Inbound is an approach to marketing that acknowledges a basic fact: the effectiveness of traditional B2B outbound tactics—on the whole—has been steadily declining while simultaneously costing more.
"Old school" outbound tactics that thrust generic messaging on prospects via mailing lists and cold calls (i.e., spam), by and large, don't align with how B2B buyers buy anymore. The way buyers research and discover products has fundamentally changed—and continues to change.
In 2026, buyers have—and expect to have—abundant access to information. They want to execute buying processes partly, mostly, or completely on their own. Now, they're often starting that process not just in Google, but in AI assistants like ChatGPT and Gemini.
| Inbound enables companies to attract those buyers by giving them the information they need to make informed decisions and guide themselves through the discovery and buying process, wherever that process begins. It streamlines and scales the buying process itself by making available all the information buyers need to move through their preferred buying journey. |
Unlike traditional forms of outbound marketing, inbound marketing is focused on attracting potential customers to you rather than interrupting them in an increasingly difficult and expensive bid for attention. By clearly communicating your value, and creating and promoting content aligned with your customers' needs and interests, inbound generates qualified inbound traffic you can convert, close, and delight over time.
Having helped B2B companies establish successful inbound programs for 10+ years, this guide explains our approach to inbound and content marketing as it's evolved to today.
Before getting into the mechanics of inbound in 2026, it's worth addressing the elephant in the room: AI search!
The rise of AI assistants has changed where buyers start their research, but it really hasn't changed the fundamentals of what makes inbound marketing work. When buyers use tools like ChatGPT or Gemini to research on their own, those systems pull from the same sources inbound marketing has always focused on:
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AI search surfaces the results of good inbound work, not shortcuts around it.
Inbound content now does double duty: It helps buyers self-educate and provides the raw material AI systems use to answer questions, recommend vendors, and shape perceptions—often before a prospect ever visits your site. Brands that invest in clear, authoritative, genuinely helpful inbound content are more likely to be cited, summarized, and remembered in AI-driven discovery.
The core principle here hasn't changed: meet buyers where they are, answer their questions, and earn trust over time. The difference is that AI is often the first reader of your content, and the first voice your buyer hears.
We see content—and by extension, inbound marketing, which puts that content to use—as a better alternative to marketing strategies and tactics that cost a lot and tend to deliver little.
Excellent, high-performing content that's easily discoverable by qualified, motivated prospects can propel companies to market leaders and pay dividends in the traffic, leads, and business they can bring in for years to come. Given how buyers seek out solutions today, and the relatively low costs of production, content is simply the most effective way to educate prospects on why they should buy your product or service.
Content is also a company's opportunity to build a relationship with its prospects and other audiences it's looking to reach. It helps people respect your competency and gain trust in you before ever communicating with you.
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We publish content for our clients specifically to:
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We believe that the most sustainable success in content marketing comes from prioritizing quality over quantity. Here's why: until readers register that what they're reading is quality information, they don't consume it. They skim it or skip it. They don't take in your message or the points you're making.
Worse, if you're operating in an industry where expertise needs to be established for buyers, low-quality content can demonstrate exactly the opposite: You could lose potential customers because prospects come away thinking you're not an expert in your category.
This is where we split from many other agencies, especially bigger ones, that farm content out to freelancers or AI who produce high volumes of lower-quality content that doesn't actually work to achieve anything meaningful.
How many times have you jumped into an article expecting something insightful, only to be disappointed by how generic and useless it is? The web is filled with search-optimized "mirage content" that appears to be high-quality but is actually high-level fluff.
The proliferation of fluffy, worthless content stems from writers not actually knowing what they're writing about. And now, on top of that, many are using AI to churn out reams of AI slop.
Without a command of the subject matter, writers often rehash basic information to satisfy search engines and struggle to bring anything new or helpful to the world. This content is often just a reframing of existing ideas that don't delve below the surface of the topic, because the writer (or AI they're outsourcing to) doesn't actually have experiences they can present as insights.
To us, low-quality content may garner some search rank and clicks, but little beyond that. Prospects often don't engage with this content for the reason we just mentioned. Also, lower-quality content typically has a short shelf life since it's vulnerable to being outperformed by content that is high-quality.
Way back in 2022, Google launched its "helpful content update" as part of a broader effort to ensure people see more original, helpful content written by people, for people, in search results. As AI writing tools exemplified by ChatGPT became widely available, Google anticipated a flood of low-quality, regurgitated content littering the web—and they were right!
What's changed since then is that AI search systems have become another layer of quality filtration. When AI assistants answer questions, they synthesize information from sources they deem authoritative. They pull from content that clearly and directly answers questions, demonstrates genuine expertise, and is well-cited across the web. Low-quality, generic content doesn't just fail to rank in Google—it fails to get surfaced in AI answers at all.
Our approach is prepared for a world where both search engines and AI systems reward content that's original and written for people to consume and use. We discovered that since so many companies and agencies shy away from great, in-depth content, we could turn it into a competitive advantage for ourselves and our clients by developing and refining processes that make it easier to produce.
We figured out how to more efficiently do the hard work necessary to create outstanding content on a web filled with mediocre content.
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To us, "quality" is a combination of four things that we prioritize in the content we produce:
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When content hits the mark on all four points, search engines deliver it, AI systems cite it, and humans consume it—helping them understand and solve their problems.
Rather than relying on freelancers or AI tools with varying levels of subject matter expertise, we work directly with our clients to identify subject matter experts within or adjacent to their organization and extract high-quality insights from their brains via interviews.
Here's how it typically works at a high level:
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It's not just humans who seek out high-quality content. Tt's become one of the critical ranking factors for Google, and it's increasingly what determines whether your content gets cited in AI search responses.
When someone finds your page through a Google search, Google measures your post's quality in part by whether someone bounces from it to read another site instead. To keep readers from bouncing, you have to hook them immediately, cover everything they're looking for, and do so concisely.
AI search systems add another dimension: When an AI assistant answers a user's question, it pulls information from sources across the web—and the sources it chooses tend to be clear, direct, well-structured, and authoritative. Content that buries key information, uses vague marketing language, or fails to directly answer questions is less likely to be synthesized into AI responses.
Where other agencies agonize over trivial technical details related to SEO, we recognize that search engines and AI systems alike want to serve users accurate, complete, useful information above all else. That's why we believe high-quality content will weather any short-term algorithm change (or paradigm shift in how discovery works) to deliver the best results over time.
There's a lot to say here, so we'll be brief. There are several factors we consider when deciding what to write about:
AudienceWho is the target audience for the content? Before writing anything, we develop buyer personas to understand and describe who buyers are, what relevant interests they have, what relevant challenges and pain points they experience, and other key details that help guide topic selection. |
GoalsWhat is the purpose of the content, and what goals will it satisfy in the reader? Identifying the goals of the content and reader helps narrow down the topic options and angles or perspectives to take. |
CompetitonWhat topics are competitors writing about that appears to be working for them? Understanding what others in the industry are discussing can help identify gaps in the market or opportunities to differentiate. Also, using a few tools, we often analyze competitor websites to understand which pages are their top performers so we can set out to create even better content and siphon that business. |
Search demandIs there a lot of demand for a topic in search engines? We use several tools to research topics and identify specific keywords that people are searching for, which signal buying intent or a level of relevant interest worth pursuing. These tools aren't exact, but extremely helpful when used as directional guides. |
AI search visibilityWhat questions are AI assistants being asked in our clients' categories? We now also consider how AI systems break down user queries into sub-questions—and whether our content can serve as a trusted source for those answers. |
We're also mindful of how we approach content based on how it's going to be deployed. For example, when we produce content designed to be discovered in search, we aim to fulfill criteria like:
If no one reads what you write, you're not doing content marketing! All too often, content marketers get to the end of creating content only to publish it and move on to writing the next piece without distributing what they just created.
Here are a few of the internal and external channels we use to distribute content:
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It's worth noting that the distribution landscape has expanded. AI search systems now function as another discovery channel—one where you don't "post" content, but where your content may be surfaced and synthesized in response to user questions.
The best way to "distribute" to AI search is the same as it's always been for SEO: create clear, authoritative, well-structured content that search engines can crawl and index. AI systems rely heavily on traditional search infrastructure to find sources. When it comes to retrieving information from the web, it's really not as magical or novel as it might appear.
Also, third-party mentions matter more than ever. AI assistants weigh brand mentions, reviews, and recommendations across trusted sites (including Reddit, Quora, LinkedIn, and industry publications) when synthesizing answers. Distribution now includes ensuring your brand is being talked about in the places AI systems look.
For content to do work for the business, it must not only attract readers but also convert them into subscribers, purchasers, or some other meaningful status. To understand how people who discover and consume content convert into customers, it's important to consider a prospect's typical conversion journey: v
This is just one of many paths a reader can take through your content. Often, these paths are non-linear and may involve many touchpoints over a period of time that ultimately build to a sale. (We've seen prospects consistently open emails hundreds of times repeatedly over the course of years before ever reaching out to sales. Prospect behavior is often strange and unpredictable.)
To continually encourage readers forward through whatever path they choose to take, we focus on four goals:
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We recognize that some content channels are better than others for getting discovered versus engaging and pitching an audience of prospects.
For example, LinkedIn and SEO are best suited for discovery—attracting the attention of new prospects.
By contrast, a newsletter or podcast is optimized for keeping those people engaged—driving demand and sales.
Simply put, the channels best for discovery tend to automatically distribute content for you (SEO, social, AI search) while others have weaker discovery power and are intended to be used with existing audiences (podcasts, newsletters).
With this in mind, we think of audience-building via content marketing as a simple funnel:
1. DiscoverProspects discover you through the content you produce and distribute, whether via search engines, AI assistants, or social platforms. Some of them will convert into contacts and leads. |
2. EngageContinued content promotion via newsletters, nurturing sequences, and follows keeps you in touch with prospects—building trust and demand with each touchpoint. |
3. PitchAfter several touchpoints, you've generated demand and built authority with your audience, so you can naturally start pitching your products and services in context. |
One shift worth acknowledging: AI search changes how we measure success.
When AI assistants provide direct answers to user questions, they reduce the need for users to click through to websites. That means organic traffic—historically a core metric for content marketing—may decline even as your content's influence grows. A prospect might learn about you from an AI-generated response, develop trust over several AI-assisted research sessions, and eventually seek you out directly—without ever showing up in your traffic analytics as an "organic visit."
This doesn't mean inbound is broken. It means we need to expand how we measure impact:
Visibility doesn't always equal clicks anymore—but it still influences buying decisions.
One of the more recent evolutions in B2B marketing over the past few years has been the emergence of what some call "inbound-led outbound"—a hybrid approach that uses inbound content and infrastructure to generate intent signals, then acts on those signals with targeted outbound motions.
The core insight is simple:
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Your inbound marketing already attracts people who are actively researching problems you solve. Many of those people visit your website, consume your content, and leave—without ever filling out a form or identifying themselves. Traditionally, those visitors were invisible. You knew someone came, but not who they were or what company they worked for. That's changed, at least to some degree! A new generation of tools can now reveal who's visiting your site—not individual people, but the companies they work for—using reverse-IP identification. When someone from a company whose IP is known visits your pricing page three times in a week, you can know that, even if they never filled out a form. That's a signal. And signals can be acted on. |
Here's how we typically set this up for clients using HubSpot and Apollo (though similar workflows exist with other tool combinations):
Step 1: HubSpot Buyer Intent captures the signalHubSpot's Buyer Intent tool (part of their Breeze suite) uses reverse-IP matching to identify companies visiting your website. It tracks which pages they visit, how often they return, and whether their activity suggests active research—like repeated visits to product pages, case studies, or pricing. HubSpot surfaces these companies in a prioritized list based on engagement intensity and how you've configured what signals and kinds of companies you care about. |
Step 2: Apollo enriches the signal with contacts (if you don't have them in your CRM already)Knowing that "Acme Corp" is visiting your site is useful, but you can't email a company—you need a person! This is where a tool like Apollo comes in. Apollo (or similar platforms like ZoomInfo) takes the company-level signal and enriches it with contact data: names, titles, emails, and phone numbers of likely decision-makers at that company. You can filter by role, seniority, department, or other criteria to find the people most likely to be involved in the buying decision, then bring that back into HubSpot. |
Step 3: Outbound is triggered by the signal
Once you've identified the company and enriched it with contacts, you can act. This might mean enrolling those contacts in a targeted email sequence, adding them to a LinkedIn outreach cadence, triggering an ad campaign, or simply flagging them for a sales rep to call. The key difference from traditional cold outbound is that you're not reaching out blindly—you're reaching out to people whose company has already demonstrated interest by engaging with your content. The signals give a clue to whether they're actually in market right now. |
This approach doesn't replace inbound, it depends on it! Without quality inbound content attracting the right visitors, there are no signals to capture. Without a well-structured website that gives visitors reasons to explore multiple pages, there's no engagement intensity to measure. Inbound creates the surface on which intent signals appear.
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Think of it this way: your blog posts, case studies, whitepapers, and product pages are doing two jobs now: They're educating prospects (the traditional inbound job), and they're also functioning as a detection grid—revealing which companies are in-market based on what they consume and how often they return. |
The better your inbound content, the more qualified the signals. If your content attracts serious buyers researching real problems, those are the companies that show up in your intent data. If your content attracts random traffic with no buying intent, your signals will be noisy and low-value.
This is why signal-based prospecting isn't a shortcut around content quality. It's a reward for it.
A few things have converged to make inbound-led outbound more viable and valuable than it was even a few years ago:
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It's worth being thoughtful here. Just because you can identify a company and find contacts doesn't mean you should blast them with aggressive sales emails. (Some will disagree with us here—that's okay.)
The best implementations of inbound-led outbound respect the fact that the prospect hasn't explicitly raised their hand. The outreach should feel helpful, not intrusive—more "I noticed your team might be exploring this; here's something that could help" and less "I see you've been on our site 12 times this week."
Done well, signal-based prospecting feels like good timing. Done poorly, it feels like surveillance. The difference is in the tone, the value offered, and the respect for the prospect's autonomy.
One of the biggest limitations of "pure" inbound marketing is that it requires prospects to initiate their own research and enter the funnels companies built for them. Inbound is focused on engaging what's likely a small cohort of your total addressable market who are "in market"—or actively interrogating their problems and/or looking for solutions—at a given time.
Inbound alone doesn't play offense to reach the wider, more passive part of your market that isn't in research or solution-hunting mode, and may not have heard of your company or come across your content, but nonetheless ought to want your solutions and are likely receptive to messaging if put in front of them the right way.
Pure inbound is also reflective of a slightly older web—one where Google was more or less the only viable place to start doing buying research and connect with sellers online.
Today, that's changed meaningfully. Some B2B buyers' habits have changed with the web. Google is still a primary starting point for many buying journeys, but as we've noted, AI assistants have emerged as another entry point. In some industries, community-based professional networks are also a place people start soliciting help solving business problems or look for vendors.
Private or public Slack channels are a prime example of this referral-based system—one where tribes of professionals gather to exchange advice from one another in a new form of word-of-mouth. In practice, those who do engage in newer channels likely mix questions posited to their Slack channels or AI assistants with web searches.
The takeaway is that while inbound is certainly still an effective marketing approach when executed well, it's important to consider and experiment with ways "smarter outbound" can be mixed with inbound to engage more of your market and optimize for more varied modes of buyer research.
Here are a few ways we've been experimenting with blending outbound motions to enhance our inbound marketing:
Intent-based outbound outreachThis is what we just talked about: inbound-led outbound. Tools like HubSpot, ZoomInfo and Apollo.io connect IP addresses to people and companies to reveal potential "intent" which can then be used to trigger outreach based on the fact that a person or company may be in research mode. Intent signals can be used to trigger ad campaign enrollment, cold email, and other outbound motions that attempt to simulate a serendipitous reaction in the prospect ("you caught me at the right time—we were just looking into this!") |
Account-based outbound marketingSales teams often have a "dream list" of accounts that would be crazy not to be working with them because the product or service is such a perfect fit. Account-based marketing uses outbound to tactfully go on offense and engage these accounts first, rather than waiting for them to contact you. Modern cold email tools can be elegantly integrated with platforms like HubSpot to make outbound activities easy to execute and manage. |
Social ads more generallyTargeted advertising is a form of outbound marketing, so it's important to categorize it accordingly. The old-school aversion to advertising that gave rise to inbound years ago was a reaction to spammy, untargeted advertising. But the targeting that can be achieved with today's ad networks is simply too good to ignore. With the right strategy and execution, social ads can be an excellent way to non-obnoxiously engage passive prospects. |
Third-party presence and digital PR
AI search has made off-site signals—like brand mentions on popular websites, top-tier reviews, and a positive reputation in community discussions—more important than ever. Because AI systems rely on third-party citations to understand a brand's offerings and reputation, influencing off-site activity is now essential. This has made collaboration between search, social, influencer, and PR teams more integral than ever before. |
AI search is an evolution of inbound marketing, not a replacement for it. If your content fundamentals are weak—if you're producing generic, low-quality content that doesn't genuinely help your audience—AI search won't save you. If your content fundamentals are strong—if you're creating clear, authoritative, genuinely useful content that answers real questions—AI will amplify it.
The winners will stay grounded, avoid shortcuts, build real authority, and measure success beyond traffic alone.