure the true impact of my paid social campaigns on PPC performance. Despite not always seeing conversions directly within the social platform, these ads can significantly influence my overall marketing efforts.

To truly understand how paid social affects my other marketing channels, including PPC, I’ve found a few strategies that help me set up and measure effective tests.

Step 1: Determine Your Hypothesis

I always start by clarifying what I want to learn from my tests. Defining a realistic hypothesis that I can evaluate with available data is crucial.

For example, I often use the following hypothesis to measure the influence of social traffic on PPC:
- Search lift hypothesis: Increasing social media spend will boost brand search volume and PPC CTRs.
- Logic:
  - Social ads build brand awareness, prompting more people to search for my brand during research and purchase stages.
  - As more people become familiar with my brand, they tend to click on PPC ads more, regardless of search terms, enhancing both brand and non-brand CTRs.
  - Exposure to my brand boosts trust, potentially increasing conversion rates.
- Measurement:
  - Track impression and click volume for branded terms.
  - Monitor CTR changes for brand and non-brand terms.
  - Observe conversion rate changes for these terms.
My hypothesis varies, sometimes focusing on the lift from social spend or a surge in direct traffic.

Step 2: The Test

Setting up test parameters is my next step. It’s essential to avoid simply comparing results before and after changes due to possible seasonal effects. A geographic split test is typically my go-to method.

In this test, I increase social spend in specific geographies and analyze PPC data from these areas versus others. While selecting geographies, I control for various factors, such as regional televised sports events or confined TV commercials, to ensure my test results are valid.

It’s crucial to compare control and experimental groups by similar factors like income levels and region types. I also ensure my budget can accommodate anticipated increases in social spent, preventing budget limitations from skewing results.

Evaluating the impression share before and after allows me to ensure budget constraints don’t impact my outcomes.

Step 3: The Measurement

When starting measurement, I keep it simple, comparing platform data to see changes prompted by stopping social spend across all channels like TikTok, LinkedIn, Facebook, etc.

Upon halting social spending, I’ve observed mixed conversion rate results, with some regions showing increases and others decreases, though an overall drop in conversions was common.

Depending on my analytics setup, I delve into more complex analyses, looking at conversion touchpoint differences, visitor overlap rates between social and paid search, or different attribution models.

Before initiating any tests, I ensure that my measurement capabilities are robust enough to understand and interpret results accurately.

Step 4: Evaluation Beyond Test Criteria

While running tests, I measure results against my hypothesis but also look at additional variables that may provide further insight.

In one case, a brand I tested on believed they could cut down on brand advertising without affecting their search volume. However, a drop in common brand terms contradicted this. An evaluation across various factors showed unpredictable results that required expanded analysis.

I rely heavily on my experience to sniff out anomalies and conduct further internal evaluations.

When results seem unexpectedly drastic, I question whether it’s a quirk or if other factors, like recent AI-driven changes, are silently influencing outcomes.

What to Do With Your Social Impact Tests

The test setup is straightforward:
- Define your hypothesis.
- Choose how to test, preferably using a geographic split.
- Ensure you can measure the outcomes appropriately.
- Run the tests and evaluate the hypothesis-related metrics.
- Assess additional metrics for further insights or testing ideas.
For some, social channels like Facebook are top converters, while others see poor outcomes in isolation, necessitating tests to guide budget allocation strategies.

In these scenarios, companies with substantial social media spending reduce to test impact, while others might increase spending to assess performance changes.

Results vary widely across companies, with some seeing significant performance lifts and others noticing minimal changes, underscoring the need for personalized testing.

Conducting geographic split tests can offer incredible insights into how social media campaigns bolster or detract from other marketing channels.

Inspired by this post on Search Engine Land.
April 28, 2026
Google’s New Consent Update: A Simplified Guide for Marketers

I recently discovered that Google is making significant updates to Analytics and Ads consent rules, which are set to take effect this June. This change will prioritize user permission as the key factor in how ads collect and utilize data.

Starting June 15th, the process of data collection in Google Ads will now rely exclusively on the ad_storage consent setting. This alteration removes the previous layer of complexity that came from linked Google Analytics configurations.

Previously, the flow of ad data between Analytics and Ads was governed by both Consent Mode and Google Signals settings within Google Analytics. This often led to confusion among marketers like myself, as many controls were hidden deep within the Analytics settings, rather than clearly visible in consent banners or tag implementations.

Moving forward, Google is streamlining the process. While Google Analytics data collection will still use Google Signals, Google Ads will now focus solely on whether users have consented to ad_storage.

This means that a linked Google Analytics tag will no longer influence Google’s ability to collect or use advertising identifiers.

The new update offers a cleaner, albeit more rigid, consent framework. If ad_storage consent is given, Google Ads can use all available advertising signals, including linking activity to a user’s signed-in Google account when feasible. If denied, Google will only utilize less persistent signals such as URL parameters like gclid.

This change substantially reduces ambiguity—marketers will have a clearer understanding of what drives ads data collection, with fewer options to customize what gets shared.

The primary concern here is that this adjustment makes consent settings more significant for measurement, attribution, and audience targeting. From June, whether Google Ads can leverage identifiers will depend largely on the ad_storage signal, highlighting the importance of correct consent mode setup for optimal campaign performance data.

The update simplifies some of the complexity hidden in linked Google Analytics settings, providing advertisers with more defined rules but less flexibility.

This move by Google underscores a broader strategy to enhance the understanding of consent systems for both advertisers and regulators. Having a single source of truth for ad consent could minimize implementation errors and simplify compliance explanations, but it also demands that brands ensure their Consent Mode is accurately configured.

Should consent updates be delayed or improperly configured, marketers might face gaps in measurement, attribution, and audience targeting.

Marketing teams need to take action before the June deadline by auditing their consent implementation. We should verify that Consent Mode update calls are firing correctly, and that ad_storage settings reflect users’ choices precisely. Brands with Google Signals disabled should be especially vigilant, as they could witness more Ads-linked data under the new setup if users allow ad consent.

The takeaway is clear: streamlined rules are on their way, but getting consent right will be more critical than ever.

Inspired by this post on Search Engine Land.

April 13, 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
Unlocking Revenue: The AI Visibility Advantage

Hey there! I’m excited to share some insights into a groundbreaking partnership between Profound and Partnerize. It’s all about using AI to turn visibility into verified revenue. Trust me, this is a game-changer for any brand eager to scale up their AI investments smartly.
AI Search is evolving at lightning speed, and as brands, we need to do more than just monitor our AI visibility. The key is figuring out how to measure its value effectively. Those who master this will be the ones leading the pack in scaling their spending efficiently.
Partnerize’s powerful payment infrastructure, which already handles billions in partner transactions, gives us a robust platform to ensure these measurements translate into real financial gains. Imagine being able to track and verify revenue directly tied to AI visibility—sounds like a win, right?

Inspired by this post on Try Profound Blog.

March 12, 2026
PPC Challenges: AI’s Limited Impact and Growing Platform Opacity
PPC is becoming an increasingly difficult landscape to navigate, and even though AI provides some help, it doesn’t save the day. Meanwhile, platform transparency continues to decline, leaving us in the dark about budget management.

The latest survey of PPC professionals reveals a challenging environment characterized by less transparent platforms, diminishing effectiveness of traditional measurement methods, and AI tools that have yet to revolutionize our daily routines.

Why I care. As someone deeply invested in PPC, it’s notable that over half of practitioners (53%) believe PPC has become tougher compared to two years ago. The issue isn’t just competition; it’s the increasing number of decisions being made by platforms out of advertisers’ view, which contributes to this growing complexity.

Considering that a whopping 89% of digital ad spend goes to just three companies, those of us who don’t have private measurement tools are essentially navigating without a compass.

By the numbers:
- 1,306 respondents participated in the survey conducted between November and December 2025, representing agency, freelance, and in-house roles.
- 62% identified platform opacity as the main reason for increased PPC complexity, with 53% pointing to the loss of effective measurement tools.
- 5.2 hours/week are saved on average with AI tools, though the majority of us (55%) save only 1–5 hours; almost nobody reports saving over 20 hours.
- 59% are now using LLMs for ad copy, up significantly from 42% the previous year, marking it as the fastest-growing AI use case.
- 73% of in-house teams now manage PPC entirely in-house, a significant increase from 44% two years ago.
- 20% of clients are considering replacing agency work with AI, compared to just 12% planning to switch agencies.
- $1 trillion was spent globally on digital ads in 2025, with 89% directed towards Google, Meta, or Amazon.
What they’re saying. Among PPC features, exact match keywords remain the most reliable, with 75% of us using them frequently. However, AI Max for Search sees minimal adoption, with 34% never having used it, possibly due to it being one of Google’s newest updates. Across the board, auto-apply recommendations are viewed with skepticism.

Between the lines. The underlying theme in the report revolves around agency survival. Many of us (62%) highlight the challenges of finding talent and increasing revenue, with the real threat being clients opting to manage PPC internally using AI.

The big picture. We’ve developed a cautious yet practical approach to incorporating AI — leveraging it for tasks like copywriting and research while being wary of its ability to make autonomous decisions. The more pressing issue that remains unaddressed is that platforms are gaining control and giving us less control over visibility, with no easy solution on the horizon.

Dig deeper. For more insights, check out The State of PPC Global Report 2026.

Inspired by this post on Search Engine Land.
March 11, 2026
Mastering PPC Measurement in a Privacy-First World

I often find myself reflecting on the challenges of PPC measurement in this privacy-driven era. As browser restrictions tighten, our reliance has shifted from perfect tracking to methods like redundancy, modeling, and inference.
Managing PPC accounts has shown me firsthand that something has changed. The signs are everywhere:
Missing GCLIDs, delayed conversions, and reports that are harder to explain have become routine.
Initially, it feels like something broke—perhaps a tracking update or a platform shift. Yet, it’s simpler than that. We often assume identifiers will persist from click to conversion, but that’s no longer a reliable expectation.
Measurement hasn’t ceased to function; what has changed are the conditions it relies on. These changes have been creeping up, gradually becoming the norm.
Why this shift feels so disorienting
Having dealt with this issue for most of my career, I find the evolution quite disorienting. Before native conversion tracking in Google Ads, I crafted my tracking pixels and parameters for affiliate campaigns. Moving towards automation and less control can feel unsettling compared to the traditional methods.
The things I once depended upon for PPC data interpretation don’t apply in the same way anymore. Adjusting my mindset is key to navigating this evolved landscape, as restoring the old assumptions won’t work.
Dig deeper: How to evolve your PPC measurement strategy for a privacy-first future
The old world: click IDs and deterministic matching
Predictability was the hallmark of Google Ads measurement. A click led to a gclid being stored in a cookie and matched back upon conversion, creating an easy-to-explain deterministic system.
This depended heavily on things like parameters passing through browsers and cookies persisting. Thankfully, these conditions were favorable back then.
Why that model breaks more often now
Today’s browsers impose stricter limitations on identifiers. Apple’s Intelligent Tracking Prevention and similar techniques significantly reduce tracking data’s shelf life, directly impacting how data is stored, or even if it can be stored.
On occasions, click IDs fail to reach the site, and the behavior of browsers today necessitates adapting, rather than attempting to cling to outdated deterministic systems.
The adjustment isn’t just technical
On my team, GA4 poses challenges not because it’s ineffective, but because it suits a reality where some data is presumably missing. This experience is shared industry-wide; we must acknowledge that measurement now requires a new mentality.
Ultimately, surviving in this privacy-centric era demands refocusing energy on resolving data problems rather than merely optimizing ad settings.
Dig deeper: Advanced analytics techniques to measure PPC
What still works: Client-side and server-side approaches
The question now is how we can thrive under current constraints, and the answer involves both client-side and server-side measurement practices.
Pixels still matter, but they have limits
Though these pixels provide valuable data and instant feedback, their efficacy is limited by browser constraints and consent banners blocking data.
Relying solely on pixels for measurement affects both our reporting and optimization efforts. While they’re not obsolete, they no longer cover every base.
Changing how pixels are delivered
In search of better solutions, some focus on improving pixel delivery, such as Google Tag Gateway, which routes tags through the same-origin setup. This minimizes failures but does not define better measurement logic by itself.
There’s a distinction between improved infrastructure and improved measurement logic—we must remember that proper data collection and event definition are crucial.
Offline conversion imports: Moving measurement off the browser
Using offline conversion imports moves measurement away from browsers to backend systems, mitigating browser-imposed privacy restrictions and extending its efficacy to longer sales cycles.
Combining offline imports with pixel tracking ensures a complete view of customer interactions.
Dig deeper: Offline conversion tracking: 7 best practices and testing strategies
How Google fills the gaps
Matching when click IDs are missing
Even without click IDs, Google Ads utilizes other inputs to match conversions, although we must be aware that modeled data fills gaps when consent is denied or IDs are missing.
Even with complete information from pixels or offline imports, conversions sometimes remain elusive.
Determining how this aligns with privacy restrictions and user consent requires ongoing refinement and a strategic approach.
Designing for partial data
Partial data is now the status quo, and including multiple sources of input can create a robust strategy to overcome discrepancies in systems like CRMs and Google Ads.
By learning to accept this tension and strategically managing incomplete data, we can optimize campaigns for the current data landscape.
Dig deeper: Auditing and optimizing Google Ads in an age of limited data
Making peace with partial observability
As we embrace a privacy-focused measurement strategy, perfect tracking is no longer feasible. Building useful measurement systems requires recognizing differing operational views and aligning accordingly.
Ultimately, strategic thinking, redundant data systems, and careful evaluation are essential components in adapting to a partially observable data world.
Today’s measurement landscape demands a strategic approach instead of recreating past perfection.
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Inspired by this post on Search Engine Land.

February 10, 2026
Measure PR Success: SEO, PPC, and GEO Strategies Unveiled

As I reflect on the challenges of PR measurement, it becomes clear that many hurdles exist. Limited budgets and siloed teams often make it tough to connect our media efforts with tangible results.

That’s why I’m convinced that collaboration with SEO, PPC, and digital marketing teams is key. Together, we can achieve what feels impossible on our own:

Specifically, by linking media outreach with customer actions, integrating SEO and GEO into our measurement, and choosing the right tools, we can truly measure impact.

This piece offers a practical roadmap for achieving this without needing an enterprise budget or specialized analytics team.

Our digital age of communication isn’t linear. Audiences often engage with content across various channels before taking action, if they do at all. Understanding this loop is essential for measurement.

I’m reminded of how SEO and PPC professionals focus on actions like searches, clicks, and conversions. We in PR should adopt this action-oriented mindset to enhance our measurement strategies.

First, we need to prove the link between media outreach and customer actions. This often requires cross-departmental collaboration to access valuable data currently scattered across different systems.

By incorporating PR touchpoints into analytics tools like Google Analytics 4, I can see our earned media’s influence on downstream behavior, turning PR from a cost center into a demand-creation channel.

Second, while SEO is widely accepted, understanding its measurement in PR is less clear. Traditional metrics like coverage volume or sentiment don’t fully capture SEO’s impact.

GEO presents a new frontier, focusing on whether our content is a source for AI-generated answers. Tools like Profound and Semrush’s AI Visibility Toolkit offer insights into this new layer of measurement.

Lastly, it’s crucial that we select tools based on strategic goals, not just what’s trendy. This involves working backward from the desired audience actions to choose the right measurement tools.

In collaboration, PR, SEO, and PPC teams can integrate their strategies, avoid duplication, and create comprehensive insights that inform and improve future campaigns.

Ultimately, this collaborative approach gives us the edge, allowing us to adapt swiftly to evolving measurement tactics and strengthen our collective impact.

Inspired by this post on Search Engine Land.

February 6, 2026