Bottom-of-funnel Content for Growth-Stage B2B SaaS | Content Strategy Vibe Marketer⚡️| Founder @ BOFU.ai

I was an in-house content strategist for over a decade. 
I got amazing results running BOFU programs in-house.
But since becoming a consultant I've been able to:

✅ Increase blog conversions from 6 to 30/mo ~ 5 months
✅ Achieve over two dozen first page rankings for teams 
✅ Take blog conversions from 5% to 30% ~ 90 days
✅ Drive 7-figures in attributable inbound pipeline
✅ Empower content teams to learn BOFU marketing 

When clients come to me they have a very specific goal:
Drive inbound leads, sales and customers from their blog 

But they're usually running into the following problems:
❌ Too much (or only) top-of-funnel content on the blog 
❌  Seeing an increase in traffic but not leads 
❌  Most of the leads are coming from paid ads vs oragnic
❌  Their content team is lean and limited on resources 
❌  Content and SEO agencies aren’t delivering
❌  In-house writers have knowledge gaps about BOFU 
❌  Not able to clearly attribute content to pipeline 

Here's how I help content teams:
- Creating a bottom-of-funnel comparison page strategy 
- Showing clear content to pipeline attribution 

If you’re a VP of Marketing or Marketing lead and you’re facing one or more of these problems: 

- Producing content there’s a lack of strategy and direction
- Not seeing attributable inbound signups from blogs
- Working with SEO agencies but not seeing inbound leads
- No or limited inbound leads coming from the blog  
- You or your team's knowledge of BOFU is limited 

I'm here to help you out. 

Let's talk!

Lashay Lewis

If you want to create content that surfaces in LLM's...
Don't start with a keyword. Use these 3 steps.

I used to create content how most writers are taught.

- Look around the company's website
- Get a sense of what they really do
- Find keywords for their audience
- Check the volume
- Start creating

But doing research this way is backwards.

Keywords are less relevant with LLM's.

Queries aren't formed the same way there...

As they are in Google.

- They are longer.
- More contextual.
- More conversational.

So where do we start now?

How do we get content to show up in LLMs?

> Persona research <

Here's how it works (3 steps).

STEP #1. Dig into the pain points

Since LLM searches are more pain driven than keyword driven...

We need to dig into what pains our persona is having.

Then we reverse engineer those pains back into a potential query they could be searching.

And you find that pain through customer research.

Supporting inputs.

STEP #2. Gather internal documents:

- ICP documents
- Sales call transcripts (GOLD)
- Closed/won data
- Analysis step

STEP #3. Query Specifics

I then take that internal info and query certain specifics I want from it like:

- Basic profile
- Responsibilities
- Pain points
- Goals and objectives
- Decision making profile

This helps me get crystal clear on:

- Who I'm talking to
- What they're struggling with
- What their day to day looks like
- Their goals

So when I'm creating content...

All of these factors are taken into account.

Content isn't written in a vacuum.

Different personas will care about different features and outcomes of your product.

This is how you make sure they're all captured.

Why does this work for LLMs?

The words and phrases they're using to describe their pain on sales calls...

Are probably similar to the ones they're searching in LLM's.

Use that info to guide your strategy.

P.S. I built a really big playbook all about how I use AI to run my bottom-of-funnel process. Everything from customer research, to how I do framework based prompting. Its the same one I teach in my live workshop. 

Link in the comments if you'd like to grab it.

Lashay  Lewis

Founder @ BOFU.ai | Bottom-of-funnel Content Systems for Growth-Stage B2B SaaS | The BOFU Queen

What if you could attend an hour long workshop.
And have a step by step process on ranking in AI search? 

Im partnering with the team at TripleDart to give you:
Creating a 3-Phase BOFU content system that ranks in AI search.

Im covering my entire productized process I've been running for my clients.

Nobody does BOFU the way I do so if you come you'll learn something new.

I haven't shown any of this outside of my paid workshop. 

Its happening tomorrow! 

Thursday, March 5, 2026 at 9:30 am EST.

Here's the link to register: https://lnkd.in/egXwJyYj

I hope you'll join us!

Helping tech companies with SEO & content.

I help businesses drive organic growth through strategic content and SEO. 

With 20 years in the tech industry, I've helped them scale in visibility, leads, and revenue. 

This experience fuels my lectures at universities, workshops and industry events, where I enjoy empowering others.  

When not strategizing, you'll find me enjoying live music, comedy shows, or exploring the local food scene with friends.

Lily Grozeva

This might be the end of the landing page as you know it.

Google was granted a patent so that it is no longer just about ranking, but also about whether Google trusts your landing page experience enough to send traffic there at all.

If your UX, structured data, and conversion flows don’t meet their quality thresholds, Google could increasingly intervene in the journey

This means less control over your narrative, data capture, and brand differentiation.

B2B SEO & AI Search Advisor for Tech Companies

Data & ML Engineer | SEO & Search Systems | LLM Applications

Google just got a patent granted (US12536233B1) for generating AI-built landing pages that replace yours when your site doesn't meet their quality threshold.

Read that again.

The system works like this: you search for something, Google returns results, and behind the scenes it calculates a "landing page score" for each result based on conversion rate, bounce rate, CTR, page design quality, and content quality. If your score crosses a threshold (meaning your page is bad enough) Google generates its own AI page for your brand and inserts a navigation link to it directly in the search results.

That AI-generated page pulls from your product feed, includes a chatbot, shows call-to-action buttons to your PDPs, and is personalized to the user based on their entire search history. It's not a cached version of your site. It's a net new page Google built about your brand, for your customer, using your data.

If you've been paying attention to the trajectory — AI Overviews summarizing your content, AI Mode enabling multi-turn conversational search, Personal Intelligence connecting Gmail and Photos to search results, Universal Commerce Protocol enabling checkout without leaving Google — this patent is the logical next step. Google is building the capacity to not just summarize your content but to fully intermediate the brand-to-consumer experience when it decides your landing page isn't good enough.

For e-commerce specifically, claim 13 in the patent is telling: the landing page score exceeds the threshold "when the first landing page does not have a filter for products." Google is literally scoring whether your PLP has functional filtering UX.

The SEO implications here are significant. The conversation is no longer just about ranking, but whether Google trusts your landing page experience enough to send users there at all, or whether it decides to build something better on your behalf. Your product feeds, structured data, and on-site UX aren't just optimization levers anymore; they're the inputs Google uses to decide if you still own your own customer journey.

This doesn't mean every site is getting replaced tomorrow. Patents aren't product launches. But the infrastructure is clearly being built, and it fits perfectly into the agentic commerce stack Google has been assembling with UCP, Business Agent, Direct Offers, and AI Mode shopping.

The brands that will navigate this well are the ones investing in landing page quality, robust product data, and genuine user experience. Not because Google might penalize them in rankings, but because Google might just build a better version of their storefront and serve it instead.

B2B companies still treat SEO as a content workflow. 

Topics are planned, articles are published, rankings are monitored. 
Meanwhile, sales teams operate separately, 

• events run on their own calendar, 
• product pages evolve independently, and 
• brand messaging shifts depending on the channel.

Organic visibility and SEO no longer work like that. 
They are a function of:

• organic search results, 
• AI-generated summaries, 
• branded / navigational searches, 
• industry platforms and news outlets, 
• product documentation, FAQ sections, product and industry term glossaries 
• partner mentions, and 
• thought leadership 

All contribute to 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗷𝗼𝘂𝗿𝗻𝗲𝘆. 
Buyers do not experience these touch points in isolation. They form an aggregated perception of 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆, 𝗰𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆, and 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲. 

The companies that will win over the next few years are 𝗻𝗼𝘁 𝘁𝗵𝗼𝘀𝗲 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗻𝘁𝗲𝗻𝘁. 

They are the ones aligning search with positioning, positioning with product narrative, and product narrative with revenue goals. They treat visibility as an integrated system rather than a sequence of campaigns.

AI-driven discovery accelerates this shift. It amplifies brands that demonstrate coherence across surfaces and quietly ignores those that operate in silos.

If your search strategy is still measured primarily in rankings and impressions, you are looking at the symptom, not the structure.

The real leverage now lies in 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗵𝗲𝗿𝗲 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝘀, and directly supports pipeline generation.

That is a very different conversation than “how do we get more traffic.”

Most conversations around AI search are happening at the tactic level.

LLMs.txt files. Chunk optimization. Prompt patterns. Citation tweaks.

None of that is where the real struggle sits.

What I keep seeing across B2B organizations is something much less discussed and much more structural.

The biggest gap is operational and strategic at the same time.

Many teams simply do not have the internal capacity, structure or alignment to implement what they already know needs to be done. Not just for AI search. The same was true for SEO for years.

The constraint is rarely technical. It is organizational.

Who owns the work.
How priorities get aligned across content, product, marketing and engineering.
How content and technical changes get made.
How execution is resourced and sequenced.

Without those answers, even the most sensible recommendations stall.

This is why I think owning the AI search in the next 12–18 months will not be defined by AI search tactics. It will be defined by which organizations can operationalize visibility consistently across teams and channels.

Curious how others are experiencing this inside their organizations.

Helping B2B brands survive and thrive as SEO upgrades to AI Search.

AI search/SEO programs are not failing because teams don’t know what to do.
They’re failing because organizations don’t know how to change.

Kevin Indig put this bluntly in his latest memo: AI-SEO is a change management problem.
I agree with Kevin, and talked about that on Linkedin many times. And this is exactly what I keep seeing inside companies right now.

The pattern is always the same.

Leadership buys tools. Teams issue recommendations. Reports get built. Nobody agrees what success actually means (or as it with AI search the last few years - no one knows).

One stakeholder wants “rankings in ChatGPT.”
Another wants citations.
Another wants traffic recovery.
Another wants brand mentions.

So every recommendation gets judged differently. And slowly, momentum dies.

𝗔𝗜 𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘁𝗮𝗰𝘁𝗶𝗰𝘀 𝘀𝗵𝗶𝗳𝘁. 𝗜𝘁’𝘀 𝗮𝗻 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘀𝗵𝗶𝗳𝘁.

AI Search changed the discovery layer. Which means SEO stopped being a page-level discipline and became a system-level one. 

Narrative systems, citation systems, distribution systems, measurement systems.
Systems force alignment - alignment forces change.

That’s the real bottleneck.

Inside most orgs I see the same failure modes Kevin outlined:

• Conflicting KPIs across teams
• Turf fragmentation between SEO, content, PR, brand, product
• Panic testing instead of strategic reorientation
• Reporting that leadership doesn’t trust or understands

None of this gets fixed by better prompts or another tool.

If you’re leading AI search internally, your job has quietly changed.
You’re no longer just running SEO. You’re running a change program. SEO was a change program too, but complexity level got way higher now.

Which means your leverage is definitely not tactical execution - it’s alignment, governance, and incentives.

The companies making real progress right now aren’t the ones with "truer" recommendations or more impactful tactics.

They’re the ones redesigning how work actually moves across the organization.

I’ll drop Kevin’s article in the comments. Worth reading if you’re trying to move organic search from recommendations to execution.

We’ve spent ~20 years designing websites for humans to look at.

But a growing share of “visitors” don’t actually look at anything.

They land on your site, read the structure, extract what they need, and leave.

No scrolling, no brand moment, no appreciation for how nice your homepage looks.

Just - can I complete the task or not?

Most websites are still built as visual layers on top of pretty messy foundations. Heavy JS, custom interactions, things that make sense to a human, but not to a machine trying to understand what’s going on. 

AI agents don’t “figure it out.”

They rely on structure, proper HTML, labels that make sense, content that exists without needing to execute half your frontend.

If they can’t move through your flow cleanly, they don’t get stuck.

They just go somewhere else, and you may never even know it happened.

This is less about SEO in the traditional sense, and more about whether your site is usable by something that behaves like a very efficient, slightly impatient user.

Accessibility suddenly matters in a very non-theoretical way. 

Not because of compliance, because it’s how these systems read your site in the first place.

We’re already seeing early versions of this shift.

Tools that interact with websites more like APIs than pages. Forms becoming structured inputs. Flows becoming something you either can or cannot execute.

Right now, most teams still assume their primary audience is human.

That assumption probably holds for the next year or two. After that, it will start to break.

And when it does, this won’t be a gradual shift you can ignore. It will show up as silent leakage of lost conversions and demand that never reaches you.

So the real question isn’t whether this is coming but whether you’re already preparing for it.

Are you ready to start optimizing for non-human users? 

And do you actually know how big the gap is on your own site?

For years, search worked like this - someone typed a question, Google returned a list of links, and brands competed to rank first. 

𝗧𝗵𝗮𝘁 𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴.

In a recent conversation, Google’s VP of Product for Search, Robby Stein, explained that Google has trained its models to link out based on “query fan-out,” meaning complex questions are decomposed into multiple subquestions and answered using different trusted sources. 

In practice, Google is no longer selecting a single “best page.” It is assembling an answer (example query and its answers in the few screenshots from querytool .ai).

𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗕𝟮𝗕 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗮𝗻𝗱 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲:

1. 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝘂𝗽𝘀𝘁𝗿𝗲𝗮𝗺. 
   
Decision-making increasingly happens inside AI summaries before a buyer clicks anything. 
   
If your brand is not cited across the sub-questions shaping the answer, you are unlikely to make the shortlist.

2. 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗳𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴. 
   
You may still rank, drive traffic, and appear “visible” in reporting dashboards, while losing the parts of the conversation that actually determine vendor selection. 
   
The real question is no longer “Do we rank?” but “Which parts of the decision do we influence?”

3. 𝗕𝗿𝗮𝗻𝗱 𝗽𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗶𝘀 𝗯𝗲𝗶𝗻𝗴 𝗺𝗲𝗱𝗶𝗮𝘁𝗲𝗱 𝗯𝘆 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀. 
   
AI systems synthesize positioning, comparisons, and recommendations from multiple sources. 
   
If those inputs come primarily from competitors, analysts, or third parties, you are not controlling how your brand is understood at the moment it matters most.

𝗪𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗕𝟮𝗕 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆?

Shift from keyword planning to decision-journey coverage. 

Understand how AI decomposes your category (definitions, use cases, comparisons, implementation, risks) and ensure you are the most credible source for specific parts of that journey. 

Structure content so it can be extracted, cited, and trusted, not just read.

Rand has been preaching about correlation ≠ causation for the last 20 years, at a minimum.

This time, it is about citations, not being the cause of AI search results inclusion.

In most modern answer systems (Google AI Overviews, ChatGPT with browsing, Perplexity, etc.), the process roughly looks like this:

1. The system interprets the query.
2. It predicts what a good answer should contain (model prior).
3. It retrieves supporting material from the web or internal indexes (RAG layer).
4. It composes the answer.
5. It attaches citations that best support what it already decided to say.

That last part is where confusion happens. People assume step 5 explains step 3. 

It doesn’t.

It also doesn't mean that getting cited is a mistake. Again, from the rooftop: 
correlated ≠ causal
but correlated ≠ irrelevant either

Listen to his entire recording and deep-dive into the comments in his post. It's worth it.

Cofounder of SparkToro, Alertmouse, & Snackbar Studio. Author of Lost & Founder. Feminist. I love underdogs, cooking, & helping people do better marketing

I'm really worried that marketers are getting bamboozled by all the "AI Citation Research."

We see things like "Reddit dropped from 8% to 3% of citations in AI answers," and think that Reddit posts must therefore have less influence on the brands ChatGPT, Claude, or Google present in their responses... But that's not true! Citations are correlated, sure, but not causal, to which brands appear in AI answers (and that's further complicated by RAG vs. base model).

I know all this sounds complex, but I did my best to break it down in the latest #5MinuteWhiteboard 👇

p.s. Lily Ray points out that in some cases of RAG where information is very recently updated and/or quite limited, the response will pull exclusively and causally from the citations.

I’ve been thinking a lot about where search is actually going after reading Liz Reid’s interview and a couple of other pieces this week.

It feels like we’re in that awkward middle phase where nothing is fully clear yet, but the direction is obvious enough if you pay attention.

I wrote a short breakdown of what I think is really changing and what it means for B2B leaders and marketing teams trying to stay visible right now.

Hope this brings a bit more clarity.

The most important thing Liz Reid said in her recent interview was not a product update. It was uncertainty. Google’s Head of Search was very open abo

Open article: What Liz Reid 's (Google Head of Search) Interview Tells Us About the Future of Search by Lily Grozeva • 3 min read

What Liz Reid 's (Google Head of Search) Interview Tells Us About the Future of Search

The current narrative around Agentic AI is split into two loud and equally unhelpful camps: the utopian automators and the cautious laggards.

Both are missing the point.

𝗧𝗵𝗲 𝗿𝗶𝘀𝗸 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗔𝗜. 𝗧𝗵𝗲 𝗿𝗶𝘀𝗸 𝗶𝘀 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿.

We are watching a rush to deploy autonomous agents across marketing and sales workflows. Speed without direction isn’t progress, it’s high-velocity chaos.

Agentic AI excels at structured workflows, research aggregation, and operational acceleration. It is a world-class 𝗱𝗼𝗲𝗿.

But it is a dangerous 𝘁𝗵𝗶𝗻𝗸𝗲𝗿.

When you delegate core judgment calls to an agent without a senior operator in the loop, you’re not being AI-first. You’re being brand-reckless.

Agents cannot define market positioning, shape narrative from lived customer context, or make distribution decisions with real commercial awareness. Those remain operator responsibilities.

Brand equity compounds slowly. Organic visibility is fragile.

Right now, inexperienced teams are deploying agents to scale output before they have clarity on positioning. That’s the mistake.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝗰𝗮𝗹𝗲𝘀 𝘄𝗵𝗮𝘁𝗲𝘃𝗲𝗿 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗲𝘅𝗶𝘀𝘁𝘀, 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝗼𝗿 𝗰𝗵𝗮𝗼𝘀.

If your systems are weak, agents will expose them faster.
If your voice is generic, agents will amplify it at scale until it disappears into the Sea of Sameness.

I am pro-Agentic AI. What I am against is marketing theater.

The strongest B2B leaders I know use agents as multipliers, not drivers. They use them:

• 𝗨𝗻𝗱𝗲𝗿 𝘀𝘁𝗿𝗼𝗻𝗴 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀
You upload an SME interview into a GenAI tool, but the expert rewrites the LinkedIn or Substack draft to align positioning, accuracy, and buyer intent.
  
• 𝗜𝗻𝘀𝗶𝗱𝗲 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀
Before letting agents drive visibility, you lock in technical SEO, architecture, schema coverage, and brand workflows so outputs stay controlled.
  
• 𝗔𝗳𝘁𝗲𝗿 𝘁𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗶𝘀 𝘀𝗲𝘁
Once 𝙡𝙚𝙖𝙙𝙚𝙧𝙨𝙝𝙞𝙥 defines ICP, messaging, priorities, and the content calendar, AI scales briefs, drafts, and repurposing across formats and channels.

Don’t confuse speed with progress.

If you replace thinking with prompting, you’re not building a future-proof company. You’re automating your own irrelevance.

Agentic AI is a tool of operational discipline. Use it to sharpen your edge, not dilute your brand.

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