SaaS Total Addressable Market:

SaaS Total Addressable Market: A Misunderstood Concept

SaaS Total Addressable Market: A Misunderstood Concept

TAM, SAM, and SOM are starting points. Not answers. In a SaaS market that is actively restructuring itself, treating these numbers as gospel is how you get caught off guard.

Everyone loves a big TAM.

Put a billion-dollar number in a deck and watch the room light up. Investors lean in. Founders feel validated. The marketing team finally has a number to put on the homepage.

And then reality shows up.

Because the SaaS market right now is not behaving like the TAM said it would, especially when you look at recent SaaS market trends. Categories are collapsing. AI is eating whole product lines for breakfast. Companies that had defensible positions two years ago are scrambling to find a reason to exist.

The TAM did not warn you. But it could have.

That is the problem. Not the metric itself. How everyone is using it.

TAM, SAM, SOM: What They Actually Tell You

Let us get the basics out of the way fast.

TAM is the total universe of people who could theoretically buy what you are selling, a concept closely tied to understanding your SaaS product-market fit. SAM is who you can actually reach. SOM is who you will realistically win.

Most SaaS teams calculate these once, feel good about the numbers, and move on without aligning them to real SaaS metrics that reflect performance. The TAM goes into the deck. The deck goes into the board meeting. The board meeting produces a strategy built on a number nobody revisits.

This is backwards.

TAM is not a destination. It is a compass. It tells you which direction the market is pointing. And right now, for SaaS, that compass is spinning.

The market is not expanding the way the models assumed. It is contracting in some segments, fragmenting in others, and getting absorbed by AI platforms in ways that make entire product categories irrelevant. If you calculated your TAM in 2021 and are still using those numbers, you are navigating with an old map.

The Real Problem With How SaaS Reads TAM

SaaS companies have a bad habit of treating TAM as a ceiling to aspire toward. It is not. It is a mirror.

The composition of your TAM reflects the culture of your buyers. Healthcare buyers move slowly and care about compliance. Startup buyers move fast and churn fast. Enterprise buyers have committees and timelines that have nothing to do with your product roadmap.

If you are not reading the culture inside your TAM, you are just reading a number, instead of leveraging proper B2B SaaS customer segmentation. A number with no instructions attached.

And in SaaS right now, the culture has changed dramatically. Buyers are exhausted, which is evident when you analyze current B2B SaaS funnel conversion benchmarks. There are too many tools that promise transformation and deliver incremental improvement. The trust erosion is real. You can feel it in longer sales cycles, more skeptical procurement teams, and buyers who have been burned before asking harder questions than they used to.

Your SaaS TAM might look the same on paper. The buyers inside it are not the same people they were.

SAM Is Where the Honesty Lives

Here is a question most teams avoid.

If TAM is the whole ocean, why is your SAM so small?

Because you cannot actually serve most of your TAM. Geography limits you. Your product roadmap limits you. Your support infrastructure limits you. Your pricing model eliminates entire segments before a conversation even starts.

SAM forces you to be honest about where your B2B SaaS growth marketing strategy is truly effective. And honesty is uncomfortable when you have been telling investors you are going after a massive market.

But SAM is also where the strategy lives. Because the gap between TAM and SAM is not just a limitation. It is a map of where to go next. Expand your language support and part of that gap closes. Launch a self-serve tier and another chunk becomes reachable, especially when supported by strong SaaS inbound marketing. Build the integration a specific vertical needs and suddenly a segment that ignored you starts paying attention.

SAM is not a fixed number. It is a decision.

SOM Is Telling You Something Most Teams Ignore

SOM is the uncomfortable one.

It is the number that says: of everything you could theoretically win, this is what you are actually winning.

And in a crowded SaaS market, that number is humbling.

But here is what most people miss about SOM. It is not just about competitive intensity. It is about fit. If your SOM is a small fraction of your SAM, one of two things is happening, and often it ties back to gaps in your SaaS product marketing strategy. Either the market does not understand what you do well enough yet, or you have not figured out how to communicate why your solution is the obvious choice for the segment you are targeting.

Both of those are solvable. Neither of them gets solved by ignoring the ratio.

SaaS Is Forcing a Reevaluation Nobody Wanted

Here is the situation nobody wants to say out loud.

SaaS as a category grew on the back of distribution, not differentiation. Get to market fast. Land and expand. Optimize for MRR. The TAM was so large and the market so willing that you did not need to be the best. You needed to be good enough and visible.

That era is ending.

AI has compressed what used to take a team of people into workflows that cost almost nothing, a shift highlighted in emerging AI SaaS trends 2026. Categories that were safe, project management, basic analytics, lightweight automation, are now being absorbed or disrupted. The TAM for those categories is not growing. It is reorganizing under new ownership.

So when you calculate TAM now, you have to ask a different question than you used to. The old question was: how many companies could buy this? The new question is: how many companies will still need to buy this in two years?

That is a harder question. But it is the right one.

What This Means for Your GTM

If your TAM is contracting, your SAM strategy needs to get sharper, not broader, aligning with proven SaaS growth strategies to scale your business.

This is where most SaaS companies make the wrong call. They see the market getting harder and they try to expand their addressable market to compensate. They go upmarket. They chase new verticals without first strengthening their B2B SaaS marketing principles. They rebuild the product for a segment they have never served before.

Sometimes that works. More often, it dilutes the one thing they were actually good at.

The better play is to look at where inside your SAM you are already winning, and then accelerate traction with targeted lead generation services that bring the right buyers to your pipeline. Winning. Where are your customers renewing fastest, expanding usage most, referring other buyers most naturally? These signals are central to reducing churn in SaaS.

That segment is your real TAM right now. Not the number in the deck.

Go deep before you go wide. The SaaS companies surviving this period are the ones that got brutally specific about who they serve and why that segment cannot live without them.

TAM Is a Starting Point. Not a Strategy.

And this is the whole point.

TAM, SAM, and SOM give you coordinates. They tell you the size of the territory, the slice you can reach, and the slice you can realistically win. That is useful. That is necessary. But it is not a plan.

The plan comes from reading what is inside those numbers and aligning execution with your broader SaaS marketing strategy. Who are the buyers? What do they actually care about? How is the composition of the market shifting? Which segments are growing in urgency and which ones are quietly going dormant?

SaaS went through a long season of growth where the numbers made the decisions. The market was forgiving. Capital was cheap. Distribution was the whole game, often fueled by aggressive lead generation for SaaS.

That is not the season we are in anymore.

The teams that will come out of this period stronger are not the ones with the biggest TAM slides, but those who measure impact and optimize B2B SaaS marketing ROI. They are the ones who actually understood what their TAM was telling them and built a motion around that reality instead of around the story they wanted to tell.

TAM is a mirror. Right now, SaaS might not love what it sees.

But looking away does not change the reflection.

SaaS Marketing Funnel

Challenges Marketers Face with Their SaaS Marketing Funnel: You’re Reading the Wrong Signals

Challenges Marketers Face with Their SaaS Marketing Funnel: You’re Reading the Wrong Signals

B2B deals go to pre-ranked vendors. Your SaaS marketing funnel isn’t broken-you’re measuring too late. Win by influencing the shortlist early.

Here’s a number that should stop you mid-scroll- 77%.

That’s the share of B2B deals that go to the vendor, which was already the buyer’s first choice- before the first sales conversation ever happens.

According to a 2025 Buyer Experience Report, 94% of buying groups rank their preferred vendors before contacting any of them. And in 77% of those cases, that preliminary ranking is where the deal ends up.

Read that again. Most deals are decided before your funnel even knows the buyer exists.

So, when you look at your MQL numbers, your SQL conversion rates, your pipeline velocity- what do they assert? You’re measuring the aftermath of a decision that already happened somewhere you can’t see.

You think your SaaS marketing funnel is broken. But it’s just you’re measuring the wrong metric — especially if you’re not aligning it with core SaaS metrics that reflect real buying behavior.

The SaaS Marketing Funnel is For Buyers That No Longer Exist

The LeadMQLSQL → Opportunity → Closed model made sense once. particularly in traditional B2B SaaS marketing frameworks.

Buyers needed vendors to help them understand the market, compare options, and build a shortlist. So, the moment someone raised their hand? They became a lead that was actually close to the start of their purchasing journey.

Marketing doesn’t operate this way in 2026.

Buying cycles have compressed from 11.3 months in 2024 to 10.1 months in 2025. Buyers are moving faster. However, they’re also making decisions earlier, and the shortlist they begin with on day one is the same shortlist most deals close with.

But the window to influence a decision hasn’t widened. It’s gotten smaller and harder to find.

Here’s what that means in practice.

The average MQL-to-SQL conversion rate across B2B SaaS sits between 13 to 21%. Most teams treat that gap as a handoff problem instead of evaluating their B2B SaaS funnel conversion benchmarks more critically. They focus on the same solution: better sequences, quicker follow-up, and tighter scoring.

However, if buyers are already 60% through their journey by the time they become an MQL, most of the drop-off doesn’t mean your process failed. The real problem is with the selection.

Those leads were never seriously evaluated for a purchase. They were curious. They were benchmarking. They’d already made a different choice; they just hadn’t told you yet.

No amount of SDR optimization fixes that. especially if your SaaS marketing lead scoring method isn’t aligned with how buyers actually shortlist vendors. The decision was made somewhere else.

Two Pieces of Advice: Your SaaS Marketing Funnel Is Running Simultaneously

Speed to lead matters. That’s real. Teams that respond to MQLs within an hour convert 53% quicker than teams that wait 24 hours, dropping to 17%. That’s a 36-point gap worth taking seriously.

But here’s what sits directly next to that finding: 94% of buyers have already ranked vendors before first contact. Over 80% of the time, buyers initiate that first contact themselves. SDR outreach plays “a minimal role in influencing the point of first contact.”

So, which is it? Does speed matter, or does the buyer decide before you even know you must respond?

Both are true. And that’s the problem.

Speed-to-lead improves your performance in a phase that succeeds the most critical phase. You’re getting faster at responding to a signal that appears once the shortlist is already set. It helps. But it doesn’t move the needle on the question that actually determines whether you win- did you make the shortlist at all?

Most SaaS marketing teams have no metric for that question, even though it should sit inside a clear SaaS marketing playbook. No stage in the funnel. No budget line. Nothing.

The Part of the Journey Your SaaS Marketing Funnel Can’t See

There’s something else worth sitting with.

Buyers have prior direct experience with 85% of the vendors on their shortlist before the buying journey even begins. Not awareness. Prior evaluations, i.e., demos they took, trials they ran, and tools they compared six months or two years ago.

The shortlist isn’t built during the active buying cycle. It’s shaped by long-term thought leadership in SaaS marketing that builds credibility over time. It’s built from everything that came before it. That’s why the dark funnel conversation mostly misses the point.

People perceive it as a tracking problem- “if only you had better attribution, you could see the Slack conversations and G2 reviews and LinkedIn threads that shaped the decision.” But better attribution doesn’t help you. The issue isn’t visibility. It’s that you’re not investing in the period when those conversations happen.

80% of vendor shortlist slots are filled on the first day of the buying journey. Buyers don’t research their way to a shortlist. They start with one. And the positioning work that earns you a spot on it happens months or years before a buyer has a budget and a deadline.

Most SaaS marketing teams put 70-80% of their budget toward buyers who are already in-market, often influenced by annual SaaS marketing budgets 2026 planning cycles. Only 5-6% of potential customers are actively buying at any given time. The other 94% are in the exact window when shortlists form and preferences harden. And most teams have almost nothing targeted to tackle that.

How Do You Track the Dark Side of Your SaaS Marketing Funnel?

None of this means MQL-to-SQL conversion doesn’t matter. It does. But it’s a secondary metric. It tells you how well you’re performing in a phase that follows the one that counts.

The teams gaining ground in 2025 and 2026 are asking a different first question a shift reflected in evolving SaaS marketing insights 2026. not “where does our funnel leak?” but “were we on the shortlist. And why or why not?”

That shift changes three things.

1. Win/loss analysis gets more honest.

The question isn’t just why a deal closed or churned in the first place.

It’s whether the company was in the initial consideration stage at all. If you’re consistently showing up late, i.e., getting contacted rather than being sought out, the funnel problem is upstream of everything you’re currently measuring.

Content becomes positioning infrastructure, not a lead machine. a principle central to the SaaS content marketing playbook. There’s a real difference.

2. Lead generation content is built to produce hand-raisers now.

Positioning infrastructure builds the familiarity that earns you a shortlist spot before the buying trigger exists. SEO-generated leads convert from MQL to SQL at 51%, reinforcing the long-term value of SEO for SaaS.

Not because organic search is mechanically better. Because a buyer who finds you through search has already been shaped by the positioning that made your content visible and credible in the first place.

The conversion advantage isn’t in the channel. It’s in what the channel reflects.

3. Sales-marketing alignment stops being about handoffs.

Buyers personally know someone at their preferred vendor before the buying journey begins, almost 70 to 90% of the time. This relationship wasn’t built during a nurture sequence. It existed before the buying trigger did. Teams that treat alignment as purely a post-MQL coordination problem are solving for the wrong moment.

The Stage That Decides Everything Else in Your SaaS Marketing Funnel

Your SaaS marketing funnel isn’t failing because of bad conversion rates or slow handoffs.

It’s failing because the stage that determines most outcomes, i.e., the quiet period, doesn’t exist anywhere in your funnel. That’s the latent parts- when shortlists form, preferences harden, and buyers rank vendors before reaching out.

There’s no metric for it. No owner. No budget — which is why forward-thinking teams rethink their entire B2B SaaS growth marketing strategy.

And so, every team pours energy into optimizing what happens after that moment. Quicker sequences. Better scoring. Tighter MQL definitions. Real improvements, all of them- but improvements to a phase that follows the one where the deal was already trending a certain direction.

The funnel starts measuring too late.

Until that changes, everything downstream is moving faster toward a finish line that already exists — regardless of how advanced your SaaS marketing tools may be.

Why NVIDIA's New Chip Matters More Than You Think

Why NVIDIA’s New Chip Matters More Than You Think

Why NVIDIA’s New Chip Matters More Than You Think

NVIDIA’s upcoming inference chip is more than a speed upgrade. It exposes a growing pressure point in AI economics and signals where the next real competition will unfold.

NVIDIA’s latest chip plans are easy to slot into the usual narrative. Faster hardware. Bigger benchmarks. Another GTC headline.

But this one hits differently.

The focus this time is inference. That’s the part of AI most people actually interact with. Every prompt answered. Every generated line of code. Every AI-powered search result. Training may win headlines, but inference carries the daily load.

And that load is getting heavy.

As models grow more capable, they also grow more demanding. Tasks like reasoning through complex instructions or generating structured software are not light lifts. Companies building on top of large models have quietly run into friction. Latency creeps in. Costs balloon. Infrastructure teams start having uncomfortable conversations.

That is where this chip fits.

It isn’t about chasing bragging rights. It is about tightening the gap between model capability and usable product performance. When responses slow down or compute bills spike, it doesn’t matter how advanced the model is. Users notice the lag. CFOs notice the spend.

There is another layer here. Reports suggest NVIDIA is drawing from newer architectural approaches, including technology tied to Groq. That signals something important. The era of relying on GPU upgrades alone may be fading. Workloads are getting too specific. Too demanding. Too nuanced.

Hardware is starting to specialize.

For tech leaders, this is less about silicon and more about leverage. Inference efficiency shapes margins. It shapes user experience. It shapes how ambitious you can be with your product roadmap.

AI doesn’t only scale with model size. It scales on how efficiently you can serve it. And right now, serving is where the real pressure sits.

OpenAI

OpenAI Shakes Hands with the Trump Administration; Offers its AI for Intricate US Military Networks

OpenAI Shakes Hands with the Trump Administration; Offers its AI for Intricate US Military Networks

There are two specks to the OpenAI-Pentagon narrative. One overly political and one highly ill-judged- product-centric.

There are two easy readings of the OpenAI–Pentagon story.

One turns it into pure politics. The other reduces it to market expansion and enterprise revenue.

Both are incomplete.

It’s about military integration. And military integration is about security.

When a frontier model enters defense workflows, it does not sit there answering casual prompts. It becomes the crux of intelligence analysis, logistics modeling, cybersecurity simulations, and even decision-support systems.

Even if it is not for operating weapons, AI will impact workflows that affect real-world operations.

That raises serious technical questions.

How are models sandboxed in classified environments?

What happens when sensitive data flows into training feedback loops?

Can adversarial actors manipulate outputs through prompt injection or poisoned inputs?

Where does human oversight actually sit in the chain of command?

These are not abstract concerns. Military systems are prime targets for cyber intrusion. Generative models introduce new attack surfaces. One can easily exploit retrieval systems. Fine-tuned instances can drift from baseline behavior. If a model is used to summarize intelligence or simulate threat scenarios, small reasoning errors compound quickly.

At the same time, defense environments are often more disciplined than commercial ones. They demand audit logs. They demand access controls. They demand strict validation layers. In theory, that pressure should improve robustness.

But theory is not assurance.

For tech leaders, the real issue is this: when AI becomes embedded in national security infrastructure, the tolerance for ambiguity drops to zero. Safety documentation cannot be marketing copy. Guardrails cannot be symbolic.

The OpenAI–Pentagon agreement forces the industry to confront a harder truth. Frontier AI is no longer just productivity software. It is infrastructure. And infrastructure demands security standards that match the stakes.

That’s the real story.

The Future of Retail Media And Its Impact On Advertising

The Future of Retail Media And Its Impact On Advertising

The Future of Retail Media And Its Impact On Advertising

Why the most consequential shift in advertising right now has nothing to do with where you are placing ads, and everything to do with whether you understood your buyer before you bought the inventory.

Here is a number that should reframe how you think about B2B advertising in 2026.

US advertisers will spend $69.33 billion on retail media this year. Up from $58.79 billion in 2025. A channel that barely registered as a budget line five years ago now accounts for nearly 18% of all US digital ad spending. Retail media is not a niche. It is not a test-and-learn experiment. It is the third pillar of digital advertising, sitting alongside search and social, and it is growing faster than either of them, a shift already explored in depth in our breakdown of retail media advertising and adtech companies.

Retail media will outpace social media networks entirely by 2028. That is the current trajectory.

And most B2B advertisers are watching it happen from the outside.

Not because they have evaluated the channel and found it irrelevant. Because they have not evaluated it at all. The assumption, running quietly beneath most B2B marketing plans, is that retail media is a CPG problem. A Procter and Gamble problem. A sponsored product on Amazon has a problem.

That assumption is now expensive.

What Retail Media Actually Is, Before We Talk About What It Means

The definition has stretched. A lot.

Retail media started as advertising inventory owned by retailers, placed on their own platforms, targeted using their own purchase data. Amazon sells sponsored product placements to brands that want to appear when someone searches for a category. That was the original model.

It worked because it had something other digital channels did not: first-party transactional data. Not behavioral signals. Not modeled audiences. Actual purchase history. A company knows that this specific account, at this specific organization, bought a particular category of product three times in the past year. That is a fundamentally different quality of signal than what Google or Meta ever had.

But the definition has since expanded into territory that directly concerns B2B advertisers. Retail media now includes off-site advertising, where retailers take their first-party audience data and use it to place ads across third-party publisher websites and programmatic exchanges. a structure closely aligned with modern programmatic advertising strategies. It includes connected television integrations. It includes in-store digital signage with closed-loop attribution. It includes the entire concept of a retail media network, or RMN, which is any company that monetizes its audience data and owned inventory to serve advertisers.

There are now 277 retail media networks operating globally as of late 2025. That number is expected to grow. Payment platforms are turning transaction data into advertising infrastructure. Airlines, hotels, and financial services companies are all building media networks on the back of their purchase and behavioral data.

That last part is the B2B entry point.

When a platform that knows the procurement behavior of hundreds of thousands of businesses starts offering advertising inventory targeted to those buyers, that is not a CPG problem anymore. That is your problem. That is your opportunity, or your competitor’s.

The Fragmentation Problem, Which Is Also the Opportunity

Retail media has a fragmentation issue. It is severe enough that analysts talk about it constantly, and it is worth understanding technically before drawing the strategic conclusions.

The average organization is currently working with six retail media networks. a level of ecosystem complexity similar to what brands face when navigating multiple programmatic advertising platforms. That number is projected to reach eleven by 2026. Each network runs its own data infrastructure, its own identity system, its own reporting logic, its own measurement standards. Brands managing campaigns across even five or six of these networks are, in practice, operating in five or six different systems with five or six different versions of what ROI means, a challenge that mirrors broader B2B SaaS marketing ROI measurement issues.

Amazon and Walmart alone will capture 89% of incremental retail media spend in 2026. The remaining 11% is distributed across 275 other networks. Which means the ecosystem is simultaneously dominated by two giants and violently fragmented everywhere else.

The measurement problem is compounding this. Thirty-six percent of marketers cite difficulty proving investment incrementality as a reason they might reduce retail media spend. Another 32% point to lower ROI compared to other channels. These are not complaints about a channel that does not work. There are complaints about a channel where the infrastructure for proving that it works is still being built. Unified measurement across networks, where you can see exposed versus unexposed lift across all your activations in a single view, is only now becoming available at a small number of networks.

Here is the strategic point buried inside all of this technical complexity: the brands that figure out measurement and cross-network attribution now, while most competitors are still confused, will have a data advantage that compounds. In retail media, clarity about what is actually driving results is a competitive asset.

In B2B advertising, that is doubly true. Because the buyer journey you are trying to influence is not a single transaction, it reflects the complexity of modern B2B lead generation strategies. It is a nine-to-twelve-month committee process. The measurement problem in retail media is difficult. The measurement problem in B2B is harder. Any advertiser who gets serious about closed-loop attribution in retail media will have developed capabilities that transfer directly.

The B2B Buyer’s Attention Is the Most Fragmented Thing in Advertising

US B2B digital ad spending reached just over $20 billion in 2025. That number sounds large. Divided across the actual problem it is trying to solve, it looks different.

The average B2B purchase now involves around ten people. Seventy-two percent of B2B purchases involve high-complexity buying groups spanning multiple functions, including IT, operations, finance, and end users. These people are not in the same room. They are not on the same channel. They are not reading the same content or consuming the same media. They move at different speeds and have different concerns. And they are, individually, completing most of their research independently before anyone on the selling side gets involved.

Eighty-three percent of buyers have fully or mostly defined their purchase requirements before speaking with sales. Ninety-four percent are using large language models at some point in their buying process. Seventy-two percent encountered Google’s AI Overviews during their research.

The buying committee’s attention is not fragmented in the way a consumer’s attention is fragmented. A consumer flips between TikTok, YouTube, and email. A B2B buying committee member is doing something more specific and harder to reach: they are conducting private research, consulting peers outside their organization, running their own evaluations, and arriving at conclusions that vendors never get to see or influence. Formal buying committees are giving way to what analysts are now calling fluid networks of influence, where internal stakeholders, external peers, AI agents, and digital communities all shape decisions long before sellers are contacted.

You cannot interrupt your way into that process.

That sentence is the entire strategic case for moving from reactive to proactive B2B advertising.

Reactive Advertising Is Chasing the Buyer After the Decision Has Been Made

Most B2B advertising is reactive, relying heavily on intent triggers and traditional demand generation vs lead generation models. It responds to signals. Someone searches for a keyword: serve them an ad. Someone visits the pricing page: trigger a retargeting sequence. Someone engages with a competitor’s content: launch an ABM campaign targeting their account.

These tactics are not wrong. They work in a narrow sense. They capture demand that already exists. They remind a buyer who already has your brand in their consideration set that you are still there.

But they do nothing about the buyer who has never heard of you. They do nothing about the eight other members of the buying committee who did not trigger the signal. They do nothing about the vendor preference that was formed six months before anyone searched for a keyword.

The Demand Works analysis of B2B buyer intent trends for 2026 frames it clearly: the buyer journey is a series of fragmented micro-moments, and success requires orchestrating immediate, context-aware responses to those moments before they happen. Not after. The strategy that wins is the one that anticipates need rather than reacts to expressed intent.

This is where retail media’s architecture offers a model that B2B advertisers should study, even if the direct application takes a different form.

Retail media works because it places a brand in front of a buyer at the moment of highest commercial intent, using data that describes the buyer’s actual behavior rather than their modeled profile. The sponsored product appears when someone is actively looking for the category. The off-site ad follows a shopper who has demonstrated purchase intent within a specific category across the open web. The in-store digital screen reaches the buyer at the physical point of decision.

The B2B equivalent of this is not a sponsored listing on Amazon. It is being present, with a message that actually fits the buyer’s context, at every point in the long pre-purchase research process that the buying committee is conducting without you.

Seventy-five percent of buyers turn to peers when creating a vendor shortlist. Reinforcing the power of thought leadership in SaaS marketing. Top-of-mind awareness determines whether a vendor is considered at all. Being on the shortlist is the outcome of months of low-level, ambient presence that most B2B advertising plans never account for.

Partnerships Are the Infrastructure of Proactive Advertising

There is a lesson in how the best retail media networks actually work that B2B advertisers miss because they read the surface feature and not the underlying structure.

A retail media network’s value is not the inventory. Amazon has plenty of inventory. The value is the data that makes the inventory addressable. First-party purchase history. Behavioral signals from actual commercial activity. The ability to identify, with precision, which accounts are in an active buying cycle and what they are buying.

No B2B advertiser can build that infrastructure alone. The data is distributed. The buyer’s research happens across dozens of platforms and channels. Their intent signals are generated in places the advertiser has no access to.

This is why partnerships are not a nice-to-have in proactive B2B advertising. They are the mechanism.

The first category of partnership is data. Intent data providers aggregate behavioral signals from across the web, including content consumption, research activity, and engagement patterns at the account level, forming the backbone of behavioral targeting for high-quality leads. They build the dynamic account-level intent profiles that modern B2B buying research describes, consolidating signals from multiple stakeholders within an organization into a picture of the buying committee’s collective needs. This is the retail media first-party data advantage, reconstructed for the B2B context.

The second category is channel. Most B2B buying committees include Millennial and Gen Z members who are digitally native and carry consumer-grade expectations into business purchasing decisions. They are not waiting to be reached by a cold email sequence. They are on LinkedIn, which took 9% of all B2B digital ad spend to $4.59 billion in revenue in 2025, alongside broader social media lead generation strategies. They are watching YouTube. They are in Slack communities, private Discords, and specialized forums. They are consuming creator-led content from individual voices they trust, which is why 73% of leaders in the Edelman study said thought leadership was a more trustworthy basis for assessing vendor capabilities than company marketing.

A proactive advertising strategy is one that maps where each member of the target buying committee spends their attention, aligning closely with structured B2B SaaS growth marketing strategy frameworks. before they are in an active buying cycle, and builds presence at each of those points. Not with generic brand advertising. With content and messages that are specifically calibrated to that member’s role, concern, and stage of awareness.

The third category is the one most B2B advertisers skip entirely: partnerships with the organizations and voices that already have credibility with your target buyers. Retail media’s influencer integration problem, where creator and influencer campaigns remain loosely connected to the broader ecosystem without standardized booking or attribution, maps directly onto B2B’s creator and thought leadership gap. The brands that have figured out how to make individual voices synonymous with the category they sell into do not struggle for attention. Their buyers come to them already believing the value proposition, because they heard it from someone they trusted.

Understanding the Buyer Is Not a Research Project. It Is an Ongoing Practice.

Here is where most B2B advertising plans break down.

They are built on research that was conducted once, at the start of the year, by a team that read a few industry reports and spoke to three existing customers. The ICP was defined. The personas were documented. The messaging framework was finalized. And then the year proceeded, with every campaign running against that static picture of the buyer.

Meanwhile, the actual buyers changed their problem statements an average of 3.2 times during complex purchases in 2025. Fifty-four percent of buying groups are actively evolving their decision-making models. The point of first contact in the sales process moved forward by six to seven weeks in a single year, as economic pressure pushed buyers to engage vendors earlier while still arriving with fully formed requirements.

The retail media model has something useful to say about this, too. The reason RMNs with closed-loop measurement outperform other channels is not that they have better targeting. It is that they have real feedback. They know, from actual transaction data, whether an ad exposure translated into a purchase. That feedback shapes the next campaign. The loop closes, and the system learns.

B2B advertisers who want to move from reactive to proactive need the same loop. Not annual persona refreshes. A continuous feedback mechanism that pulls in what the sales team is hearing, what the support team is seeing, and what the content that is performing is telling you about what buyers are actually worried about right now.

Elsa Dithmer of Auvik puts the content side of this cleanly: high-value content should provide actionable insights that empower buyers. When it is well-optimized, highly relevant, and consistently delivers value, it establishes authority, nurtures prospects, and ultimately accelerates pipeline velocity. The operative word is consistently. A proactive advertising strategy is not a campaign. It is a practice.

The Practical Shape of What This Looks Like

Let’s be concrete.

A proactive B2B advertising program has three layers running simultaneously, each addressing a different part of the buying committee at a different stage of awareness.

The first layer is the ambient authority building. This is the content, the creator partnerships, the thought leadership that runs continuously in the channels where your target buying committee members spend time, whether or not they are in an active buying cycle. It is not optimized for clicks or form submissions. It is optimized for mindshare, for becoming the name that comes up when someone in the committee mentions the category in a peer conversation. This layer is the one that puts you on the shortlist before the search starts.

The second layer is intent-activated precision. This is where the data partnerships matter. When account-level intent signals indicate that a target account is entering an active research phase, the advertising activates at a higher level of specificity. Messages tailored to the committee member’s role. Content that matches the problem framing that purchase research suggests is the primary concern. Coordinated across channels so that the CFO, the CTO, and the operations leader are all seeing relevant material at the same time, even if the material is different for each of them.

The third layer is conversion-stage proof. This is where the reactive advertising that most B2B teams are already doing actually belongs. Retargeting, case studies, competitor comparisons, and ROI calculators. The mechanisms for closing the deal with a buyer who is already on the shortlist. This layer is no less important than the first two. It is simply downstream from them. The mistake most teams make is running only this layer and wondering why the shortlist was already set before they showed up.

Retail media’s closed-loop measurement infrastructure is the model for what holds all three layers together. You need to know which exposures in the first layer are showing up as account-level intent signals in the second, which requires a robust lead tracking system. You need to know which intent-activated campaigns are translating into shortlist inclusions. You need to know, at the account level, whether the investment is working. This requires data partnerships, clean room arrangements with publishers, and the discipline to build attribution models that account for a nine-month buying cycle rather than a seven-day click window.

It is more complex than reactive advertising. It is also the only advertising that reaches buyers before their minds are made up.

The Underlying Shift That Makes All of This Urgent

Gartner’s projection is that by 2027, 95% of B2B buying journeys will begin in a language model. Eighty-nine percent of buyers already name generative AI as one of their most important sources across every phase of purchase.

This changes the reactive advertising equation further. If the buyer is beginning their research in a language model, keyword-triggered advertising does not reach them at the start of the process. It reaches them later, if at all. The language model does not serve banner ads. It synthesizes information from sources it considers authoritative. Which means the B2B advertiser’s job is now, in part, to be the authoritative source that the language model cites, a challenge deeply connected to SEO for SaaS.

That is an SEO and content authority problem as much as an advertising problem. And it is one more reason why the first layer of the proactive strategy, the ambient authority building, is not optional. It is the infrastructure that makes everything else work in a world where the buyer’s first twenty queries about your category go to a chatbot.

The brands that have spent two years consistently producing genuinely useful material about the problems their buyers face, in their own voice, with their own perspective, will be cited. reflecting the long-term discipline outlined in the SaaS content marketing playbook. The brands that produced 85% AI-scored content optimized for keyword clusters will not.

A Thought to Leave With

Retail media reached $165 billion globally in 2026 because it solved a real problem. Advertisers needed to reach buyers with high purchase intent, using data that actually described commercial behavior, in a way that connected ad exposure to sales outcomes.

The B2B advertising problem is structurally identical. Reach the right members of the buying committee. Use data that describes actual commercial behavior and intent. Connect the advertising investment to revenue outcomes in a way the CFO will believe.

The tools exist. The data partnerships are available. The channel mix, from LinkedIn to creator networks to intent-activated programmatic to the emerging retail media networks that are building B2B-relevant inventory, is more sophisticated than it has ever been.

What does not exist, in most B2B organizations, is the will to build the proactive infrastructure before the reactive one fails. Most teams will wait until the pipeline number forces the conversation. Until the marketing-attributed opportunities dry up. Until the sales team starts complaining that they are showing up to deals where the decision has already been made.

By then, the shortlist will have been set for six months.

The buyers who will call you in the second half of 2026 are forming their opinions right now. They are reading content that either includes your voice or does not. They are in conversations with peers who either mention your name or do not. They are doing research in language models that either cite your authority or return someone else’s.

Google Strikes Multibillion-Dollar AI Chip Deal with Meta

Google Strikes Multibillion-Dollar AI Chip Deal with Meta

Google Strikes Multibillion-Dollar AI Chip Deal with Meta

The chain of partnerships keeps on increasing. Is this a collaboration or a consolidation of power?

The surface reading of the Google-Meta chip deal is straightforward. Meta has agreed to rent Google’s tensor processing units through Google Cloud to train and run its next-generation large language models, in a multi-year agreement worth billions of dollars. Google gets a major enterprise customer. Meta gets another compute supplier. Clean transaction. Except nothing about this is clean.

Meta’s AI infrastructure spending is projected to reach $135 billion in 2026. The company has 30 data centres planned, 26 of them in the United States. That is not a company with a compute problem. That is a company executing a deliberate strategy to ensure no single supplier can hold it hostage. This week alone, Meta has deals running simultaneously with Nvidia, AMD, and now Google. Morningstar analysts are calling it a multipronged silicon strategy. We would call it something blunter: leverage, bought in advance and at scale.

The timing is not incidental. Meta has been running into serious problems with the AI chips it is designing internally, scrapping its most advanced in-house training chip last week. When your own silicon program hits a wall, you move fast, and you move wide. The Google deal is partly a diversification. It is also insurance.

For Google, the stakes are different but equally structural. Google first developed TPUs more than a decade ago for internal workloads. The Meta deal represents a major expansion of its TPU commercialisation strategy, which previously kept the chips largely inside Google’s own infrastructure. Google is now forming a joint venture to lease TPUs to other AI customers, with some Cloud executives estimating the expansion could capture as much as 10 percent of Nvidia’s annual revenue. In October, Anthropic signed a deal for access to up to one million TPUs. Meta follows. The pattern is becoming a market.

So is this a consolidation of power, an expansion of it, or something that resembles creative collaboration? We think the honest answer is: it is none of those things individually. It is two dominant players using each other to reduce their dependence on a third dominant player. NVIDIA sits at the centre of the AI race in a way that makes every other company in the ecosystem uncomfortable, and the deals being signed this week are the market’s response to that discomfort.

The partnership economy does not always produce partners. Sometimes it produces mutual hedges dressed in press release language. This is one of those.