How to cheaply build and strengthen product-market fit

 

This sense of excitement and camaraderie is the ~vibe~ we were aiming to recreate through our product. Photo by Beyza Kaplan.

I previously wrote an article on measuring the only leading indicator for product market fit (PMF) but I got some questions around what I tactically did to build product market fit, so I thought I would outline my process here.

Even if you know how to measure product market fit, that doesn’t tell you how to actually figure out what to build to increase your level of product market fit. I also haven’t seen many comprehensive or tactical articles on this gigantic topic, so I thought I’d give it a try.

Feel free to share your feedback in the comments section if you’d like to help anyone else who may be wandering through the Forbidden Forest of product-market fit.

Finding or strengthening product market fit can essentially be broken down into four types of problems:

1. Validating the problem
2. Validating the primary target persona
3. Validating market viability
4. Validating the product

I think it goes without saying, but these items are all quite intertwined, and I don’t do them in the same order every time.

This type of topic is always more helpful when tactical examples are given, so below I run through high-level examples of how I did this when I was the head of product at a peer-to-peer sports betting startup.

Note: I value working on ethical products that bring joy to people. Sports betting as a whole may be seen as unethical on first glance, but our product created a transparent and inclusive environment for people who might not typically interact in this space (as well as avid sports betters).

We designed our product to be honest and transparent (e.g. making wins/losses clear so that people could see if they were spending too much) and saw that our average bet size was $15, typically between friends who used betting on basketball or football games to stay in touch with each other.

As product managers, we have a responsibility to be mindful about what types of technology we help bring into the world, so I just wanted to share just a couple of the reasons why I felt comfortable building this product with the team.

Part 1. Validate the problem

1.a. What’s the opportunity/problem that we’re uniquely positioned to address?

Our hypothesis was that we were solving for the fact that, at the time, sports fans couldn’t bet with their friends on betting platforms (now there are a lot of apps popping up around this) — they could only bet against the “house” or random, anonymous people on the Internet.

That’s fine for many fans, but some fans, like our founders, wanted to bet not just to win money, but to engage in some friendly competition against a friend that’s smack talking your favorite player.

The founders of this app loved sports and wanted a space to bet with friends on different games that they cared about. They were uniquely positioned to solve a niche problem by being a power user themselves who had an intricate understanding of all the pitfalls and obstacles involved with betting on sports.

Realizing that the typical sports betting apps intimidated their friends, the founders decided they wanted to create a beginner-friendly space that focused on friendly competition over making money anonymously in a dark room. They also hated that betting apps also don’t let fans create their own bets (i.e. choosing players, stats, odds).

In the Opportunity Solution Tree (OST), you map out your desired outcome, potential Opportunities to solve, Solutions to those Opportunities, and Experiments to test assumptions about your solutions. Looks simple but is actually kind of difficult to do and takes many revisions.

We used the Opportunity Solution Tree to understand the parent-child relationships between different opportunities we were considering, and visualize which directions we were NOT going towards.

Providing an example OST to illustrate how to frame Opportunities and how you can have parent-child relationships between them. It’s also helpful to visualize what Opportunities your product is not addressing.

Product strategy is essentially deciding what problems you won’t solve and making sure that you’re framing the problems you are solving correctly, so this step is critical.

1.b. Do users really care about the problem we’re solving? What are the true alternatives?

When I asked the founder whether not being able to bet with friends was a big pain point for sports betters, his response was “Of course! I run into this all the time and it’s such a pain!”

But when I asked this question in user interviews, the average sports better didn’t seem to register it as a problem. It’s just what they knew. If they wanted to bet with friends, they hopped on Venmo or PayPal. It only became an issue when they had to track their friends down to settle up.

The question then became, “Why use our app when you can just use those alternatives? What other problems do we solve? Does our product offer enough value to change your behavior?”

Our competitors were not just the other sports betting platforms. They also included Venmo, PayPal, and cash, and being aware of that influenced our product strategy big time. Not only did we have to offer unique value on the sports betting side of the experience, we also needed to make sure that we made getting or giving payments was as easy as Venmo’ing someone.

1.c. How do we measure our desired outcome?

There’s a lot that’s already been said about defining OKRs, so I won’t repeat them here. The one key insight on determining how to measure success that I haven’t heard as much is around choosing a north star outcome that is a win-win for both the customer and the business.

A north star outcome should reflect a win-win for both the customer and the business.

Specifically, start by identifying what a win looks like for the person who actually uses your app, and then layer in success for the business.

For example, success for our users is when they can create their own bet and have a friend accept that bet. It means that they found enough value to use our platform over another option, and that they were able to compete with a friend on players that they were both invested in enough to bet on. It’s not a win for the user if they create a bet that no one accepts, because that’s a waste of their time.

From the company’s perspective, the number of accepted bets is also a good reflection of engagement on both sides of the marketplace (people who create the bet and people who accept the bet). Accepted bets is also how we drive our revenue, since we charged a small fee on every successful bet.

Part 2. Validate target persona

Assuming I already have a product, I look at the analytics to see demographics and behaviors of people who are performing the key desired actions. If possible, I interview them in person. That’s exactly what I did here.

Based on what I found in the analytics, I built a few different types of user personas and shared them with my team to get diverse perspectives on who our customers are. If we ever got stuck on defining who we’re building for, I switch tactics to define who we’re NOT selling to, and that usually helped provide clarity. We then tested the accuracy of our target personas by actually marketing our product to people who fit that description.

If I’m working on a marketplace product, then I have two personas to keep in mind. For this particular product, those two personas (the person who creates bets vs. receives them) could be the same person or they could be different. Some people only created and sent out bets, while others preferred to be on the receiving end of a bet. Having two personas and clarifying the different types of each was critical to understand which problems to solve. (This also resulted in two different opportunity solution trees, btw.)

Lately I’ve been experimenting using unmoderated voice user interviews on usertesting.com where users read my prompts out loud, test my prototypes, and tell me what they think.

2.a. What do I do if I don’t yet have a product or a solid target user persona?

I use userinterview.com or usertesting.com to talk to people who I hypothesize are my target users. In addition to moderated interviews that I run myself, I also love leveraging un-moderated voice user interviews here, because it allows to me to ask standardized questions and listen to a high volume of different responses very quickly.

Part 3. Validate market viability

3.a. What’s the market context? Why now?

Before diving into the details of what problems I’m potentially solving, I like to zoom out and see the broader market context of where I’m playing. How competitive is the landscape? What does revenue look like in this market? What’s the latest news?

Through sizing the market and revenue opportunity, we knew that sports betting was a huge and growing market that users cared very much about (and with a high willingness-to-pay). The U.S. saw $7.5 billion in revenue from sports betting in 2022, which was a 72.7% increase from 2021. That’s insane.

Timing also played a big role — the United States Supreme Court had recently issued a highly anticipated decision that struck down the federal ban on state authorization of sports betting. We were one of the first startups to have the necessary state-by-state legislation in place, and this gave us a temporary advantage that we needed to leverage quickly.

3.b. Can we reach customers in a cost-effective way?

We often hear about product-market fit, but product-channel fit is just as critical. In order to have a sustainable customer acquisition cost (CAC), it’s important to find the channel that resonates with your users and your product, and then customize your product and your marketing content to that specific channel.

While it’s good to experiment in multiple channels, most products typically get a majority of their users from one channel, so keep that in mind when finding product-channel fit.

Not only did we test the performance of marketing in various traditional channels (TikTok, Instagram, Google Ads, word of mouth), we also went to where our users are by leveraging stadium or sports team partnerships during live games.

Part 4. Validate the product

4.a. Where are we in the product lifecycle? Does the product have product market fit?

When I first joined, the founders told me that this product was actually in it’s growth phase (rather than 0 to 1 phase). Trust, but verify.

I verified that assumption by measuring the 40% rule, which is a leading indicator for product-market fit. Why did I do this? I wanted to make sure it had strong product market fit before focusing on growth optimization.

If you’re not sure whether you have product-market fit or how strong it is, this is the most comprehensive article I can find on how to calculate and use the 40% rule.

For those of you who aren’t going to read that, the tl;dr is that if roughly 40% of your current users say they would be “very disappointed” if they could no longer use your product, you most likely have product market fit.

By calculating that number and prioritizing feedback from users who were aligned with our core value proposition and who were on the fence about being disappointed if they could no longer use the app, we doubled our product market fit “score” and hit that 40% threshold (read the article if you have questions about this).

4.b. Test core assumptions of the product with zero development

Testing assumptions, rather than entire solutions, allows me to learn 10x faster and often with 0 dev effort.

When I was tasked with increasing the number of bets that get accepted, I had several solution (AKA feature) ideas to solve for that problem.

Instead of jumping to solutions and building each of those solutions in a prototype and then A/B testing them (requires a lot of design/dev work), I identified the assumptions of why people weren’t accepting bets and tested those first.

Opportunity solution trees (OSTs) come in clutch when doing this analysis too!

For example, if we’re trying to increase bet acceptance, we could move the location of the CTA notifying the user that they received a bet, or we could create a push notification, or we could introduce filters so users can find the bet.

Should we build each one and A/B test the results? Or is there a faster way to see which one is most effective?

Why, yes there is! You can instead identify the assumptions of what problem you’re solving for:

• Users don’t know that they were invited to a bet
• Users know that they were invited to a bet, but later can’t find their bet invites
• Users can’t find the bets that they’re interested in accepting on the feed
• Users forget that they’ve been invited to a bet entirely

I identified the riskiest assumption and built a test around that. The result?

I learned within 5 days which assumption wasn’t true, allowing me to confidently decide what to build before spending a single developer hour on it.

Conclusion

Thanks for reading my book (jk but actually).

As always, my tactical articles are much longer than I hoped. Still, I hope I gave tactical insight into how finding or strengthening product market fit can essentially be broken down into four types of problems:

1. Validating the problem
2. Validating the primary target persona
3. Validating market viability
4. Validating the product

https://tinyurl.com/mskpuw8w

Finding Product-Market Fit with the PURE Model: A Guide for Start-Ups

 

Finding product-market fit (PMF) with the PURE model involves optimizing four key pillars—Performance, Utility, Reliability, and Economics—to ensure a product solves a specific market need, as explained by sliwainsights.com. This framework helps startups move beyond just having a product to achieving a sustainable, in-demand solution by focusing on: 

• P – Performance: Meeting or exceeding expectations for speed, efficiency, and quality.
• U – Utility: Ensuring high, intuitive, and practical use for customers.
• R – Reliability: Building trust through uptime and low maintenance.
• E – Economics: Providing cost-effective value that fits customer budgets.

This video explains how to find and measure product-market fit:

 

The PURE Framework in Action

• Performance (P): The product must deliver superior results compared to alternatives, focusing on speed and durability.
• Utility (U): The product must solve a pain point better than competitors, ensuring it is easy and intuitive to use.
• Reliability (R): The product must be dependable, reducing the need for frequent support and maximizing uptime.
• Economics (E): The product must be affordable and provide clear value, ensuring it fits within the target audience's budget. 

Key Indicators of Product-Market Fit

Beyond the PURE model, PMF is validated when: 

• The 40% Rule: At least 40% of surveyed customers say they would be "very disappointed" without your product.
• Customer Behavior: High retention rates and organic, word-of-mouth growth are present.
• Financials: The cost of customer acquisition (CAC) is lower than the lifetime value (LTV). 

Applying the PURE model allows founders to move away from trying to be perfect everywhere, and instead, focus on delivering value in the areas that matter most to their customers, leading to a sustainable, growth-focused business, as discussed in sliwainsights.com. 

One of the most critical challenges facing entrepreneurs is finding the elusive product-market fit—the point at which your offering resonates with your target audience and begins to generate consistent demand. To navigate this journey effectively, start-up founders must be able to clearly articulate and deliver their value proposition. The PURE Model offers a structured, pragmatic framework to help them do just that.

What Is the PURE Model?

The PURE Model breaks down product value delivery into four core components:

• P – Performance
  How well does your product meet or exceed customer expectations compared to alternatives? This includes metrics such as speed, efficiency, durability, and throughput.

• U – Utility
  How useful is the product in real-world applications? Is it intuitive and easy to use? Does it improve productivity or solve a pain point better than existing solutions?

• R – Reliability
  Can customers count on your product? This encompasses maintenance frequency, uptime, and the need for customer support.

• E – Economics
  Is your product cost-effective for the target audience? How does it fit within their budget? Can it be delivered affordably and on time?

Why It Matters for Start-Ups

Entrepreneurs are often tempted to strive for perfection in every category. But the truth is, you can’t be 100% in all four areas simultaneously. Enhancing one aspect usually requires trade-offs in another. Improving utility might compromise performance. Prioritizing reliability could drive up costs. Focusing on performance might extend development timelines and negatively impact economics.

The PURE Model helps entrepreneurs embrace these trade-offs strategically rather than haphazardly. It encourages experimentation to determine which combination best matches the needs and values of a specific customer segment.

Positioning Through Trade-Offs

In practical terms, start-ups can use the PURE Model to define their positioning:

• A productivity app may focus on Utility and Economics, offering ease of use and affordability, while compromising slightly on high-end features.

• A performance-driven drone might emphasize Performance and Reliability, targeting professional users willing to pay a premium.

• A mass-market gadget could prioritize Economics and Utility, accepting that it may not have the highest performance or long-term durability.

By making intentional decisions about which elements of PURE to prioritize, entrepreneurs can craft a compelling story around their product’s value proposition—and ensure their development, sales, and marketing efforts are aligned.

From Model to Market

Once a start-up identifies its PURE profile through testing and feedback, it’s essential to communicate this positioning both internally and externally. Internally, it guides teams in aligning their work with customer expectations. Externally, it helps customers understand the trade-offs and advantages of the product, fostering transparency and trust.

Final Thoughts

The PURE Model is more than a diagnostic tool—it’s a compass for navigating the complex decisions that shape a product’s success. By embracing the inevitable trade-offs between Performance, Utility, Reliability, and Economics, entrepreneurs can position their start-ups with clarity and confidence—and dramatically improve their chances of finding product-market fit.

https://tinyurl.com/yhmpssdw

AI search strategy: A guide for modern marketing teams

 

Written by: Alex Sventeckis

Search no longer rewards keywords alone — it rewards clarity. Large language models now read, reason, and restate information, deciding which brands to quote when they answer. An AI search strategy adapts content for that shift, focusing on being understood and cited, not just ranked and clicked.

Structured data defines entities and relationships; concise statements make them extractable; CRM connections turn unseen visibility into measurable influence. Clicks may decline, but authority doesn’t. In AI search, every sentence becomes a new point of discovery.

This article explores what an AI search strategy is and how content marketers and SEOs can implement an effective one. Readers will also learn how to measure success and the tools that can help. Check your AI visibility with HubSpot’s AEO Grader to see how AI systems currently represent your brand.

What is an AI search strategy?

An AI search strategy is a plan to optimize content for AI-powered search engines and answer engines. An AI search strategy aligns content with how large language models (LLMs) and answer engines interpret, summarize, and attribute information.

Traditional SEO optimizes for rankings and clicks; AI search optimization focuses on eligibility and accuracy so that when AI systems generate an answer, they can recognize, quote, and correctly attribute a brand. This kind of AI search optimization ensures machine learning systems can interpret your brand’s authority and present it accurately across AI Overviews, chat results, and voice queries.

In practice, that means structuring content so every paragraph can stand alone as a verifiable excerpt. Sentences should use clear subjects, defined relationships, and unambiguous outcomes. Schema markup confirms what each page represents — its entities, context, and authorship — while consistent naming helps AI systems map those entities across the web.

This approach reframes SEO fundamentals for the LLM era. Topics, intent, and authority remain essential, but the unit of optimization shifts from the page and its keywords to the paragraph and its relationships.

The Building Blocks of AI Search

Large language models interpret not just words, but the relationships between concepts — what something is, how it connects, and who it comes from. Three foundational elements make that possible: entities, schema, and structured data. Together, these determine whether AI systems can recognize, understand, and cite a brand’s expertise.

Entities: How AI Defines “Things”

An entity is a clearly identifiable thing — a person, company, product, or idea. If keywords help humans find information, entities help machines understand it.

Example:

• Entity: HubSpot (Organization)
• Related entities: Marketing Hub (Product), AEO Grader (Tool), Marketing Against the Grain (Creative Work)

When entity names appear consistently across content and structured data, AI systems can unify them into a single node in their knowledge graphs so that a brand is interpreted as one coherent source.

Schema: How AI Reads the Context

Schema is a type of structured data that uses a shared vocabulary (like Schema.org) to label what’s on a page. It tells search engines and AI models exactly what kind of content they’re seeing — an article, a product, an FAQ, an author, and more.

Examples:

• Adding FAQPage schema clarifies that the section answers specific questions.
• Adding Organization schema connects your brand to official profiles and logos.

Without schema, AI must infer meaning; with it, the developers state meaning explicitly.

Structured Data: How AI Connects the Dots

Structured data refers to any information arranged for machine readability. That includes JSON-LD schema markup and visible structures like tables, bulleted lists, and concise TL;DR summaries. These formats help models extract and relate ideas efficiently.

Structured data improves content eligibility and interpretability for AI search engines. For marketers, structured data forms the technical foundation of Answer Engine Optimization (AEO), making content more eligible for AI Overviews, knowledge panels, and chat citations.

How AI Changes Discovery

Search used to work like a race: crawl, index, rank. Now, it works more like a conversation. LLMs read, extract, and restate what they understand to be true. Visibility still matters, but the rules have changed.

Clarity is now the new authority signal. AI systems surface statements they can quote confidently — sentences that express a clear subject, predicate, and object. The most citable content isn’t the longest but the clearest.

Eligibility now comes before position. Before a model can recommend a brand, it must recognize it. That recognition depends on consistent entities, clean schema, and structured formats such as FAQs, tables, and summaries.

The goal has shifted from outranking competitors to earning inclusion in the model’s reasoning — writing statements precise enough that AI can reliably reference and attribute them.

Dimension	Old SEO (pre-AI)	AI Search (LLM era)
Primary goal	Rankings, CTR	Citations, mentions, eligibility in AI Overviews
Optimization unit	Keyword → Page	Entity / Relationship → Paragraph
Formatting cues	Long sections, link architecture	Summaries, tables, FAQs, short standalone chunks
Authority signals	Backlinks, topical breadth, EEAT	Factual precision, schema, entity consistency, EEAT
Measurement	Sessions, positions, CTR	AI impressions, brand mentions, assisted conversions
Iteration loop	Publish → Rank → Click	Structure → Extract → Attribute → Refine

What “Zero-Click” Really Means

AI search strategy prioritizes earning citations from large language models and optimizing for zero-click results. But zero-click doesn’t mean zero value. It means the first moment of influence happens before anyone visits your site. When AI systems quote your definition or summarize your advice, your brand still earns awareness — it just happens off-site.

In this model, trust builds through representation, not traffic. The goal is to connect the invisible touchpoints to real outcomes.

• AI impressions show how often your ideas appear in AI results.
• Entity mentions confirm how accurately the models recognize your brand.
• Assisted conversions reveal when that early visibility leads to engagement or revenue.

When these signals feed into a CRM, visibility becomes measurable. Recognition — not just clicks — becomes the proof of value.

Where Inbound Marketing Fits

Inbound marketing still anchors the strategy, but the first moment of connection moves upstream. A table, a TL;DR, or a one-sentence definition can now introduce a brand within an AI experience. From there, the familiar lifecycle continues: capture interest, deliver value, nurture, convert, and retain.

The shift is in how teams connect those off-site impressions to real results. That connection depends on visibility data, structured content, and CRM attribution working together. HubSpot’s ecosystem supports that stitching in practical ways:

• AEO Grader reveals how brands appear across AI systems and highlights visibility and sentiment gaps.
• Content Hub ensures templates, content briefs, and modules support consistent structured data and defined entities.
• Marketing Hub enables multi-channel tracking and allows experiments with new entry and conversion paths.
• Smart CRM captures contacts influenced by content, tracks assisted conversions, and links those signals to stage and revenue outcomes.

The fundamentals haven’t changed: Be useful, be clear, be consistent. The difference is that the first win now happens in a sentence, not a search ranking.

AI Search Strategy for Content Marketers and SEOs

An AI search strategy for content marketers and SEOs focuses on clarity, structure, and measurable visibility. The process unfolds in five practical stages:

1. Audit current AI visibility.
2. Structure content for answer engines.
3. Optimize for citations over clicks.
4. Operationalize and automate.
5. Attribute and iterate.

Each stage builds on the last, creating a repeatable system that turns structured clarity into discoverability — and discoverability into influence measurable within a CRM.

Step 1: Audit current AI visibility.

Every AI search strategy starts with understanding how the brand appears across AI environments. HubSpot’s AEO Grader establishes that visibility baseline by querying leading AI engines (GPT-4o, Perplexity, Gemini) to analyze how they describe, position, and cite a brand in synthesized answers.

Source

The report focuses on five measurable areas:

• AI Visibility Score. Frequency and prominence of a brand’s inclusion in AI-generated results.
• Contextual Relevance. How accurately AI engines associate the brand with key topics and use cases.
• Competitive Positioning. How the brand appears relative to peers (Leader, Challenger, or Niche Player).
• Sentiment Analysis. Tone and credibility of AI references to the brand across contexts.
• Source Quality. Credibility of the external sources AI systems rely on when representing the business.

Together, these indicators provide a top-level view of brand representation in AI search. AI Search Grader diagnoses AI search visibility and optimization gaps. Marketing teams receive a snapshot of how clearly AI understands and communicates their identity.

Step 2: Structure content for answer engines.

In this new format, the content’s structure becomes the primary delivery vehicle for ideas and positioning. Think of each heading as a micro-search intent. Beneath it, the first 2–3 sentences should provide a direct answer that can stand alone in AI summaries. This pattern mirrors how LLMs read pages: segment by segment, not end to end.

Practical structure principles to incorporate in the strategy include:

• Lead with clarity. Open with a plain-language answer before adding background or nuance.
• Use TL;DR or summary blocks. Brief recaps under each H2 make information easier to extract for answer engines.
• Keep paragraphs compact. Short sections (roughly 50–100 words) maintain readability for both humans and models.
• Show relationships visually. Tables, numbered lists, and bullet points help AI systems map entities and connections.
• Add schema at the template level. Apply Article, FAQ, or other structured data to the full page so that intent and entities are clear to crawlers and AI systems alike.

HubSpot’s Content Hub enables this structure through AI-assisted content briefs, reusable templates, and module-based schema fields. Together, structure and schema make information easier to interpret, cite, and reuse across AI-driven discovery.

Step 3: Optimize for citations, not clicks.

Traditional SEO optimized content for rankings. AI search optimizes for credibility, meaning your paragraph earns the right to appear in the model’s reasoning chain. That credibility depends on your language’s consistency and verifiability.

LLM citations happen when:

• Entities are clearly named.
• Facts are precise and locatable.
• Relationships are clarified.
• Paragraphs are self-contained.

Use these patterns within paragraphs to write toward a citation:

• [Tool] helps [audience] [achieve goal] through [method].
• [Process] improves [metric] when [condition].
• [Feature] reduces [pain point] for [persona].

A model can extract this information and attach attribution reliably. That’s what moves a line of text from “invisible background noise” to “cited authority.”

Step 4: Operationalize and automate.

An AI search strategy becomes sustainable when automation and consistency support it. Within HubSpot’s connected ecosystem, each tool reinforces the broader AI search optimization process:

• Content Hub – Centralizes briefs, templates, and schema fields to keep structure and metadata consistent.
• Marketing Hub – Runs campaign tests and optimizes CTAs and formats for low-click environments.
• Smart CRM – Unifies marketing and sales data so attribution connects structured content to lifecycle progress.
• Breeze Assistant – Accelerates ideation and content outlining for conversational format.

Together, these tools turn AEO from a one-time project into a repeatable system: structure, publish, measure, refine.

Start this process with HubSpot’s Content Hub and Marketing Hub for free.

Step 5: Attribute and iterate.

An AI search strategy works best as a continual system. The goal is to connect what your content earns in AI environments to what it drives in your CRM. Marketing teams then repeat that process with each update. Over time, this loop turns structured visibility into measurable growth — the practical outcome of a scalable AI SEO strategy.

Start by running the AEO Grader on core pages monthly. Use those results to identify where AI search results improved (and where they didn’t). Refine what works, adjust what doesn’t, and measure again. Over time, this rhythm turns AI visibility into a continuous cycle of structure, validation, and growth.

How Loop Marketing Integrates With Your AI Search Strategy

Loop Marketing is HubSpot’s four-stage operating framework for growth in the AI era. It operationalizes AI search optimization by combining brand clarity, data precision, and continuous iteration within HubSpot’s AI ecosystem.

Source

Stage 1: Express — Define your brand identity.

The Express stage builds clarity. AI tools can generate content, but they can’t replicate perspective or tone. Consistent naming, style, and messaging strengthen entity accuracy so models recognize and attribute a brand correctly across summaries and search results.

Stage 2: Tailor — Personalize your approach.

The Tailor stage aligns content with audience intent. Unified CRM data reveals patterns that inform relevance and timing. Personalization ensures that when AI systems surface content, it resonates with context and feels built for each reader.

Stage 3: Amplify — Extend your reach.

The Amplify stage broadens discoverability across channels. Structured content, distributed through multiple formats, reinforces authority signals that help AI systems and human audiences encounter a brand consistently. Cross-channel repetition turns structure into recognition.

Stage 4: Evolve — Improve through feedback.

The Evolve stage transforms performance data into iteration. Visibility insights and assisted conversions inform what to update and where to focus. Each cycle sharpens accuracy and efficiency, creating a self-learning system that compounds.

Loop Stage	Purpose	Connection to AI Search
Express	Define a brand identity	Strengthens entity accuracy for AI citation
Tailor	Personalize by data	Aligns content to user intent and context
Amplify	Distribute widely	Expands authority signals across channels
Evolve	Analyze and optimize	Feeds insights back into structured updates

How to Measure AI Search Strategy Success

Measuring AI search strategy performance requires blending traditional SEO metrics with new signals from AI visibility and CRM attribution. Measurement goes beyond traffic and into how machine learning SEO systems interpret, quote, and credit expertise.

AI search performance is measured by AI impressions, assisted conversions, and engagement depth. When teams link visibility, structure, and CRM attribution, they can see how AI exposure yields measurable results. HubSpot’s 2025 AI Trends for Marketers report found that 75% of marketers report measurable ROI from AI initiatives, primarily through improved efficiency and insight.

Core Metrics for AI Search Performance

Metric	What it measures	Why it matters
Assisted Conversions	Deals or contacts influenced by a content asset, even without a direct click	Shows how early-stage content contributes to revenue
Schema Coverage	Share of key pages with valid Article, FAQ, or Organization markup	Improves eligibility for AI and answer-engine visibility
Entity Consistency	Uniform naming for brand, product, and author entities	Ensures correct recognition and citation in AI summaries
AI Visibility	How often a brand appears in AI-generated results (AEO Grader, Gemini, Perplexity)	Expands reporting beyond clicks to include AI exposure
Engagement Depth	Time on page, scroll rate, and repeat sessions from structured content	Indicates quality of engagement after AI discovery

Emerging or Stretch Metrics

These indicators point toward where attribution is heading, not where it is today. AI visibility data doesn’t directly integrate into CRM or analytics platforms (yet), so these signals work best as experimental metrics that provide directional insight.

• AI Share of Voice – Frequency of brand mentions versus competitors in AI results.
• AI-Informed Pipeline – Revenue influenced by AI-discovered contacts.
• Brand Recall via Entity Health – Consistency of brand phrasing in AI outputs.
• Lifecycle Velocity – Speed of movement through CRM stages after AI exposure.

Making AI Visibility Measurable

An AI search strategy becomes measurable by relying on the systems that already prove marketing performance. Today, HubSpot supports practical measurement through assisted conversions, engagement depth, and structured-data visibility — all available inside Smart CRM and Marketing Hub. AEO Grader adds narrative and competitive context, showing how AI systems describe the brand. Together, these signals create a repeatable framework for improvement, while newer AI-specific metrics continue to evolve.

How HubSpot’s AEO Grader Can Help

HubSpot’s AEO Grader analyzes how leading AI engines describe a brand when answering real user queries. Instead of measuring clicks or rankings, the Grader evaluates brand visibility, narrative themes, sentiment, and competitive standing inside AI-generated responses. It reveals how AI systems characterize a company in synthesized answers and whether that representation aligns with the brand’s goals.

AEO visibility depends on how consistently and accurately AI engines summarize your brand. The Grader turns those qualitative signals into structured indicators that highlight strengths, gaps, and opportunities to improve AI-era discoverability.

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What the AEO Grader Evaluates

The AEO Grader report includes three primary dimensions related to a brand’s AI search visibility.

Metric	What it checks	Why it matters
AI Visibility / Share of Voice	How often a brand appears in AI-generated answers across GPT-4o, Gemini, and Perplexity	Shows relative brand presence in synthesized AI results and category conversations
Brand Narrative & Sentiment	The tone, themes, and language AI engines use when describing the brand	Highlights which storylines shape perception and how credibility or expertise is framed
Source Credibility & Data Richness	The authority and completeness of external sources AI engines reference	Reveals whether models rely on strong, reliable information or weak/noisy sources

Run this audit consistently (quarterly or monthly) to get a clear timeline of how AI systems shift their descriptions, introduce new competitors, or adjust sentiment. Tracking these changes over time shows whether your brand is gaining clarity and relevance or losing ground in AI-generated narratives.

Frequently Asked Questions About AI Search Strategy

How long does it take to see results from an AI search strategy?

Most teams start seeing movement within a few weeks of implementing structural updates, like adding schema or tightening TL;DR sections. But sustainable visibility usually takes three to six months.

AI systems surface new content quickly, but actual results depend on model refresh cycles and the consistency of your updates. HubSpot’s 2025 AI Trends for Marketers Report shows that AI adoption speeds up content production and experimentation, giving teams more frequent opportunities to refine and update structured content — a key factor in improving AI visibility.

Do I need to rebuild my entire content library for AI search?

No, you can evolve what you already have. Start by modernizing your highest-performing pages — the 20% that drives most of your organic or assisted conversions.

Add Article and FAQ schema (using built-in blog templates or custom modules), clarify entities (brand, author, product), and insert concise TL;DRs under each major heading. Then, move outward through supporting pages. This incremental approach builds visibility faster and avoids overwhelming your team.

Which structured data should I implement first?

Start with structured data that helps AI systems interpret both content and context. At the content layer, use visible structure: tables, bulleted lists, and short Q&A sections under each heading. At the metadata layer, apply Schema.org markup, starting with Article, FAQPage, and Organization. These schema types clarify what the page covers and whom it represents.

How do I prove value to leadership when clicks are declining?

Zero-click environments require conversion paths that do not rely on traditional clicks. They show influence, not traffic. Traditional analytics miss the visibility your brand gains when AI systems cite or summarize your content.

Connect visibility to revenue with the following tools:

• AEO Grader, which shows brand presence and sentiment in AI results.
• HubSpot Smart CRM, which shows contact and deal movement influenced by AI-discovered content.
• Marketing Hub, which showcases conversions and engagement depth.

What’s the best way to keep AI search work sustainable?

AI search optimization stays sustainable when it’s folded into your normal reporting cycle.

• Run AEO Grader audits on a consistent cadence (monthly or quarterly) to track how AI systems describe your brand and competitors.
• Use Content Hub templates and custom modules to keep structured data and schema fields current.
• In Smart CRM, log or import the insights from each audit so engagement and lifecycle metrics can be reviewed alongside AI visibility trends.

Does Loop Marketing replace inbound marketing?

Inbound marketing still forms the foundation. Loop Marketing builds on it to meet the realities of AI-era discovery. Where inbound organizes around a linear funnel, Loop Marketing creates a four-stage cycle — Express, Tailor, Amplify, Evolve — that keeps your brand message adaptive across channels and AI systems.

Do I have to use HubSpot products to implement an AI search strategy?

No, but HubSpot’s connected tools make implementation easier. You can apply AEO principles manually, but HubSpot’s ecosystem streamlines the process:

• AEO Grader surfaces brand visibility, narrative, sentiment, and competitive gaps across AI systems.
• Content Hub centralizes creation, supports schema-ready templates, and includes AI-assisted content features.
• Marketing Hub and Smart CRM track engagement and convert signals into revenue outcomes. You can also import or tag AI visibility data manually for full-funnel attribution.

According to HubSpot’s 2025 AI Trends for Marketers Report, 98% of organizations plan to maintain or increase AI investment this year. Connected tools simply speed up progress.

How will I know if AI systems recognize my brand?

Use AEO Grader to see how AI systems describe your brand and where you appear in category-level answers. Then, test key topics directly in assistants like Gemini, ChatGPT, and Perplexity to see how individual pages are referenced.

Make AI search strategy a system, not a sprint.

AI search has reshaped how visibility works, but the fundamentals still apply: Clarity earns trust, and structure earns reach. Winning marketers will build systems that connect visibility to measurable outcomes.

HubSpot’s AEO Grader makes AI visibility tangible. It reveals how generative search systems describe a brand — what they highlight, how often it appears, and how the story compares to competitors. These insights help marketing teams see where their message lands inside AI-driven discovery and where clarity or coverage needs work.

AI search has become measurable not by clicks, but by presence and perception. The smartest way to improve both is by understanding how AI already represents your brand.

Get a free demo of HubSpot’s Breeze AI Suite and Smart CRM and see how HubSpot connects AI visibility, structure, and attribution.

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