Category: Analytics & conversion

Top 4 CRO Strategies for Both Humans and AI Success
Before I dive into updating my Conversion Rate Optimization (CRO) strategies for AI, it’s crucial to focus on the basics first. Clear messaging, robust user experience, and technical precision are still the foundation of successful CRO efforts.

Every marketer wonders how CRO and findability differ between AI systems and humans. Do different strategies cater to AI needs versus human needs, or is there common ground?

As more marketers adopt AI-powered discovery tools, understanding how CRO functions for AI agents compared with humans is crucial. Despite various considerations, the main takeaway is straightforward: effectively serving people also enhances AI findability. Though technical aspects are important, drastically different strategies for AI compared to humans aren’t necessary.

Understanding CRO Beyond the Website

When customers interact with my business directly through AI or agents, my information needs to be clear and actionable. This means having clean, well-structured data that’s easily processed by downstream systems.

With more consumers using AI assistants, it’s important that my products and services seamlessly connect. Standards like Model Context Protocol (MCP) help agents effectively engage with shared information sources.

Sometimes, humans still prefer to interact directly on a brand’s website. In these cases, my content and formatting must consistently enable users to take the actions they want, whether through paid media or organic avenues.

Dig deeper: Are we ready for the agentic web?

Optimization 1: Balancing Text Quantity

In the past, SEO strategies suggested maximizing keywords and text blocks. That’s no longer the case.

Both humans and AI favor well-structured, modular content. People find dense text blocks difficult to scan, which leads to misunderstandings. A clear layout with good spacing and a visual hierarchy helps users quickly grasp their objectives on the page.

There’s no perfect text amount for every situation. I aim to provide just enough content to clearly describe my offering, its benefits, and what makes it unique.

Visual elements, complete with effective alt text, can enhance user experience. Lead generation forms should be simple for humans to use and regularly tested to minimize spam or friction. Difficult content creates hurdles for both humans and automated systems.

Dig deeper: Lead gen PPC: How to optimize for conversions and drive results

Optimization 2: Clear Communication With Humans

The best way to communicate effectively with systems is to communicate well with people. I focus on showcasing my expertise without using excessive jargon. Descriptions should be precise, honest, and reflect the brand.

A simple test: If a 10-year-old can’t roughly understand what I offer, why it’s valuable, or how to engage, my messaging is overly complex. Even with sophisticated AI systems, clarity remains key to achieving human-focused outcomes.

If clarity is an issue, I might ask an AI assistant to critique my position statements. The goal is to simplify and clarify without adding embellishments or unfounded claims.

Visual aids like comparison tables can be useful if they genuinely clarify information. They can be detrimental if used as mere design gimmicks. Accessibility is paramount: adequate color contrast, readable fonts, and moderate font choices are necessary for everyone to access my site.

Images should be easily understood and relevant to their accompanying text, with alt text supporting users with assistive technologies and reinforcing content relations.

Optimization 3: Effective Calls to Action

People visit my site for a purpose, whether it’s shopping, requesting a quote, or contacting my team. They need to know what action to take.

When the intended action lacks clarity, it confuses both users and automated systems.

Good shopping experiences align with shopping intentions, as assistants aim to fulfill tasks they’re set to do. If checkout processes are unclear, it obstructs human businesses with me and AI might fail to understand my site’s transactional nature.

Lead generation also demands transparency. Include clickable phone numbers for calls, submit forms to lead systems, or initiate email clients. Avoid frustrating users with complex, multi-page forms.

Dig deeper: 6 SEO tests to help improve traffic, engagement, and conversions

Optimization 4: Essential Technical Fixes

Technical adjustments come last for a reason: the primary goal is to support my audience. Technical tweaks can help but aren’t game-changers on their own.

Excessive imagery, low text-to-background contrast, or unstable layouts can create usability issues.

Ensuring consistent and meaningful rendering is important for my site. Large layout shifts that occur after page load, measured as cumulative layout shift (CLS), frustrate users. Pages flooded with ads or pop-ups detract from their primary purpose, raising trust concerns.

Security is non-negotiable. Malware warnings, display issues, and incomplete page loads worry both users and automated systems.

Using tools like IndexNow helps alert search engines about content updates faster. Microsoft Clarity is free and provides insights into user site behavior, identifying friction points that might go unnoticed without it. It’s particularly handy for improving chatbot experiences.

What’s more, utilizing ad platforms and auto-generated creative tools, like Performance Max campaigns, can be enlightening. They offer glimpses into how platforms interpret my content. If the output aligns with my intentions, I’m properly serving both humans and systems. If not, it’s a sign to reevaluate clarity and user flow.

Dig deeper: CRO for PPC: Key areas to optimize beyond landing pages

What Does CRO for AI and Humans Look Like?

Whether for humans or AI, certain CRO fundamentals remain essential:
- Information must be clear and truthful.
- User tasks should be easy to complete.
- The site should refrain from manipulative or deceptive design.
- Trust should be reinforced, not undermined, by the experience.
Remember these vital CRO principles:
- Both humans and AI benefit from a clarity-first CRO approach.
- Information should be precise, grounded, and easy to follow.
- Actions need to be straightforward and easy to carry out.
- Technical choices should bolster, rather than detract from, the experience.
Focusing on these principles helps me support both human results and AI-fueled discovery.

Inspired by this post on Search Engine Land.
March 3, 2026
Unlock In-Depth Insights with Asset Hierarchies

I’ve discovered that Asset Hierarchies offer a powerful way to track each of my products, features, and other sub-assets individually. Despite this detailed tracking, everything seamlessly integrates back into the bigger picture of overall brand performance.

This approach allows me to gain granular insights while still maintaining an understanding of my brand’s overall landscape.

Inspired by this post on Try Profound Blog.

March 2, 2026
Discover LLM Traffic Growth and Conversion Secrets
Analyzing LLM referral traffic has opened my eyes to intriguing trends regarding volume, growth, citation shifts, and an impressive 18% conversion rate.

Discussing LLMs and their impact on website traffic has become a staple in my client consultations. I’m often asked about current trends, potential improvements, and established best practices.

For brands eager to navigate these waters, my advice is straightforward: begin with the data you can rely on.

To understand how LLM traffic influences key metrics, I thoroughly analyzed 13 months of LLM prompt referral traffic within Google Analytics from our customer base (Jan. 1, 2025, to Feb. 7, 2026).

We concentrated on traffic from various LLM models to brand sites and the conversion events that align closely with substantial business outcomes, such as purchases or lead generation.

Our analysis unveiled four significant insights:
- LLM referral traffic remains modest.
- LLM traffic is growing rapidly.
- Sources mentioned in responses are evolving.
- LLMs have a high conversion rate compared to other channels.
LLM Referral Traffic is Still Small

Our dataset reveals that LLM referral traffic constitutes less than 2% of total referral traffic. This means that fewer than 2 out of every 100 site visitors come from an LLM source.

The figures vary between 0.15% and 1.5%, with sources like ChatGPT, Perplexity, Gemini, and Claude.

Though a hot topic, it’s not yet the top concern for immediate financial impacts for many businesses.
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In this rapidly evolving space, I believe staying focused, driving innovation, and leveraging data can give brands a strategic advantage over competitors.

Inspired by this post on Search Engine Land.
February 25, 2026
Retire These SEO Metrics to Supercharge Your 2026 Strategy
I’ve realized that many of us, myself included, might be tracking the wrong SEO metrics lately. We need to shake things up, especially with 2026 approaching.

Picture this: I present an impressive chart depicting a 47% increase in site traffic. But instead of excitement, I’m met with puzzled looks from the CMO, wondering why revenue remains stagnant. Or, I celebrate a top-three ranking for a keyword nobody searches for.

The SEO metrics that boosted my confidence back in 2019 might just be steering me wrong in 2026. With AI Overviews taking over search results and zero-click searches becoming the new standard, clinging to outdated metrics might jeopardize my strategy and budget.

I’m ready to take you through the precise metrics that our SEO team should retire and which new, revenue-focused metrics to prioritize instead.

Traffic Metrics

1. Organic Traffic

Organic traffic has been my go-to KPI in SEO reports ever since I started. But relying solely on it doesn’t provide enough context.

Not all traffic is equally valuable. A thousand visitors who bounce instantly are not beneficial. However, a hundred visitors converting at an 8% rate? That’s a success story.

I witnessed a local HVAC company whose traffic dropped by 22%, year on year. Panic, right? Yet, organic revenue increased by 31%. We focused on enriching high-intent service pages, pruning low-intent content. Fewer visitors, but better ones.

Before panicking over traffic drops, I always reassess where traffic is declining. If losses involve informational articles and customer login pages, it’s not a revenue issue. That’s just noise exiting my dashboard.

2. Total Impressions Without Intent Segmentation

This metric can mislead. A million impressions from merely informational queries like “what is SEO” might build some awareness, but they contribute zero revenue. Meanwhile, ten thousand impressions from business-driven queries like “best enterprise SEO agency” could significantly boost my pipeline.

Google Search Console offers this data, but many teams, myself included, often fail to segment it intelligently.

3. Traffic Growth Without Revenue Correlation

This is a risky trap for SEO teams. Bringing a 35% increase in organic traffic to a quarterly review sounds impressive, right until the CFO asks, “And how does this translate to revenue?” If I can’t answer that, I’m just reporting noise.

Ranking Metrics

4. Average Keyword Position

This metric might look compelling in a dashboard, but it doesn’t hold up under scrutiny. If I rank first for a keyword with ten monthly searches and fiftieth for one with 50,000, my average position might seem okay, but I’m losing where it matters most.

The average position treats all keywords as identical when they aren’t. With personalized search results, an “average position” can vary greatly by user and location.

5. Isolated Keyword Tracking

Searchers these days don’t typically use isolated keywords. They pose questions, explore themes, and adjust their queries. Google’s focus has shifted toward semantic search and topic modeling.

Tracking a solitary keyword like “lawyer” is pointless without understanding intent — are searchers interested in criminal defense, divorce services, or merely looking up what lawyers do?

6. Share of Top 10 Rankings

This metric sounds clever until it’s clear that 80% of my top-10 rankings might involve low-intent, low-volume queries. Meanwhile, competitors claim the top-three spots for crucial commercial queries in my niche.

Achieving a No. 1 ranking for a high-converting transactional keyword is more valuable than holding 50 top-10 positions for low-value informational queries.

Authority and Engagement Metrics

7. Domain Authority and Domain Rating

DA and DR might not align with Google’s metrics. They’re proprietary scores from SEO tool companies. Yet, teams often set misguided goals like boosting DA from 42 to 50 by Q3.

It’s possible for a competitor with a DA of 35 to outperform my DA of 65 if their content aligns better with search intent. So, let’s keep these out of executive dashboards.

8. Total Backlink Volume

I’ve seen how backlink volume is often overrated. Google’s algorithm prioritizes link quality, relevance, and context over sheer volume.

A single link from a high-quality, relevant site outweighs hundreds of low-grade directory links. I’ve seen sites with 100,000+ backlinks struggle to rank for meaningful terms because most links lacked quality.

9. Bounce Rate

I’ve found bounce rate misunderstood for years. If someone searches for my company’s business hours, finds them on the contact page, and leaves, that’s a success with a 100% bounce rate.

Google replaced bounce rate with “engagement rate” in GA4 for a reason. Similarly, session duration and pages per session need context. A high pages-per-session score on my pricing page may indicate confusion, not engagement.

Why These SEO Metrics Are Failing Now

I’ve noticed the search landscape shifting quite a bit. Up to 58.5% of U.S. and 59.7% of EU Google searches now conclude without a click, as per SparkToro’s zero-click study. This means, for every 1,000 searches, only 360 result in a visit to a site.

AI technologies are capturing and synthesizing information, bypassing the need for a click. My content can gain visibility and influence without contributing to sessions in Google Analytics.
- Wynter’s latest B2B buyer research indicates nearly 24% of CMOs now utilize AI tools like ChatGPT for research, a significant rise from last year.
Buyers discover brands via AI tools and use Google to validate those discoveries. This alters my SEO focus from merely driving traffic to ensuring my brand is visible during pivotal decision-making stages.

Modern customer journeys can be erratic. Often, users who initially find us through organic search might return through paid ads or direct links. If we use last-click attribution, the true value of SEO is obscured, although this organic start was critical for conversion.

Dig deeper: Measuring zero-click search: Visibility-first SEO for AI results

What to Measure Instead

Revenue and Pipeline Contribution From Organic

For ecommerce, I aim to track revenue from organic sessions by product category and landing pages. For lead-generation, I’ll track how many leads convert to customers. Integrating with a CRM helps in connecting those dots.

No one’s interested in your DA if you can demonstrate $1.2 million in revenue attributed to organic channels.

Conversion-weighted Visibility

I’ll focus on visibility for high-value terms that lead to conversions.

A franchise client noticed they dominated low-intent queries but were invisible for crucial local terms. We adjusted priorities, and their qualified leads doubled in four months.

Topic Cluster Performance

This metric supersedes individual keyword rankings. Monitoring how I rank across full topic clusters, and the aggregate visibility and conversions from these clusters, gives a comprehensive view of topic authority.

SERP Real Estate Ownership

By gauging control over the entirety of search pages, not just listings, including snippets and local packs, I can effectively keep competitors at bay for crucial queries.

AI Platform Visibility and Brand Mentions

My focus will also be on how frequently my brand is mentioned in AI responses. Mentions are becoming as crucial as click-through rates.

For instance, if I secure a favorable recommendation rate across multiple AI platforms for vital topics, it’s a win, even if website traffic appears unchanged.

While tools are emerging to monitor this, manual spot checks can reveal valuable insights, enhancing authority and awareness, eventually leading to brand searches and conversions.

Branded Search and Direct Traffic as AI Visibility Proxies

I notice when buyers find out about my brand through zero-click searches, they often search the brand name directly instead of clicking through. This reflects in my branded and direct traffic rather than organic metrics.

If I see no change in nonbranded organic traffic but an increase in branded search and direct visits, it usually indicates that my content gains attention in AI Overviews.

How to Transition My Reporting

Revamping reporting around new metrics might feel daunting. Stakeholders are comfortable with old metrics.

I start by evaluating my current dashboard, ensuring relevant metrics face business outcomes directly rather than just tallying activities.

Transition by gradually omitting vanity metrics. If organic traffic was my focal KPI, I now introduce it segmented by intent and accompany it with organic-attributed revenue. Gradually, I pivot focus and phase out the dated metrics.

When I introduce new metrics, I frame them in relatable terms. Avoid using “conversion-weighted visibility.” Opt for “visibility metrics for top-converting terms.”

The Metrics That Prove SEO’s Value

The metrics we’ve relied upon — organic traffic, average keyword position, domain authority, bounce rate — aren’t inherently harmful. They’re just incomplete, providing a potentially false sense of security while others prioritize revenue-generating metrics.

Newly adopted metrics — revenue contributions, conversion-oriented visibility, topic authority, SERP dominance, AI platform mentions — directly relate SEO to tangible business outcomes. They prove ROI, justify budgets, and align strategies with business growth.

Consider which metrics in your dashboard lend false impressions of activity over effectiveness. Retire them. Replace them.

Ultimately, no one’s concerned with traffic numbers or DA scores. They want to know if SEO drives growth. Make sure your metrics affirm it.

Inspired by this post on Search Engine Land.
February 19, 2026
AI Search Impact: Revealing Attribution and Buying Decisions

AI search has a subtle impact on trust, sales velocity, and potential client shortlists, which often isn’t reflected in analytics data. These insights came to light through a series of revealing experiments I’ve been involved in.

It was a chance encounter with a new prospect who mentioned, “I actually found you via Grok.” That was a pivotal moment for me. Not only had we not attempted to rank on Grok, but we also weren’t monitoring it. Yet, here it was, influencing potential buyers’ search and evaluation processes.

This realization permeated conversations with other clients; fascination with AI search was rampant, but there was skepticism regarding data credibility. Many wanted visibility on platforms like ChatGPT but hesitated due to unclear attribution.

So, I embarked on structured testing using resources I could control entirely—our agency website, personal experiments, e-commerce ventures, and select domains for testing purposes. The goal wasn’t to attain AI rankings but to decode which elements remain crucial once AI integrates into buying decisions.

These inquiries involved figuring out if AI search altered purchasing preferences or merely the ranking of brands. Additionally, I wanted to understand if revenue metrics could be influenced by AI visibility without hitting the analytics tracking radar and how AI-driven recommendations might affect performance across other channels.

I realized early conversations around AI search revolved around visibility metrics—think brand citations, visibility screenshots from AI tracking platforms, and more. I believed that the primary role of search remains to aid decision-making. My experiments aimed to determine if AI search retained this capability and transformed business outcomes.

Focusing on measurement was crucial. Instead of just relying on API data—which often diverges from user interactions—I observed live interfaces of ChatGPT, Perplexity, Gemini, and Google AI Overviews. Prompt tracking aided in identifying patterns but was not a definitive gauge of success.

During my first experiment, the creation of self-promotional ‘best of’ lists on my own website revealed fascinating insights. Agencies frequently leveraged a tactic where they placed themselves atop ‘best X’ lists, allowing AI systems to inadvertently amplify their prominence.

Inspired by Glen Allsopp’s extensive research, which highlighted how ‘best’ lists were frequently cited by ChatGPT, I tested the findings on my brand webpage. I was intrigued by the rapid visibility of my site, LawrenceHitches.com, across AI platforms for queries like “best SEO agency Sydney.”

However, ranking visibility alone lacked significance. Similarly, when I fabricated a landscaping site to further test self-promotional tactics, it also appeared swiftly in AI responses, reaffirming visibility alone’s limited value.

Through these experiments, it became evident that while AI simplifies appearing on search radars, building and sustaining trust remains pivotal—a sentiment ringing true from the likes of Wil Reynolds. Self-lauding across one’s platform may catalyze skepticism rather than assurance.

I’ve also seen how prompt tracking tools became popular, with demand from clients ever-increasing. Yet, reliability remained a challenge. Surfer SEO research suggested brands often appeared differently in API outputs versus real user sessions. With overlap sometimes as low as 24%, discrepancies remind us that prompt appearances could be misleading.

This is where the narrative eases away from where brands show up and involves questioning efficacy: How did AI influence sales velocity? Did consultations eliminate the need for education? Was buying speedily initiated?

I discovered that signals—where leads factored AI tools into decision-making without prompting—started appearing, shaking traditional attribution’s foundation. A telling instance was Kadi, an e-commerce brand we support, encountering a buyer who, influenced by AI, engaged in a thorough purchasing journey yet showed attribution through Instagram.

For Kadi, digital PR efforts garnered visibility spurt, but gaps in fundamentals meant traditional SEO foundation work was essential to move past quick traction and truly compete. AI played a silent role in buyer decisions, even when attribution data failed to capture its essence.

My journey with StudioHawk provided another layer of understanding. Post a rebranding and digital migration, SEO emerged as a potent channel, complemented by AI leads that became more recurrent.

Sales processes further illustrated the transformation, where AI-affected leads saw reduced education requirements and minimized objections, closing deals notably faster than traditional SEO leads. The blend of ChatGPT, Perplexity, and Grok-influenced conversions stood testament to AI’s influence, even as traditional paths remained evasive in attribution reporting.

Throughout these endeavors, I’ve realized that while AI doesn’t redefine discovery, it compresses consideration significantly. The buyer’s journey is evolving beyond static funnels. AI provides succinct answer summaries, reshaping the ‘messy middle’ where amenities like risk reduction, vendor shortlisting, and trust assurance occur.

It’s evident AI aids decision-making once foundational trust is laid. Traditional SEO confirms search engines recognize your entity, but its real value is now within supporting thoughtful content that pre-sells your services.

So, as I reflect, brands need to realign focuses. Record where AI’s footprints actually land beyond mere appearances. Prioritize intelligibility over creativeness in content. Opt for consistency in entity-driven narratives and prioritize content resonating with comparison and risk evaluations.

Inspired by this post on Search Engine Land.

February 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
Harnessing the Power of First-Touch Analytics for Enhanced SEO
As I navigated through 2025, I kept hearing the same narrative from my SEO peers: organic traffic seemed to be dwindling, clicks were on the decline, and attribution models just didn’t make sense anymore.

The evolution of AI-driven search experiences, with zero-click results and platform-level answers, has further complicated the gap between discovery and actual visits. This has made it even tougher to report accurately on organic performance.

For many, the impact was clear—visible through double-digit declines in organic traffic and leads, year-over-year.

Leaders rightfully asked, “Why are clicks dropping? Why does organic traffic appear 25% lower than last year? Is SEO failing us?”

The truth is, organic search hasn’t ceased to be effective. Instead, our measurement methods haven’t kept up with current discovery patterns.

Why Last-Touch Attribution is Outdated

We haven’t been measuring organic search accurately.

Many organizations still cling to last-touch attribution, only spotlighting the journey’s end rather than its beginning.

Our attribution models, often linear – Search → Click → Convert – fail to capture the intricate user behavior today.

Traditional models assume that discovery leads directly to a measurable click, but AI-driven SERPs are challenging that assumption.

Last-touch attribution focuses on the finish line, ignoring the starting point of the customer journey.

In this AI-first, zero-click landscape, the gaps in attribution widen, particularly for organic search.

Our measurement isn’t entirely broken but outdated. It doesn’t tell the complete story.

We need to rethink our KPIs and redefine success metrics, painting a full picture of the customer journey from beginning to end.

Dig deeper: Marketing attribution guide: Models, tools, & best practices

Problems with Last-Touch Attribution

Last-touch attribution captures only the final stage of the customer journey.

It misses preceding interactions across various platforms like Google, Reddit, YouTube, and AI channels.

Relying solely on last-touch metrics can provide a useful baseline, but it fails to tell the complete story.

With organic traffic down with the rise of AI, understanding first interactions is crucial.

Preparing for First-Touch Attribution

Many organizations still grapple with disorganized, siloed data, often fraught with quality issues.

Reflect on your own data landscape: can you easily pinpoint how customers enter your funnel through organic means?
- Are you attributing conversions correctly? Is AI traffic monitored distinctively?
- Can you discern conversion differences based on the initial touch channel?
Lack of search activity doesn’t necessarily imply ineffective SEO—perhaps your measurements are lacking precision.

The solution? Clean and analyze every traffic-driving channel to truly understand organic search impacts.

Dig deeper: Measuring zero-click search: Visibility-first SEO for AI results

Validating Organic with First-Touch Analytics

Imagine when someone searches, and your brand appears in AI results. That discovery is significant.

If that individual visits your site later via social media or shows up in your store, did SEO not work?

Absolutely, it did! By seeding visibility, organic results funnel potential customers into the journey.

But how can we accurately measure when the conversion wasn’t a direct click?

Understanding both first-touch and last-touch is crucial for a complete view of the customer journey.

Organic searches lay the groundwork for credibility before any digital engagement occurs.

Dig deeper: 7 must-know marketing attribution definitions to avoid getting gamed

Visibility: The Key SEO Term for 2026

The new measure of SEO success in 2026 isn’t just about clicks. It’s about visibility and mentions.

AI’s choice to cite your brand makes organic visibility the first step to becoming top of mind.

Today’s “organic” is about self-discovery by users across diverse platforms, not just Google.

With AI, users can get information without visiting company websites, making brand visibility essential.

As marketers, it’s vital to redefine visibility and strategize its expansion effectively.

Dig deeper: How to build search visibility before demand exists

Time to Expand SEO Strategies

The fragmented, AI-driven world calls for elevating SEO’s role in early discovery, not diminishing it.

Traditional post-click metrics fall short, unable to capture where true influence begins.

Last-touch metrics often undervalue the critical early stages, particularly in AI contexts.

First-touch analysis aids in linking organic visibility to final outcomes and business success.

Despite the challenges, collaborative efforts across analytics and SEO can bridge these gaps.

Adapting our approach to measuring SEO will ensure its growth and continued investment, even as traditional metrics shift.

Dig deeper: MTA vs. MMM: Which marketing attribution model is right for you?

Inspired by this post on Search Engine Land.
January 26, 2026
Embrace the Future: Positionless Marketing Transforms Strategy

In these unpredictable times, I’ve realized something important: it’s not talent but structure that often hinders our marketing performance. Positionless Marketing helps us overcome these constraints.
Five days can now be condensed into five minutes, and six weeks into just six days. This isn’t about small improvements; it’s about enabling our marketing teams to move at the speed of customer behavior by dismantling outdated structural barriers.
I’ve discovered that Peter Drucker’s insights, particularly from his work “Managing in Turbulent Times,” resonate deeply with this idea. He cautioned against using “yesterday’s logic” in our ever-changing world, and I see this happening in marketing all too often.
Markets are continuously shifting, and customer behaviors are changing in real time. Yet, many marketing groups are still stuck in old structures meant for slower eras, leading to missed opportunities.
Understanding Structure: The Barrier to Performance
Drucker emphasized that an organization’s structure is more impactful than individual talent. Even the smartest individuals will underperform if trapped in the wrong system.
Consider a global gaming operator I worked with. They required seven teams and six weeks just to launch a single campaign. The Global Head of Customer Marketing noted, “We needed seven teams and six weeks to send a single campaign.”
This wasn’t a problem of skills. The issue lay in fragmentation: insights were with analysts, creatives with designers, and execution depended on engineers. This led to delays and lost opportunities. Drucker saw this and advocated for breaking down barriers to give knowledge workers clarity and freedom.
From Knowledge to Real-Time Execution
In a leading U.S. iGaming firm, campaign execution once took five days. But in our real-time world, where customer actions shift instantly, five days is far too long. By streamlining processes to reduce handoffs, we cut execution time to just five minutes.
This aligns with Drucker’s belief in empowering those closest to the action to make decisions swiftly and effectively. Positionless Marketing allows us to move from insight directly to action, faster than ever before.
The results speak for themselves—better-targeted spending on the right customers and decisively enhanced outcomes.
Shifting from Task Focus to Outcome-Driven Marketing
Drucker’s concept of “management by objectives” introduced an outcome-focused mindset. Unfortunately, marketing had drifted back into task-focused operations over time. With Positionless Marketing, a global gaming operator transformed its campaign process from six weeks to mere hours.
This change ensured accountability. Previously shared responsibility made no one truly accountable. Now, a single marketer manages the entire campaign, ensuring precision and ownership, driving not just tasks but tangible responses.
Real-World Transformation: Speed Meets Effectiveness
Across industries, adopting Positionless Marketing principles yields incredible results: execution cycles plummet from days to minutes, and planning shrinks from weeks to hours, all while enhancing personalization and relevance.
These aren’t just tech advancements; they stem from restructuring processes. We’ve transitioned from dependency on hierarchical systems to empowered, outcome-focused teams.
Technology Enhance Judgment, Not Replace It
Drucker believed in technology as a means to enhance human decision-making, not replace judgment. Successful Positionless Marketing exemplifies this: AI aids prediction while automation removes friction, yet decisions remain human-centric.
With comprehensive access to data and tools, marketers act promptly without waiting on cross-functional approvals, making Positionless Marketing a vehicle for immediate, improved decision-making.
The Evolution Drucker Advocated For
While Drucker envisioned nimble, autonomous organizations, he could not foresee today’s always-on customer engagement. In this reality, execution lag wastes potential, and structure without flexibility is risky.
Positionless Marketing embodies Drucker’s philosophy, offering immediate information access and authority to act, transitioning from assembly-line operations to self-reliant marketing processes.
From Thought to Action
What Drucker defined for effective knowledge work, Positionless Marketing puts into fast-paced practice. It transforms waiting into swift action, cumbersome handoffs into clear ownership, and process-centric work into real-time relevance. The pivotal question is: will marketing teams evolve before their competitors do?
Today’s knowledge worker isn’t merely informed—they’re finally empowered to act decisively, embodying Positionless freedom.
To explore more on this topic, dive into this case study and this example.

Inspired by this post on Search Engine Land.

January 19, 2026
Understanding the Shakeout Effect: Key to CLV Insights
I’ve come to realize that misinterpreting churn can lead to flawed assumptions about customer lifetime value (CLV). By analyzing retention over time, I can better identify which customers truly drive profit.

In my experience, CLV is often viewed as a static metric, but in reality, it is shaped by how different customer types behave and churn over time. One critical dynamic to understand is the “shakeout effect.”

The shakeout effect is when early churn filters out lower-value customers from a cohort, leaving a smaller, more stable group with higher engagement and predictable purchasing behavior.

In this article, I’ll delve into the shakeout effect in CLV analytics, explore why it occurs, and discuss how marketers should consider it when evaluating churn, retention, and long-term profitability.

What is the shakeout effect in CLV analytics?

Imagine I have a new group of customers. Over time, the “bad” customers—those likely to drop—leave, while the “good” ones remain. These customers have lower drop rates, better engagement, and more predictable purchasing patterns.

This decreases overall churn propensity over time, known as the shakeout effect, and results from heterogeneity among customers.

Typically, analysts use one-year windows or the entire purchase history; the timeframe can vary.

For businesses with monthly subscriptions, analyzing the window after the first 30 days is crucial. No purchases after this period often indicate churn.

When assessing overall churn probability over time, I look for trends like the one in this example.

Breaking out retention rates across dimensions like UTM medium reveals heterogeneity. For example, email as a first touch shows higher retention, around 27% after 500 days, compared to Google’s 18%.

Dig deeper: How to use CRM data to inform and grow your PPC campaigns

Why should the shakeout effect matter to marketers?

In my view, not all customers are equal in terms of CLV. Many businesses lose money on new customers who churn before achieving a CLV sufficient to cover acquisition costs.

Profitability is typically concentrated in a small segment of loyal customers.

If I ignore the shakeout effect and don’t analyze churn adequately, I risk overestimating long-term churn or CLV by misjudging early losses.

A strategic view incorporates the Lorenz curve and the Pareto principle—often, 80% of CLV comes from 20% of customers.

Identifying this loyal core, understanding their demographics and preferences, can generate insights to engage similar potential customers.

How to identify heterogeneity in your CRM

I’ve found that ranked cross-correlation analysis (RCC) is an effective way to explore CRM data and understand CLV drivers.

Initially, I check if features in the data exhibit significant variance in CLV.

For instance, customers with above-average CLV often show frequent purchases, subscribe to newsletters, and make recent or initial product-related purchases.

Further, I find visualizing CLV distribution by dimensions like purchase frequency and geo provides valuable insights.

For B2B, I consider job title, vertical, and account types in my analysis.

Advanced statistical methods, while beyond this discussion, can further refine these insights.

Dig deeper: LTV:CAC explained: Why you shouldn’t rely on this KPI

CLV takeaways from the shakeout effect

To sum up, as a marketer, I should:
- Account for the shakeout effect to accurately estimate CLV.
- Use descriptive and predictive analytics to understand CLV influences.
- Investigate core loyal segments to find similar future customers.
Inspired by this post on Search Engine Land.
January 15, 2026