As I dive into the data we’ve amassed from over 80 SaaS clients between 2022 and 2026, this report paints a vivid picture of freemium model effectiveness. Together, we’ll explore industry averages, see how visitors transition to becoming free users, and how these free users convert to paid customers. I’ll also guide you through the nuances of various freemium offerings compared to free trial success rates.
I’ve always been fascinated by the evolving nature of SEO, especially in an era dominated by artificial intelligence. For over twenty years, SEO heavily relied on keywords. But with the rise of generative AI and conversational tools like ChatGPT, we’re now seeing a shift toward prompts as the backbone of search visibility.
Understanding the prompts my audience uses with large language models is crucial. Otherwise, my content might never see the light of day in search results. Let’s explore how prompts vary by industry and their impact on search visibility.
How Prompts Differ by Vertical
It’s clear to me that the context holds paramount importance in the responses generated by large language models (LLMs). Different industries have specific patterns that dictate how users construct their prompts. I need to tailor my content to these unique frameworks to ensure maximum relevance.
Healthcare: Symptom-driven and Cautious Language
In the healthcare sector, I’ve observed users leveraging AI as an initial triage tool. Instead of a vague term like “chronic fatigue,” detailed prompts narrate specific symptoms.
The prompt pattern: These healthcare prompts are rich in personal context, symptom mapping, and cautious constraints. Questions often revolve around symptom lists and safety considerations linked to age or medication.
Anatomy of a healthcare prompt: Consider a prompt like: “I’m a 45-year-old female experiencing sudden joint pain and a rash after starting [Medication X]. What side effects should I monitor, and when is it critical to seek medical help?”
The content shift: To stand out here, my content cannot simply define medical terms. It must align with a patient’s decision-making process.
The action: I focus on structured FAQs, clear risk factors, and headers addressing specific symptoms combinations to engage effectively.
B2B: Comparison-heavy and ROI-driven
In B2B contexts, I see users turning to AI for detailed comparisons and ROI evaluations, bypassing traditional marketing materials.
The prompt pattern: B2B prompts are analytical, featuring deep dives into financial justifications. Requests often include data for presentation-ready tables or matrices.
Anatomy of a B2B prompt: Typical requests might be like: “Compare CRM ‘Brand A’ and ‘Brand B’ for a 500-user company, with implementation timelines and ROI over three years formatted in a table.”
The content shift: Without transparent, data-rich content, my B2B efforts remain invisible to LLMs.
The action: I need to publish open comparison pages with hard data, ensuring technical details are structured in an easily extractable format for AI systems.
Ecommerce: Intentional Clusters of ‘Best,’ ‘Cheap,’ and ‘Reviews’
The ecommerce landscape, as I see it, is an interactive shopping experience with AI-driven, personalized recommendations.
The prompt pattern: Queries often combine quality markers like “best reviewed” with budget constraints like “under $150” within specific contexts.
Anatomy of an ecommerce prompt: An example might be: “What are the best-reviewed running shoes for overpronators under $150, excluding brands with poor durability?”
The content shift: Beyond simple keyword targeting, I must infuse my content with the semantic depth necessary for LLM validation.
The action: I optimize my merchant feeds with conversational attributes, ensure crawlable user reviews, and connect product specs to consumer value.
Why Prompt Structure Impacts Your Search Visibility
Understanding why prompt structures matter is key for me. They shape whether my site appears in LLM responses, based on how a user constructs their inquiry.
The Power of ‘Reasoning Lift’ and Direct Citations
By optimizing for direct citations and structured data, I could boost the visibility of my content by up to 40%, according to research from Princeton and the Allen Institute for AI.
It’s intriguing how more than 80% of links in AI-driven searches come from domains not ranking in traditional top searches. This emphasizes the importance of content quality and structure over legacy backlinks.
Operationalizing Prompt Research
Shifting my focus from keywords to prompts is crucial. I need to revamp my content strategy to align with conversational search trends, ensuring my brand stays visible.
Stop tracking isolated keywords: Instead, I’ll search for conversational data within search logs and consumer interactions.
Audit for LLM readability: My content must be easily parseable by AI, underpinned by modern standards and structured data.
Write for the follow-up: Rather than focusing solely on initial queries, I’ll anticipate and address follow-up questions within the same content.
To stay ahead, aligning my content with AI interaction patterns is non-negotiable.
When I dive into platform reports, I realize they tell only part of the story. It’s the incrementality, CRM data, and broader measurement insights that truly reveal the impact of our marketing efforts.
I recall a time when PPC attribution was never flawless. Now, with AI widening the gap, it’s even trickier to pinpoint what truly influences a conversion and what ends up receiving credit.
Imagine someone discovering a product on social media, watching a YouTube review, diving into Reddit opinions, using an AI tool to compare options, and then returning through a branded Google search ad days later.
While the PPC report might show a single conversion from branded search, I see a more complex journey that needs recognition beyond the final click.
AI is reshaping brand discovery, how purchases are researched, and how ad platforms decide who sees which ads. As a marketer, I find there’s now less visibility into these platform-driven decisions.
It’s clear to me that relying solely on platform attribution data doesn’t tell the whole story of my business’s truth.
AI is changing where the journey begins
Traditionally, the search journey starts well before an advertiser sees a measurable click. Recently, findings like those from Responsive’s 2025 research indicate that a significant portion of B2B buyers favor generative AI over traditional search when exploring vendor options.
For someone entrenched in the tech sector, I can’t ignore how 80% of tech buyers are now using generative AI at least as much as traditional search.
If AI-derived lists are excluding my brand from their answers, I’m instantly out of the buyer’s consideration set, which is disconcerting.
Google’s announcements about AI advancements reaching billions of users show how rapidly the landscape is evolving. This shift means that brands like mine need a strategy to ensure we’ll still be visible.
I can’t help but notice how Pew Research Center’s findings about declining clicks when AI summaries are present have personal and business implications for me.
I also realize the importance of brand recognition, even if initial interactions don’t result in a direct click-through.
The discovery phase deeply influences the eventual conversion, yet often, only the final touchpoint gets the credit.
Branded search often receives credit for demand generated elsewhere
Observing branded search, I frequently note it’s a classic case where attribution is mistaken for actual impact.
The efficiency portrayed by a branded search campaign can be misleading. Although such campaigns often perform well on metrics, primarily because they target users already familiar with the brand, they don’t always generate the initial demand.
A user might only search my brand due to exposure from other channels, such as social media, YouTube, or even an AI-generated suggestion.
Thus, distinguishing between demand capture and creation is vital. The real test is understanding whether certain conversions would have occurred absent of these campaigns.
AI-driven discovery creates a measurement blind spot
In client data, I’ve observed that direct traffic from AI platforms boasts a higher conversion rate compared to organic search, which piques my curiosity.
With these findings, I’m reminded of how much goes unmeasured. AI introduces complexities that create attribution challenges, as visible AI traffic might be just a small fraction of the journey.
Recognizing this, I understand the importance of viewing these interactions as part of a larger conversion narrative.
Ads are becoming part of AI-generated search journeys
With ads now interwoven in AI results, I face an added layer of complexity in correlating AI search with paid media.
Google’s policy of serving ads based on the commercial intent inferred from AI responses means my ads could surface earlier in the buyer’s research journey—a fact that fascinates me.
Despite these placements, I’m aware of the limited visibility and reporting challenges they present, which is both frustrating and intriguing to navigate.
Platform automation can make attribution look better while making analysis harder
Within account platforms, the allure of automation promises efficiency, yet it can blur analytical clarity.
I reflect on how broader targeting can deliver impressive surface-level results, but the lack of granular insights into why certain ads perform complicates future decisions.
This dilemma emphasizes for me the critical balance between leveraging automation and maintaining rigorous scrutiny.
I see the trap of prioritizing metrics like reach and click-through rate over genuine business outcomes.
The challenges extend to future optimizations and highlight the importance of qualifying lead quality over sheer volume.
Bringing CRM data into PPC reporting brings everything full circle, ensuring the focus isn’t lost in translation between metrics and actual business value.
Assessing upper-funnel activities, I note that they can have sustained, profound impacts on lower-funnel metrics— a sentiment validated by research indicating significant long-term returns on initial media investments.
This insight reassures me of the need to invest in awareness and video campaigns that extend beyond immediate ROAS measurements.
A single ROAS figure no longer suffices. PPC reporting, in my view, must integrate platform attribution with broader business metrics and strategic experiments.
1. Separate demand creation from demand capture
I ensure campaigns are assessed by their unique objectives—demand creation versus demand capture.
2. Review attribution paths, not just final clicks
Using GA4’s paths report, I analyze the customer journey comprehensively to understand how channels influence conversions from start to finish.
3. Import deeper CRM outcomes
For me, importing qualified leads and sales data enriches platform optimization and aids strategic alignment.
4. Monitor the metrics sitting outside the PPC dashboard
I track various metrics—branded searches, AI-referred sessions, and lead quality, which together form a holistic view of the customer journey.
5. Test incrementality rather than assuming
Incrementality testing, such as Google’s Conversion Lift, helps me understand the genuine impact of my ads beyond the dashboard numbers.
6. Add regular human checks to automated accounts
Despite automation, I regularly review and ensure account settings and outcomes align with my overall business objectives.
I’ve learned there isn’t a single PPC attribution model to explain the fragmented, AI-influenced customer journey we see today.
Rather than abandoning attribution, I see the value in treating it as just one piece of the puzzle alongside analytics and CRM outcomes.
The most insightful question isn’t, “Which channel received the conversion credit?” but instead, “What would be different if this activity never happened?”
As an advertiser using Google’s Dynamic Search Ads, I’ve got good news: Google has decided to delay the transition to AI Max by five months. This gives me more time to explore AI-powered options and manage the migration at my own pace.
The extended timeline is a relief, offering me the opportunity to test out new strategies and alternatives without the pressure of a sudden shift. Managing the transition away from one of Google’s most established campaign types on my own terms is crucial for continuity.
What’s changing: Google has pushed the auto-migration of DSA campaigns from September 2026 to February 2027. From June 15, I can even create new DSA campaigns again, offering flexibility during the transition period.
Catch up: For those not in the know, Dynamic Search Ads have been making way for newer, AI-driven campaign formats. These include AI Max for Search, broad match, and Smart Bidding.
With this delay, I have more time to assess how these AI-driven options perform compared to traditional methods. Preparing a solid migration plan before Google’s automatic upgrades can now be done thoughtfully and strategically.
Why we care: Google’s extension is quite significant, offering nearly six extra months to see how AI Max and other AI formats stack up. This valuable period allows me to conduct side-by-side tests, set performance benchmarks, and fine-tune migration strategies.
What advertisers should do: Google is prompting us to audit existing DSA campaigns, run experiments against AI Max for Search, and use tools for voluntary migration before February 2027.
Timeline:
June 2026: DSA campaign creation is back.
June 2026 – January 2027: Extended testing and voluntary migration period.
January 2027: New DSA creation ceases.
February 2027: Automatic migration kicks in for remaining campaigns.
Understanding how memory, search, MCP integrations, and AI skills come together to streamline agency workflows and eliminate context-switching.
If you work in an agency or manage clients, you probably know how quickly your morning can disappear into Gmail, Slack, and CRMs just to recall what mattered yesterday.
In the past, I would juggle decisions like pricing for my team, roadmap calls for our app, Slack threads, and urgent sales follow-ups, all before my first coffee.
Those hectic days are now behind me. About six months ago, I rebuilt my workflow using Claude Code as my second brain, and my Monday morning catch-up now takes just a minute.
Let me share what I built, why it’s been transformative, and how you can do the same.
Why Most Second-Brain Setups Break Down
The concept of a “second brain” isn’t new. Tiago Forte’s “Building a Second Brain,” PARA method, Notion, and Obsidian all capitalize on the same idea: externalizing memory.
Catching information is effective. The recall? Mostly. The real value lies in transforming recalled data into actionable tasks.
Most implementations fail in three ways:
Passive storage. Information enters but doesn’t exit without a manual search and personal memory, especially meeting notes.
Context-switching tax. Finding the right note involves copy-pasting and additional prompting before it becomes useful.
No action layer. Without drafting or executing tasks, it becomes a burden of excess notes, leading to cognitive overload.
The issue isn’t documenting tasks but having those scattered in myriad apps without a unifying layer to read across them.
What truly saves time is a layer that can amalgamate all of this and turn it into action.
General AI assistants can answer queries but aren’t seamless with file systems or past interactions. Claude Code changes this with:
Native file system access: It reads and writes within project folders, accessing local files directly.
Persistent, structured memory: Remembers session data stored in curated Markdown files.
MCP integrations: Directly connects with Gmail, Slack, Google Drive, HubSpot, Scoro, without altering workflows.
An action layer: Drafts documents, analyzes data, and handles repeatable tasks in my workflow.
The most advantageous aspect is moving from mere storage to actionable insights, saving immense time.
The Four Layers of an AI Second Brain
I structured my second brain using four fundamental layers.
1. Memory
Stored in a small collection of Markdown files. They cover my work details, client preferences, decision-making data, and my desired AI persona.
These automatically load, eliminating the need to reintroduce context every session.
Memory self-expands, converting daily logs into long-term memory selectively for accurate client models.
2. Search
Minimizing memory size keeps daily logs indexed in a local database for quick retrieval of past conversations with full context.
3. Skills
Focused capabilities like drafting a brief or proposal, replying in my voice, or summarizing meetings. Small, purposeful, and memory-inherited.
Not an all-encompassing agent, but an adaptable assistant, growing daily with specific skills.
4. A Heartbeat
An hourly process checks emails, calendar, Slack, and pipeline activities, alerting me if intervention is needed with a summarized Slack ping and draft.
I’ve discovered something intriguing about Claude’s reliance on Brave Search rankings. Based on insights shared by Jonathan Clark during a Profound session on Zero Click, it seems that Claude frequently taps into Brave’s search results, particularly when dealing with recency, ranking, or comparison prompts.
Clark, who is the managing partner at Moving Traffic Media, emphasized a key point from the session: Claude doesn’t rearrange search results but instead incorporates Brave’s top 10 search results directly into its answers.
Claude’s web searches are selective. In fact, I learned that Claude uses web search in only 36.6% of cases compared to about 90% for ChatGPT, as per Clark’s observation.
Claude is triggered to search most often by prompts that signal current trends, rankings, location, or comparisons. For example, queries like “best XYZ” caused a search 81% of the time. Ranking focus prompts had a search rate of 67%.
Location prompts initiated searches 55% of the time, while comparison prompts such as “X vs. Y” led to searches 51% of the time.
Brave rankings are crucial. Another interesting point is that Claude’s answers only matched ChatGPT’s citations in 8% of cases for the same queries, according to Clark.
Claude’s results showed a 64% overlap with Google rankings. This indicates that Google-focused SEO strategies might be more effective for Claude than efforts targeted at boosting visibility in ChatGPT.
The analysis also highlights the significance of tracking Brave search rankings. Clark mentioned that Claude relies on Brave, and achieving good rankings in Brave provides us with measurable insights.
Memory in prompts. I found it interesting that prompts like “how does,” “what is,” and “steps to” are less likely to prompt Claude to conduct a web search. Without searching, Claude cannot cite online sources.
According to Clark, Claude searches most often for prompts with keywords like “best,” “top,” or comparative phrases.
The pattern of years in queries. Clark noted that there are consistent patterns that might simplify testing with Claude:
One noticeable trait is Claude’s query fan-outs, which consistently produced the same results 65% of the time across users.
These fan-outs frequently involve years, suggesting that titles featuring the current year might be advantageous in Claude-initiated searches, especially for queries driven by ranking and recency.
Why this matters to us. It appears that Claude’s visibility hinges more on the rankings within the search results it utilizes. Clark suggests Claude might be one of the most amendable AI answer engines due to its consistent search patterns closely tied to measurable rankings.
I’ve recently delved into the world of veterinary SEO agencies and analyzed a whopping 73 companies. With a robust scoring system, I’ve ranked each based on eight criteria to ensure the firms making the list are truly top-notch.
The criteria include average review scores, leadership experience, being founder-led, notable clients, years established, average client tenure, and media references. Extra emphasis was placed on reviews from veterinary clientele, signaling relevance and client satisfaction.
After rigorous analysis, I’ve narrowed it down to the top 6 companies, and here’s the detailed ranking:
The Top Veterinary SEO Companies of 2026
1. First Page Sage: Leading the chart with an impressive blend of local SEO and GEO targeting.
2. Beyond Indigo Pets: Known for their holistic digital marketing strategies tailored for vet clinics.
3. LifeLearn: Offers an integrated platform that blends SEO with practice management.
4. True North Social: Focuses on SEO and social media to engage and convert pet owners.
5. Veterinary Marketing: Ideal for budget-conscious practices, offering essential digital marketing packages.
6. UppercutSEO: Renowned for their technical SEO expertise and local search improvements.
Insights on First Page Sage
Ranked first, First Page Sage utilizes a comprehensive thought-leadership SEO strategy. I found their approach to blend SEO with geo-targeting, engaging qualified veterinary leads. Their techniques help transform veterinary practices into authoritative local resources, driving meaningful traffic poised for conversion.
With AI becoming more prevalent in decision-making, they’ve innovated through generative engine optimization, giving clients a visible edge in AI-generated search results.
Highlights:
Average Review Score: 4.9
Leadership Experience Score: 4.9
Founder Led: Yes
Notable Clients: San Francisco SPCA, Blue Cross Pet Hospital, Lakeview Veterinary Hospital
Year Established: 2009
Average Client Tenure: 3.2 years
Media References: ~820
Approach to SEO: Local SEO and GEO targeting
Beyond Indigo Pets: A Closer Look
Beyond Indigo Pets tailors marketing strategies for veterinary practices, focusing on seasonal needs and competitive dynamics. While their services cover a wide array of digital marketing aspects, they do not specialize solely in SEO, which may be a consideration for practices in hyper-competitive areas.
Approach to SEO: Digital marketing for vet clinics
Exploring LifeLearn
LifeLearn offers a comprehensive suite integrating SEO with practice management, making it an appealing choice for those desiring a one-stop solution. However, if dedicated SEO specialization is your focus, you might explore other firms on this list.
Details:
Average Review Score: 4.6
Leadership Experience Score: 4.4
Founder Led: No
Notable Clients: N/A
Year Established: 1994
Average Client Tenure: 3.0 years
Media References: ~75
Approach to SEO: Integrated platform with SEO
Diving into True North Social
True North Social curates content that strikes an emotional chord with pet owners, transforming them into clients through strategic SEO and advertising. They prioritize intimate client engagement, which might limit their capacity for larger veterinary organizations.
Average Review Score: 4.4
Leadership Experience Score: 4.5
Founder Led: Yes
Notable Clients: N/A
Year Established: 2016
Average Client Tenure: 2.4 years
Media References: ~70
Approach to SEO: SEO, social media marketing, PPC
Understanding Veterinary Marketing
If your practice operates on a tighter budget, Veterinary Marketing offers essential services to get you started with online growth. While their packages are budget-friendly, you might need additional expertise for advanced SEO strategies.
Approach to SEO: Veterinary-specific SEO, PPC, social media
Delving into UppercutSEO
UppercutSEO focuses on technical SEO fundamentals, beneficial for practices needing foundational web optimization. They may not cover veterinary-specific insights that others on this list specialize in, so keep that in mind.
Average Review Score: 4.4
Leadership Experience Score: 4.4
Founder Led: Yes
Notable Clients: N/A
Year Established: 2020
Average Client Tenure: 1.8 years
Media References: ~95
Approach to SEO: Technical SEO and local search
The Best Veterinary SEO Companies by Specialty
Our in-depth analysis also classified top veterinary SEO agencies into three key specialties reflecting unique client needs: content marketing, local search optimization, and technical implementation.
I’ve discovered something exciting about how Google and Walmart are teaming up to enhance our advertising experiences. They’re enabling advertisers to tap into Walmart shoppers through YouTube, using Display & Video 360 (DV360) to measure sales more effectively. It’s a game-changer for those of us who focus on retail success.
This collaboration means I can access valuable shopper data from Walmart while also tracking whether my YouTube ads are translating into sales. It’s a win-win, giving me more control over my advertising efforts and results.
What’s happening? For brands like mine, this integration is a breakthrough. I can activate Walmart Connect audiences within DV360, reaching potential shoppers through YouTube with precision.
With closed-loop measurement now possible, I can directly connect the dots between ad exposure and purchasing actions at Walmart, making my advertising dollars work harder.
Why do I care? The amalgamation of Walmart’s rich shopper data with YouTube’s vast audience reach allows me to focus on real retail behavior rather than mere inferences, optimizing my targeting strategies.
Crucially, I can move beyond just monitoring views or clicks. I now have the capability to trace if my ads are actually driving Walmart sales, which helps justify my investments and refines my video advertising strategies.
Understanding the bigger picture, retail media networks are increasingly venturing beyond their platforms, delivering shopper insights and measurement capabilities into broader digital advertising spaces where I’m channeling more of my budget.
Reading between the lines, Walmart Connect’s ambition stands out, as they’re pushing to make their audience and analytics tools compatible with more advertising platforms. The conclusion of their exclusivity with The Trade Desk last year certainly paved the way for such integrations.
What do advertisers gain? As an advertiser, I unlock access to Walmart’s audience insights, can reach 150 million weekly U.S. customers via YouTube, and gain precise sales attribution tied to Walmart transactions—all streamlined within DV360.
What’s next for us? The initial focus is on YouTube campaigns, but I’m eager to see how Google and Walmart will expand this integration to cover more inventory over time.
The bottom line? This partnership is a powerful alignment of retail data, media activation, and sales measurement, offering advertisers like me a direct way to connect our YouTube ads with consumer behaviors at Walmart, both in-store and online.
I’ve got exciting news for all Instagram enthusiasts! Instagram has now rolled out an update that allows us to tailor the Your Algorithm controls directly into our main feed experience. This means we have more power to manage the topics influencing our recommendations across Feed, Reels, and Explore.
About Your Algorithm. This feature is designed to allow me to view the topics Instagram thinks I’m interested in. It gives me the option to remove topics I’m not keen on and add those I want to see more frequently. Although Instagram first introduced Your Algorithm for Reels last December, it has since broadened these controls across more recommendation surfaces.
Feed joins Reels and Explore. Now, with this update, I can manage topic-level controls on my main feed. This change means the recommended posts I see—often from accounts I don’t follow—can be more aligned with my true interests.
Instagram generates a list of topics based on my activity, and any tweaks I make to this list help the system fine-tune future recommendations.
More user control. Adam Mosseri, the head of Instagram, mentions that this update addresses how we often feel out of control in recommendation-driven feeds.
“Our system learns from what I tap, watch, and share, but there hasn’t been a clear way for me to tell it what I truly want,” Mosseri explained. With the help of large language models, Instagram can now describe content clusters in simple language, offering me a clearer way to shape the system’s understanding of my preferences.
Interest media. As Gary Vaynerchuk brilliantly put it, there’s a shift happening from follower-based feeds, which he called social media, to interest-based discovery, or interest media. Insights show that platforms like Instagram are focusing on engagement-driven content rather than purely the accounts I follow. With this update, Instagram is transparent about the interests behind my recommendations.
Why we care. Matching user interests has become a priority in Instagram’s discovery process. If you’re creating content, it’s crucial to signal specific topics and audience intent to increase visibility in recommendations.
More controls are planned. Topics are just the beginning! Mosseri assured us that Instagram is also working on controls for people, moods, content types, and other signals.
I’m witnessing a fascinating shift in the search industry, something I hadn’t anticipated witnessing in my career.
The supply of search expertise now outweighs the demand.
We can point fingers at artificial intelligence, the economy, or the increasing commonality of checkbox SEO.
Whatever the cause, the outcome remains unchanged.
SEO job cuts are rising. Openings are dwindling. I’ve never seen the market as competitive in my 15+ years.
The hard truth is many SEO skills that were once invaluable are becoming easier to automate or outsource.
Grab a seat.
I’d love to explore why this is occurring, which skills are now expected, and what SEO talent employers should really be seeking as we move towards 2026.
If I were hiring an SEO in 2026, I would focus less on technical details and more on how candidates handle complex situations.
I’d ask for a disagreement experience.
For example, I suspected H1 tags didn’t significantly impact rankings. Initially, people laughed, and opinions varied until further confirmed by experts.
I care more about their resolve than their correctness.
I’d ask about a failed test.
Experienced SEOs know projects often stall. The key is their follow-through post-failure.
I’d inquire about AI mishaps.
I aim to find candidates who turn knowledge into tangible outcomes.
The hard part has always been delivering results, not knowing what to do.
AI won’t substitute SEOs, but those unwilling to adapt may face challenges.
This article initially appeared on my personal site, shared here with permission.
