B2B Buying Groups

A Glimpse into the B2B Buying Groups: Who’s Buying and How

A Glimpse into the B2B Buying Groups: Who’s Buying and How

B2B deals fail because sellers treat a committee like a single buyer- even when your product is the best in the market. So then why are sales and marketing teams still working around it?

The Single-Buyer Myth Is Killing B2B Deals

Think about the last complex B2B deal your team lost.

Chances are, it wasn’t a product problem. The demo went well. Pricing was competitive. The Champion loved it. And then, somewhere in the final stretch, the whole thing went quiet- a few follow-ups. No response. Deal closed-lost.

What actually happened is pretty predictable once you understand how B2B purchases genuinely work. There was never a single buyer. There was a group, i.e., anywhere from five to sixteen people, depending on the size of the organization- each with their own priorities, their own concerns, and their own version of what a good decision looks like.

Nobody aligned them. So, they didn’t align.

That’s the B2B buying process problem in plain English.

Gartner research puts a hard number on it: 74% of B2B buyer teams experience unhealthy conflict during the decision process. Not a healthy debate. Conflict that stalls consensus, drags out timelines, and kills deals that should have closed. And the uncomfortable part for most sales teams is that the conflict isn’t happening in your pipeline view. It’s happening in a Slack thread or a meeting you were never invited to.

Understanding what a buying group actually looks like means grasping who’s in it, what each person cares about, and where the friction lives. Building that clarity is central to engaging the modern B2B buyer effectively.

Who’s Actually in the Room

Forrester breaks buying group members into five distinct roles. Not job titles. Roles- because the same person can wear different hats depending on the organization and the purchase.

1. The Champion

The Champion is the person whom most salespeople spend the majority of their time around.

They’re the internal advocate, the person who believes in the solution and has enough credibility to bring others along. Good champions are decisive, well-connected, and genuinely invested in making the change happen.

But here’s the thing sellers often miss: a champion’s ability to move a deal forward is directly tied to how well they’ve been equipped to sell internally.

If you haven’t given your Champion the language, the business case, and the answers to the objections they’ll face from finance and IT- you’ve essentially sent them into a knife fight with a spoon.

2. The Decision-Maker

The Decision-Maker holds the final call.

Usually, a senior executive or budget owner. They are involved heavily during the selection phase, scrutinizing financial value and business fit. But they also show up earlier than people expect, ensuring the evaluation criteria align with organizational priorities.

What decision-makers are not is passive. Several SDRs treat them like a rubber stamp at the end of the process. That’s a mistake. By the time a decision-maker is formally reviewing a vendor, they’ve often already formed an opinion based on customer references and past experience with the selling organization.

3. The Influencer

The Influencer is the role that trips teams up most often.

Internal influencers, i.e., IT security, legal, or finance, have a stake in the purchase even if they’ll never use the product. They’re vetting risk, not value. And external influencers, i.e., industry analysts, peer networks, and LinkedIn voices, shape how buyers think about the problem space before a vendor is even considered.

Trying to engage influencers the same way you engage champions doesn’t work. They’re not looking for product information. They want implementation clarity, pricing transparency, and evidence backed by buyer intent data and real-world experience.

4. The End User

The User is whoever lives with the decision day-to-day.

They care about one thing above everything else: does this actually make their job better? They’re most active in the middle and late stages of the buying process- product trials, demos, and feedback sessions. And they matter beyond the initial sale.

Users drive renewal conversations. A product that gets adopted enthusiastically performs very differently at contract renewal time than one that gets quietly tolerated.

5. The Ratifier

The Ratifier tends to show up at the end, i.e., finance, procurement, and legal- to settle terms, conditions, and pricing. They’re not evaluating the product’s strategic merit. They’re evaluating cost and risk. But their influence can be felt much earlier, especially when deal size puts a purchase on the CFO’s radar before the evaluation stage even ends.

Five roles. And in any given deal, they’re each doing their own research, forming their own opinions, and carrying their own objections- often without talking to each other.

The B2B Buying Journey These Groups Actually Take- and It’s Never Linear

Here’s what makes this harder. The journey they’re on isn’t straightforward.

Gartner’s research describes the B2B buying journey as a set of distinct buying jobs that groups cycle through repeatedly- problem identification, solution exploration, requirements building, and supplier selection. It’s not a clean funnel moving from awareness to decision.

Most buyers revisit at least one stage before they close. They loop back. They re-examine requirements after a competitor demo. They raise a concern in the selection phase that should have been mentioned during exploration.

The numbers around self-directed research make this even more stark, especially as intent signals increasingly shape how buyers evaluate vendors independently.

The Nuance Within

Buyers now spend only 17% of their total purchasing time in direct contact with vendors. The other 83% happens without you in the room- independent research, internal deliberation, peer conversations, analyst reports. By the time a buying group member gets on a call with your sales team, they’ve already formed strong opinions, surfaced objections internally, and in many cases, quietly eliminated vendors from consideration.

67% of B2B buyers now say they prefer a rep-free experience altogether. That’s not a fringe preference but the majority. It also highlights why content creation now plays a larger role in influencing buying decisions than direct sales conversations.

It creates a specific problem for teams still running individual-focused outreach.

If you’re personalizing content to each stakeholder in isolation, i.e., giving the Champion messaging about transformation, the CFO messaging about ROI, the IT evaluator messaging about security, you’re not helping the group reach consensus. You’re giving each member ammunition to dig into their own position.

Gartner found that individual-level relevance actually creates a 59% negative impact on buying group consensus. The confirmation bias it triggers makes stakeholders less likely to move toward a shared direction.

What moves groups forward is content and messaging anchored to the shared challenge, the organizational problem that brought them all into the same room in the first place. This is where organizational buy-in becomes critical to building consensus across stakeholders.According to Gartner, around 99% of B2B purchases are driven by organizational change.

Not individual pain. Not departmental preference.

A shift that’s strategic, operational, or structural is one that the whole group is trying to navigate. Sellers who anchor their narrative to that shared context build consensus. Sellers who fragment their messaging by individual role undermine it.

What Selling to a B2B Buying Group Actually Requires

The tactical shift isn’t complicated. Executing it consistently is.

First, map the group early.

as part of a stronger lead generation strategy that identifies all key stakeholders before the deal progresses. Don’t build an account strategy around a single contact. Before a deal is even qualified, figure out who will be involved in the decision, what function they sit in, and what phase of the buying journey they’re most active in.

The Champion can help you build this map- but only if you ask.

Second, equip your Champion to carry the internal conversation you’ll never be part of.

Most critical buying group discussions happen without a vendor in the room. Your Champion is your proxy. Give them the business case in language that influences the decision-maker. Offer them answers to the objections the Ratifier could raise. Give them the implementation data the influencer needs.

Don’t assume they’ll figure it out.

Third, build for group relevance, not individual relevance.

by aligning your content marketing strategy with the broader organizational challenge buyers are trying to solve. Case studies, ROI frameworks, competitive comparisons- these should speak to the organizational problem, not a single persona’s concerns. The buying group reads each other’s signals.

Content that helps them understand each other’s perspectives and validate the same direction has a measurably better shot at driving consensus than content siloed by job function.

Fourth, respect the self-directed journey.

73% of B2B buyers actively avoid vendors who send irrelevant outreach. Pushing harder when a group goes quiet is one of the fastest ways to lose a deal that was still alive.

A buying group that goes dark is usually in an internal phase, deliberating, resolving conflict, and building alignment. During this stage, email marketing strategies focused on relevance and timing are far more effective than aggressive follow-ups.

The Shift in B2B Buying Nobody Has Fully Reckoned With

The B2B buying group isn’t a new concept. What’s new is the degree of difficulty.

Buying groups are getting larger, more cross-functional, and more informed before the first vendor conversation. That shift is reshaping the entire media buying process for B2B organizations. And increasingly, individual buying group members are using AI tools to accelerate their own independent research, which means the information asymmetry that sales teams relied on is gone.

Buyers often know more about a vendor’s weaknesses than the rep covering the account.

The teams pulling ahead aren’t the ones with better pitches. They’re the ones who’ve genuinely redesigned how they engage, i.e., moving from individual lead pursuit to buying group strategy, from volume outreach to high-relevance content, from pipeline management to consensus facilitation.

The B2B buying group was always the real unit of purchase. Most sales and marketing teams are just now starting to act like it.

Snowflake

OneTrust and Snowflake Partner Up to Make Consent Signals More Actionable

OneTrust and Snowflake Partner Up to Make Consent Signals More Actionable

Snowflake and OneTrust are baking consent into data sharing as AI makes privacy mistakes far more dangerous for brands.

For a long time, “user consent” in marketing basically meant one thing: annoying cookie banners that everyone clicked through without reading.

That system was always shaky, but AI is exposing just how messy it really is.

Snowflake and OneTrust just announced a partnership that allows companies to carry user consent signals directly into Snowflake’s data collaboration environment. Sounds technical. Because it is, but the bigger story here is much simpler: companies are starting to panic about what happens when AI trains on data it was never supposed to touch.

And that panic is justified.

Before AI exploded, ineffectual data governance was primarily a compliance headache. Maybe regulators fined you. Maybe legal got involved. Maybe consumers got angry for a few days online. But gen AI completely changes the scale of the problem.

Once questionable data enters an AI system, pulling it back out is not easy. In some cases, companies may have to retrain or even roll back entire models. That is expensive, messy, and terrible for trust. OneTrust’s strategy chief, Ojas Rege, basically admitted as much, saying rollback may be the “only remedy” in certain situations.

So now the industry is trying to solve a problem it probably should have addressed years ago: ensuring consent remains attached to the data wherever it goes.

That matters because modern marketing data travels across several points. Between brands, ad platforms, analytics systems, clean rooms, AI tools, and external partners, information is constantly floating. Somewhere along the way, the original permissions often become vague or disconnected.

AI makes that vagueness dangerous.

And honestly, this feels like the start of a much larger shift. Companies spent the last two years obsessing over how much data they could collect for AI. Now they are realizing the more important question is whether they are actually allowed to use it.

That changes the conversation entirely.

Because in the AI era, ineffective consent management is no longer just sloppy marketing. It is a business risk.

Substack

Substack Loses Brownie Points as Writers Move to Ghost and Beehiiv

Substack Loses Brownie Points as Writers Move to Ghost and Beehiiv

More creators are leaving Substack for Ghost and Beehiiv as frustrations grow over growth and platform control.

Substack felt like the future of media- for a little while.

Writers could leave collapsing newsrooms, build direct audiences, charge subscriptions, and finally “own” their work. It looked clean, independent, even rebellious. But now a growing number of creators are realizing something uncomfortable: they may not have owned as much as they thought.

According to a new report from The Verge, more writers are leaving Substack for rivals like Ghost and Beehiiv, frustrated by rising costs, platform dependence, and Substack’s increasing shift toward becoming a social network.

And honestly, this feels like a very familiar internet story.

Platforms usually begin by empowering creators. Then they grow. Then they optimize for engagement. Then, creators slowly realize the platform’s priorities are no longer actually aligned with theirs.

Substack’s biggest issue is what many writers now call the “Substack tax.”

The company takes a 10 percent cut of subscription revenue, which sounds manageable until newsletters scale. And for large publications, that can turn into tens or even hundreds of thousands of dollars a year.

That is why its competitors are suddenly gaining momentum. They charge flatter fees, offer more customization, and offer creators a stronger sense of ownership over their audience and brand.

Because that is the real tension underneath all of this: creators no longer merely want monetization. They want control.

And Substack has begun to feel like it wants creators inside its ecosystem rather than building independent media businesses externally. The company has leaned heavily into all social nitty-gritty creators were running away from- algorithmic discovery, Notes, video features, and even social-style engagement systems. That helps Substack grow as a platform, but not every writer wants to become a part-time content creator feeding another recommendation engine.

There is also something bigger happening here. The internet is moving away from giant centralized platforms again. Slowly but noticeably.

Writers watched what happened to creators on Facebook, YouTube, Instagram, and even Twitter. Algorithms changed. The reach collapsed. Businesses disappeared overnight. So now many newsletter publishers are asking a smarter question earlier: if your audience lives on someone else’s platform, do you really own it at all?

Substack helped revive independent publishing. That part is real.

But creators increasingly seem to be treating it less like a permanent home and more like a launchpad they eventually plan to leave.

NVIDIA

NVIDIA is Financing the Entire Gold Rush, Invests Billions in IREN

NVIDIA is Financing the Entire Gold Rush, Invests Billions in IREN

NVIDIA’s $2.1 billion IREN deal shows the AI boom is no longer just about chips- it’s now a massive infrastructure and finance race.

NVIDIA has crossed an invisible line in the AI boom. It is no longer just the company selling shovels during a gold rush. Increasingly, it is also financing the mines.

NVIDIA announced an investment deal of up to $2.1 billion into data centre operator IREN as part of a broader partnership to deploy as much as 5 gigawatts of AI infrastructure. That is an extraordinary number.

For context, 5 gigawatts is the scale of infrastructure usually associated with national energy planning, not a single technology partnership.

And that’s precisely the point.

The AI industry is entering a new phase where software innovation is no longer the only bottleneck. It is electricity, cooling, fibre optics, land, financing, and raw compute capacity. NVIDIA understands this better than anyone, which is why the company is rapidly evolving from chipmaker into infrastructure kingmaker.

The most interesting part of the IREN deal is not even the money. NVIDIA secured rights to buy up to 30 million IREN shares at $70/piece across five years. In other words, NVIDIA is embedding itself financially inside the ecosystem it powers. The company increasingly profits not only when customers buy GPUs, but when the entire AI infrastructure economy expands.

That is a dangerous level of gravity for one company to possess.

The AI market already revolves around NVIDIA’s chips.

Now the company is moving deeper into datacentres, cloud infrastructure, optics, and even factory construction. Just this week, NVIDIA also committed billions toward expanding fibre-optic manufacturing with Corning. Meanwhile, companies like CoreWeave, IREN, and Nebius are effectively becoming extensions of NVIDIA’s ecosystem.

It looks less like a healthy technology market and more like the emergence of an AI industrial complex.

Of course, investors love it because demand still appears endless. Big tech is projected to spend more than $700 billion on AI infrastructure this year alone. But history has punished industries that assume demand curves only move upward.

The irony is that NVIDIA may now be too important for the AI economy’s stability. When one company supplies the chips, funds the infrastructure, shapes the architecture, and influences the financing, the entire market inherits the same concentration risk.

And concentration risk has a long history of looking brilliant right before it becomes terrifying.

AI

Big Tech’s AI Obsession Is Now Distorting the Global RAM Market

Big Tech’s AI Obsession Is Now Distorting the Global RAM Market

Big Tech firms are scrambling for RAM to fuel AI growth- and the rest of the tech industry may end up paying the price.

The AI boom has officially entered its “resource panic” phase.

Not software. Not models. Not chatbots. RAM.

According to a recent report covered by The Verge, major tech companies are now offering unusually generous deals and incentives to secure memory chips for AI infrastructure. Translation? The businesses building AI systems are getting nervous that there will not be enough memory to go around.

And honestly, this says a lot about where the AI industry is actually heading right now.

For all the futuristic marketing surrounding artificial intelligence, the entire framework still depends on very physical, limited hardware. AI models are memory-hungry monsters. Training them takes enormous amounts of DRAM and high-bandwidth memory, and running them at scale takes even more.

Every chatbot response, AI-generated image, or automated workflow sits on top of warehouses full of servers burning through memory at exponential rates.

The problem is that only a handful of companies really control the global RAM market- mainly Samsung, SK Hynix, and Micron. So, everybody else starts feeling the squeeze when trillion-dollar tech giants start aggressively locking in supply.

That “everybody else” includes consumers.

Laptop prices rise. Gaming hardware gets more expensive. Smartphone manufacturers start cutting corners or increasing prices. The AI race you never asked to participate in quietly affects the price of your next device.

It’s interesting how quickly the industry has shifted from optimism to spearheaded competition. AI companies were talking about possibilities over a year ago. Now they are fighting over infrastructure like countries fighting over oil.

And that changes the conversation completely.

Because this is no longer just a software revolution but an industrial one. Those rich enough to secure the raw material before their competitors do will be the ultimate winners.

The AI boom is starting to become more of a global resource grab.

And RAM is one of the first battlegrounds.

Data enrichment

Filling in the Contextual Gaps with Data Enrichment

Filling in the Contextual Gaps with Data Enrichment

Your CRM has thousands of records, but half are incomplete and a quarter are outdated. Data enrichment closes that gap- and in 2026, it’s the difference between a revenue engine that runs and one that quietly bleeds money.

Most data problems don’t announce themselves.

They show up sideways. A sales rep pitches the wrong product to a company that already uses a direct competitor. A marketing campaign misses badly because the audience was built on job titles from eighteen months ago. A customer success manager walks into a renewal call with no idea whether the account is thriving or already talking to someone else.

Same root cause, every time. The data was there. It just wasn’t complete enough to actually use.

That’s the data enrichment problem. And most teams don’t realize they have it until they’re already paying for it.

So, what is data enrichment?

It’s the process of taking the data you already have and filling in the context that’s missing. You pull in additional information- from internal systems, external sources, and third-party providers. And layer it on top of what you’ve collected so the record actually tells you something useful. Not more data for the sake of having more data.

More meaning per record.

The distinction matters. Volume isn’t the goal. Usability is.

Raw and Usable Data Are Not the Same Thing: The Need for Data Enrichment

A contact list with names and email addresses is data, technically. Add company size, industry, the tech stack they’re running, and recent buying signals- now you’ve got something a rep can actually walk into a call with.

That gap between the two is what enrichment closes.

B2B sales teams feel this most acutely.

A CRM full of prospects with names, titles, and emails is fine. But add firmographic context, i.e., revenue range, headcount, growth stage, and what tools they’re already paying for- and the conversation before the first call changes completely. This is where top B2B databases for sales growth become valuable for GTM teams. You’re not figuring out if they’re a fit in the first twenty minutes anymore.

You already know. The call goes somewhere.

There’s a cost argument too that doesn’t get made enough.

Bad data isn’t a data quality problem in the abstract- it’s a money problem. Reaching the wrong contacts, running campaigns to audiences that don’t match your ICP, and mispricing deals because account context is missing. This is why high-quality data is imperative for marketers. These have real dollar figures attached. Enrichment attacks those figures directly.

The Types of Data Enrichment That Should Matter

Seven types come up in the literature. Most teams realistically need two or three.

1. Firmographic enrichment is where B2B teams spend most of their time- industry classification, company size, revenue, and ownership structure. The information that answers “does this account belong in the pipeline at all” before anyone has to sit through a discovery call to find out.

2. Technographic enrichment tells you what a company is already running. Their current stack. What they’re likely locked into, what they might be looking to replace. It also plays a critical role in developing a data-centric martech stack for business success. For competitive positioning and integration pitches, this is genuinely the most underused type of enrichment in GTM teams.

3. Behavioral enrichment is what makes personalization real rather than cosmetic. Purchasing patterns, engagement signals, product usage data- the actual actions people take rather than the demographic bucket they fall into. These insights are central to building a data-driven marketing strategy. When personalization feels like it was written for you and not for your job title, behavioral data is usually behind it.

4. Contact enrichment is the one team’s reach for first because the gaps hurt immediately- direct dials, verified emails, secondary contacts at an account, and LinkedIn profiles. Many organizations rely on proprietary databases for B2B lead generation to fill these gaps efficiently. Basic stuff, but missing basic stuff is what kills outbound sequences before they start.

Demographic, geographic, and psychographic enrichment exists, too.

Demographics and psychographics matter more in B2C and DTC contexts. Geographic shows up in logistics, territory planning, and event targeting. Useful, but not typically the first place a B2B team should be looking.

The mistake isn’t ignoring enrichment types- it’s enriching everything without asking which specific gaps are actually costing the business money right now. Start there.

What the Data Enrichment Process Looks Like When Done Right

The steps aren’t complex. The discipline to follow them is.

Clean the data first. That is the part everyone skips because it’s genuinely unpleasant- duplicates, inconsistent formats, and records that haven’t been updated since the previous administration. Following proven data hygiene best practices is essential before enrichment begins.

None of that can be fixed “by” enrichment- it must be fixed before enrichment, or the enrichment compounds what’s already wrong. It can result in standardization, deduplication, and removal of the dead weight. Boring and non-negotiable.

Then figure out what’s actually missing. Not a theoretical wishlist of every field you’d love to have, but a practical audit of which gaps are causing bad decisions right now. What context would change how a rep approaches a call? What information would let marketing segment differently? That’s what you enrich.

Sources come from two directions.

  1. Internal sources such as transaction history, product usage data, support records, and historical interaction data are almost always underused. Many organizations are now building a layered data approach to combine these datasets more effectively. Most teams go straight to third-party providers without first asking what they already have sitting in other systems.
  2. External sources fill the gaps that internal data can’t cover: government datasets, public records, and third-party providers for contact and firmographic data. Understanding the use of third-party data becomes important when scaling enrichment efforts. Pick providers based on quality, not just coverage. A cheap provider with a high fill rate and questionable accuracy is a liability, not an asset.

Integration is where the rubber meets the road.

Enriched data that sits in a spreadsheet nobody opens didn’t actually enrich anything. It has to flow into the CRM, the marketing automation platform, and BI tools- wherever decisions get made. Solving data integration challenges is critical for making enrichment operational. Otherwise, the whole exercise was academic.

And then, the part almost everyone forgets is that it needs to keep running. Enriched data from eighteen months ago is no longer enriched data. People change jobs. Companies get acquired. Tech stacks shift. Enrichment is a continuous operation, not a project with an end date.

Use Cases That Show Why Data Enrichment Matters in Practice

Sales and marketing are the obvious ones.

Enrichment is what separates personalization that actually works from personalization that’s just a first name in a subject line:

  • Buyer intent data in ABM campaigns enables account scoring that goes beyond firmographic basics.
  • Campaign targeting built on what people actually do rather than what their LinkedIn says.
  • Customer segments that reflect real behavioral patterns, not just demographic buckets.

Customer success is where enrichment often has the highest ROI that nobody’s measuring.

Teams that enrich customer records with product usage data and external signals around company health can highlight at-risk accounts before those accounts start quietly evaluating alternatives. This is one of the ways data analytics can transform your sales and retention outcomes. That’s a completely different motion than discovering churn risk in the renewal conversation.

One is preventive. The other is damage control.

Cybersecurity teams use enrichment differently- layering geographic context, device information, and network data onto security event logs to distinguish genuine threats from anomalies more quickly. Security analysts working from enriched logs operate at a different speed than those working from raw event data.

Healthcare is doing innovative things with patient datasets enriched by wearable data, activity signals, and population health information. Same patient record. Completely different clinical utility depending on whether it’s been enriched or not.

What AI Is Actually Changing in Data Enrichment

The old enrichment model was batch-based. Scheduled jobs. Overnight syncs. Periodic updates from third-party providers. Fine when data moved at a pace that let you catch up. Not fine anymore.

AI agents are making real-time enrichment more practical.

A new account is created in the CRM- an agent triggers immediately, pulls current context from across the web, processes it, writes it back to the record before anyone opens it. The marketing data enrichment is invisible. It’s just there. That’s a meaningfully different capability than a weekly batch job.

LLMs are also making metadata enrichment scalable in ways it wasn’t before. Adding context, descriptions, and classifications to technical data- work that used to require human analysts and can now run automatically at volume.

The catch is the same one it always is.

AI running on a dirty, unstructured data foundation produces worse results faster. That’s why maintaining a future-first data foundation is becoming increasingly important for enterprises.It doesn’t rescue bad data. It amplifies it. The foundational cleanup, i.e., standardization, deduplication, and clear ownership of records, is still the prerequisite. That doesn’t change because an AI agent is doing the enrichment work downstream.

The Gap Between What Teams Think and What Actually Happens in Data Enrichment

Ask a RevOps team how complete their CRM records are. They’ll say 80%. Run an actual audit with strict completeness criteria, and it usually comes in somewhere between 40 and 60 percent.

That gap is where revenue leaks quietly, through deals pitched at the wrong angle, campaigns that don’t convert, renewals nobody saw coming because the signals were sitting in a system nobody was enriching. It’s not dramatic. It’s just consistently expensive.

Data enrichment fixes it.

It’s not a one-time push or a tool purchase that runs itself.

It’s a continuous discipline that treats data quality as a revenue problem rather than an IT problem. Many organizations are already recognizing the long-term benefits of data enrichment across revenue teams. The teams running it that way are compounding an advantage over the ones that aren’t. The distance between those two groups gets wider every quarter.