Positioning, Messaging, and Branding for B2B tech companies. Keep it simple. Keep it real.
Website activity like form fills don’t tell you who’s ready to buy. Real buying signals come from people inside companies—acting together, not alone. This article outlines how to spot genuine buying signals, track them over time, and start forecasting where deals are likely to form.
As the 6sense B2B Buyer Experience Report reveals, many GTM teams still chase form fills and PDF downloads like they’re gold.
A significant portion of buyers (81%) make purchasing decisions before ever filling out a form or speaking with a sales rep. This underscores the need to look beyond traditional metrics and focus on genuine buying signals.
In other words, clicks alone aren’t buying signals, engagement isn’t intent, and MQLs don’t predict revenue.
If you missed why the MQL model fails, read Part 1: Why MQLs Don’t Work.
This article addresses what we should be watching instead.
B2B procurement teams have more than one person checking out multiple solutions. True intent doesn’t show up once. It builds, clusters, and repeats.
Keep an eye on the following:
As you can see, it’s not a single click. It’s coordinated activity.
“Intent signals are not individual behaviors. They are patterns of behavior across accounts and buying groups.”
Kerry Cunningham, 6sense
You won’t see the full story if you only watch your own site.
Tools like 6sense and Bombora show you what’s happening elsewhere—what accounts are researching, comparing, or revisiting.
Not every visitor matters. But when a senior buyer and someone from procurement show up together? That’s not random.
Plus, every procurement team has a champion who, well, champions the purchase. That’s your new best friend.
When someone revisits after 30 or 60 days, especially with new teammates in tow, that’s a sign the conversation inside their org is moving forward.
Selecting the right tools depends on your team's specific needs and readiness to act on B2B intent data.
Most companies start with one or two, usually an intent platform and a way to personalize follow-up or prioritize Sales action. What matters most isn’t the tech. It’s having the team and process to use it.
NOTE: These tools work best with reliable data and when your team is ready to act. If Marketing tracks intent but Sales doesn’t follow up or follow through, those signals will expire fast. Intent data only works when your GTM team is aligned, trained, and supported to respond at the right time.
One visit provides limited insight. Behavior over time is more telling.
Use rolling windows in intervals of 7, 14, or 30 days. Look at what’s changing over time, not just what happened yesterday.
Patterns hold more significance than isolated points.
It’s easy to think that forecasting a buyer’s intent can predict which lead will close next.
Not true. Unless you understand which accounts are likely to enter the pipeline, you can’t forecast their intent.
The best forecast starts with behavior:
No one needs more data. We need better answers.
Causal AI tools like Proof Analytics cut through the noise. You can even create what-if scenarios that take market fluctuations and geopolitical challenges into account.
“Causal AI identifies the true cause-and-effect relationships between marketing investments and revenue outcomes.”
Mark Stouse, Proof Analytics
Causal AI tools show what actions actually led to revenue, which ones didn’t, and why.
It’s the difference between guessing and knowing.
Procurement teams leave clues in the form of patterns, people, and momentum, not gated PDFs or demo requests.
If you’re tracking real buying signals instead of MQLs, you’re well on your way to showing up early.
And don’t forget the 95:5 Rule. The vast majority of buyers won’t raise a hand today. They’re watching, researching, and forming opinions about the brands they remember (hint).
Next week we’ll get into Part 3, which is all about staying top-of-mind. Because if they forget you, they won’t choose you.
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This article explains why MQLs don’t work in B2B tech, and why it’s time to shift from vanity metrics to real buying behavior. The Marketing Qualified Lead (MQL) model was always broken. It assumes one person’s action signals buying intent. B2B buying happens in groups, not clicks. Research from Kerry Cunningham, Mark Stouse, Forrester, and 6sense shows MQLs fail because they misunderstand buyer behavior. Marketing should measure influence on revenue, not leads.
The Marketing Qualified Lead (MQL) didn’t suddenly stop working. It actually never worked. That’s because the whole notion behind MQLs was flawed from the get-go.
The entire demand gen ecosystem is built around maximizing MQLs rather than revenue, what Kerry Cunningham, former VP at Forrester and now at 6sense, calls “The MQL-Industrial Complex.”
“The MQL-Industrial Complex has a stranglehold on modern B2B marketing and sales. It shackles marketers to obsolete goals and metrics that waste revenue team resources, alienate buyers, and stifle innovation.”
Marketing teams counted form fills, webinar sign-ups, and downloads as signs someone was ready to buy. But those aren’t signs of serious interest. They show curiosity, not readiness. They’re tire kickers at best, bots at worst.
“The MQL is not just outdated—it was never designed to measure what actually drives B2B revenue.”
Kerry Cunningham
MQLs make great-looking dashboards. But when you dig deeper, the numbers don’t add up.
MQLs measure the wrong things in isolation, like sourcing metrics. These metrics make it difficult to attribute commercial outcomes solely to marketing. This is especially true where buying is complex and includes renewals and expansion.
“We often say that marketing-sourced metrics are the fastest way for a CMO to get fired.”
Simon Daniels, Forrester
B2B buyers don’t casually shop your website or randomly download a PDF hoping for an aha moment.
By the time your brand shows up on their radar, buyers are validating what they already suspect about your solutions. They’re looking for proof they made the right choice. Can you be trusted?
“Buyers don’t engage until they’ve picked a winner, at about 70% through their buying journeys.”
Kerry Cunningham
In other words, by the time someone becomes your “lead,” they’re well beyond initial research and have mentally placed you (or a competitor) at the top of their shortlist.
And with tools like Agentic AI, buyers will only become more informed, decisive, and independent.
Over the past two decades, marketers have complicated things by reinventing the basics to make Sales-Led and Product-Led models look better.
Marketing is, and always has been, non-linear. It doesn’t follow the neat linear process Sales hopes for. Creating new labels and more acronyms doesn’t help.
The fundamentals haven’t changed:
As I mentioned in a previous article on why GTM metrics fail, marketing shouldn’t just feed the funnel, it should improve it.
So what are the alternatives to MQLs in marketing that actually reflect buyer behavior and impact revenue?
Ditch Sourcing Metrics
Forrester’s Ross Graber advises B2B Marketers to ditch sourcing metrics for metrics tied closer to revenue and business goals.
When you compare B2B revenue metrics vs MQLs, the gaps become obvious. Ditching MQLs in B2B tech is overdue.
Use Causal AI
According to Mark Stouse, CEO of Proof Analytics, the MQL model is failing B2B marketing because it has become a vanity metric, often based on engagement signals that do not indicate buying intent.
By using Causal AI, you can:
“Causal AI brings a sophisticated, evidence-based approach to GTM strategy. It identifies the true cause-and-effect relationships between marketing investments and revenue outcomes, eliminating guesswork and revealing which strategies drive provable growth.”
Mark Stouse
The truth, albeit inconvenient, has been staring us in the face for some time.
MQLs didn’t suddenly break one day. They have always been broken because they have been stuffed into a linear sales model that focuses on lead volume.
Instead of chasing leads, focus on tracking genuine buying signals and measuring marketing’s influence on revenue. Then get back to the basics:
“Being remembered is more valuable than being better.”
Mimi Turner, The B2B Institute
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Ignoring AI is risky, especially now! Shareholders are already filing lawsuits over missed opportunities and messy data. The Delaware 2023 ruling now holds executives and officers personally responsible. AI is changing leadership accountability and fiduciary duty faster than we can keep up. Here’s how to prepare.
In Part 1 and Part 2 of this series, Mark Stouse, CEO of Proof Analytics, and I explored why AI accountability and fiduciary duty now sits with the entire leadership team—and how the Delaware 2023 ruling changed the rules for C-suite liability.
This third part recaps what’s happening right now.
For example, shareholders are paying attention and lawsuits have already begun by using AI to investigate leadership oversight in real time. Executives who don’t act could face serious personal consequences.
REWATCH the entire series on LinkedIn:
Before 2023, corporate officers were rarely sued unless they acted with clear intent to do harm. That’s no longer the case.
A Delaware court ruled that officers can now be held liable for poor decisions, even without bad intent. In other words, saying, “I had no idea,” won’t hold up in court.
This came from a case involving McDonald’s and their CHRO. Being careless or uninformed is enough to bring legal trouble.
“The vulnerabilities to the company just went up exponentially... the bar for proving breach of fiduciary duty was dropped to the floor from a very high place.”
Mark Stouse
Mark spoke to about 350 CFOs and many agreed this ruling is a bigger deal than Sarbanes-Oxley. AI now makes it easier to spot crappy data and call out risky decisions.
Unlike McDonald’s, a lot of lawsuits are currently being settled quietly, not just to avoid financial loss, but to prevent reputational damage.
That risk is now front and center. Executives aren’t just trying to protect the company. They’re trying to protect their names.
As mentioned, one of the first things shareholders are targeting is data quality. If your CRM or marketing automation data is flawed, your entire revenue engine is vulnerable. That’s low-hanging fruit for litigation, and it’s already happening.
And if you’ve had conversations with vendors and walked away without action? That trail exists. AI note-takers, emails, even meeting transcripts, can be used to show that you were aware—and failed to act.
This is where legal exposure gets personal.
Marketing teams are seeing this shift with buyer bots. These bots now control much of the personalization. That flips the value of seller-side personalization on its head.
“Personalization from the seller side no longer is needed... It’s really kind of going to be negated one way or the other.”
Mark Stouse
Teams need tools that reveal what’s actually working. Causal AI tools like Proof Analytics helps big time. Sticking with old habits will get you into trouble.
While the legal risk is fairly obvious, the bigger and less evident threat might be your career.
“You can insure against financial liability, but you can’t insure your reputation.”
Mark Stouse
Boards are less forgiving. Shareholders are quicker to act. Leaders who don’t move forward risk tarnishing their reputation indefinitely.
Just look at the CHRO in the McDonald’s case. That individual may never work in HR ever again.
There is a school of thought that says AI could potentially replace the C-suite as we know it today. And it isn’t just theory anymore.
“Over time, the biggest career losers from all this will be the C-suite... At some point, you don’t need a 14-million-dollar annual salary for a CEO.”
Mark Stouse
AI decision-making capabilities keep getting better and better. If AI starts making smarter calls than the leadership team, why keep the old structure?
The writing may already be on the wall.
If you lead a company (or plan to) here’s what matters right now:
AI can help you protect and defend, but it can also quickly help you find a way to lead better, faster, and with more clarity.
You can use it to plan smarter GTM strategies, adapt in real time, and stay ahead of shareholder expectations.
One last thing: Mark shared an analogy that’s quite apropo.
Imagine standing on one side of a fast-moving river, and you need to get to the other side. You can swim or stay where you are.
“The difference between humans and every other species is that when the river changes course, we can swim. But many executives are standing still, waiting to be swept away.”
Mark Stouse
The river’s already moving. The ones who swim now might just make it to the other side.
REWATCH all 3 parts of this series on LinkedIn:
If you haven’t seen them, now’s a good time. What you don’t know can still cost you.
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Most GTM teams rely on pipeline, conversion rates, and revenue tracking, but these GTM metrics fail to explain why revenue grows or stalls. Traditional reporting shows correlation, not causation, leading to unreliable forecasts and wasted marketing spend. Causal AI for marketing analytics shows what is happening and why, and how to improve GTM forecasts.
A lot of GTM teams struggle with reporting mistakes because their dashboards don’t explain why revenue grows or stalls. Most traditional GTM metrics fail to show what’s actually driving revenue or how to predict future growth accurately.
Todd Mumford recently pointed this out on LinkedIn, listing friction metrics that usually get ignored.
Friction metrics reveal where GTM efforts break down and explain why we keep missing our targets. They rarely show up at quarterly reviews because they are rarely tracked consistently.
“The marketers who will outperform are brave enough to measure what hurts.”
Todd Mumford
For Todd’s complete list of metrics, see his post on LinkedIn.
Most GTM reporting focuses on what happened, not why.
You get revenue numbers, conversion rates, and pipeline figures, but these only tell part of the story. Here’s what’s missing:
You might see an increase in web traffic alongside revenue growth and assume one drove the other. But without causal analysis, you don’t know why revenue increased. Maybe it was a pricing change, a competitor going under, or an unrelated market trend.
According to a Wharton study, 57% of marketers misinterpret correlation as causation, leading to bad investments and wasted budget.
Think of it this way:
Many RevOps teams rely on pipeline coverage. For example, “We have 3x our quota in pipeline, so we’ll be fine.”
But without understanding which opportunities are likely to close and why, these forecasts are unreliable.
Google’s research confirms that traditional Media Mix Models (MMM) often inflate ROI estimates because “MMM typically produces correlational, not causal results.” That results in improper budgeting and misleading insights.
Marketing isn’t linear. Deals don’t move through funnels nor in a straight line.
Buyers come and go as they please revisiting touchpoints, getting stalled by procurement, and engaging multiple channels. But most GTM teams don’t capture these behaviors.
For example, 60-70% of B2B marketing content goes unused by Sales, according to Forrester. If you’re not tracking which content is influencing deals, you’re burning money.
To answer what’s happening, why it’s happening, and how to predict growth, GTM teams need to track metrics that explain real-world outcomes.
Traditional analytics can tell us what happened—revenue increased 20% last quarter. But It’s a mistake to assume that just because two things happen at the same time, one must have caused the other.
Causal analytics help us understand why—a specific campaign, a competitor going out of business, or an economic or geopolitical shift. Causal AI separates real cause-and-effect relationships from coincidences. It filters out random noise and external factors to show what’s really driving growth.
When done right, Marketing is an exponential multiplier of Sales effectiveness and efficiency.
The problem with traditional GTM metrics isn’t that they’re wrong—it’s that they’re incomplete.
If you’re only tracking pipeline and conversion rates, you’re missing the friction points, the real decision drivers, and the hidden inefficiencies that stall growth.
Causal AI can improve effectiveness, helping you fix GTM reporting mistakes, forecast revenue accurately, shorten sales cycles, and optimize your marketing spend.
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The Delaware Chancery Court Ruling of 2023 is a wake-up call. CMOs, CROs, and CDAOs are now personally responsible for oversight failures, bad data, and poor decisions. AI, especially Causal AI, is exposing the truth. Lawsuits are already happening. If you lead a team, you need to assess your risks, clean up your data, and use AI to protect yourself.
In AI Accountability Part 1, Mark Stouse and I talked about how AI is making executives more accountable. Now, we’re looking at the Delaware Chancery Court Ruling of 2023, a decision that puts more responsibility on business leaders. If you’re a CMO, CRO, or CDAO, this ruling could affect you in a big way. Here’s what you need to know about AI accountability in corporate leadership and how AI-driven risk management for executives can help.
REWATCH: Part 1 and Part 2 on LinkedIn.
Mark Stouse, CEO of Proof Analytics, put it simply: “If this ruling is so important, why isn’t everyone talking about it?”
The ruling made headlines in The Wall Street Journal and Financial Times with a sexual harassment lawsuit against McDonald’s, but outside legal circles, it didn’t get much attention.
“Historically, fiduciary duty had a very high bar—you had to almost prove nefarious intent. That’s no longer the case. If you’re an officer of a Delaware-domiciled company, you can now be held personally liable for negligence, incompetence, or just not knowing what’s in your own systems.”
Mark Stouse
This ruling affects roughly 90% of venture-backed companies in the U.S. and two-thirds of the Fortune 1000. Even privately held companies are under scrutiny.
If you’re in leadership, this matters to you.
Mark shared a case where a company settled for a huge amount because of bad CRM data. In fact, CRM data integrity (or lack thereof) has become a meme.
“I was supposed to be an expert witness in a case involving CRM data. The company settled for a lot of money out of court. The issue? The data was so flawed that it triggered fraud detection software. Sales reps had manipulated CRM records to hit incentives, creating a legal liability for the CRO, CIO, and CDAO.”
Mark Stouse
If your data is unreliable and you’re in charge of it, you’re responsible. Period.
Saying “I didn’t know” won’t protect you. Delaware fiduciary duty ruling impact is already being felt across multiple industries.
Each day AI is getting better and it’s making leaders more accountable.
“AI is going to be the great truth-teller inside corporations. Everything that can be known will be known or knowable.”
Mark Stouse
If your reports claim your marketing is driving revenue, but causal AI proves otherwise, that’s a problem.
If you’re in leadership, here’s a step-by-step guide to protect yourself:
Yes, the Delaware Chancery Court Ruling is a wake-up call. But it’s more of an opportunity to get ahead of potential litigation by cleaning up our data rather than fear-mongering.
Use AI to protect yourself. If you act now, you can stay ahead of the risks, prove your value, and future-proof your business.
“This is only the beginning. Shareholders, especially activists, are using this ruling to sue executives. If you’re not prepared, it’s just a matter of time.”
Mark Stouse
In AI Accountability Part 3, Mark and I will dive into the wave of lawsuits already happening and what you can do to stay ahead.
Stay tuned.
REWATCH: Part 1 and Part 2 on LinkedIn.
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AI-driven executive accountability is forcing leaders to take responsibility for their decisions instead of relying on vague statements or outdated assumptions. A new Delaware ruling makes CROs, CMOs, and CDAOs personally responsible for data quality and governance failures. Executives need to tighten data oversight, audit regularly, and work closely with compliance because AI fact-checking is exposing governance failures in real time.
In our latest LinkedIn Live session on AI Accountability, Mark Stouse and I dug into how AI is forcing radical transparency across the C-Suite.
For years, AI has been marketed as an efficiency tool, but it’s now putting executives under a microscope—especially the Chief Data & Analytics Officer (CDAO). AI’s impact on CDAO responsibilities is undeniable. The CDAO is at the center of it all, overseeing data quality, compliance, and legal risks that didn’t exist a decade ago.
Data quality is no longer an internal issue. AI-driven transparency is turning poor oversight into a legal and reputational risk. Get it wrong, and you could face lawsuits, SEC investigations, or worse.
Watch the full episode on LinkedIn.
Many executives have built go-to-market (GTM) strategies based on the idea that growth can be mapped out in a straight line. AI is proving them wrong.
“Roughly 20 to 25 years ago, founders and VCs decided they could remake B2B GTM into a deterministic, linear machine. They thought they could put a quarter in and get a gumball every time. That model failed—92% of those startups tanked.”
Mark Stouse
AI won’t fix broken GTM strategies. It will expose bad assumptions faster and force leaders to adapt—or fail even sooner.
AI is also putting more and more buyers in control. Companies that cling to outdated demand-generation tactics will lose to competitors who use AI to adapt in real time, recognize genuine buying signals, and pivot quickly.
Mark shared an interesting story about a CEO who walked into a town hall thinking he was in control—but AI had other plans.
Employees ran AI tools on his statements in real time. They compared his answers against past company reports and financial disclosures. Contradictions surfaced immediately. The Q&A turned into an awkward grilling session.
“Executives can no longer rely on ambiguity. The days of being able to say one thing today and another tomorrow without scrutiny are gone.”
Mark Stouse
Every word leaders say is recorded, analyzed, and cross-checked against financial disclosures, internal reports, and regulatory filings. AI is removing the gray areas that once gave executives room to maneuver.
The only way to stay ahead? Make sure what you say is accurate before you say it.
The 2023 Delaware fiduciary ruling for executives has changed everything. For the first time, leaders beyond the CEO and CFO—including CDAOs, CROs, and CMOs—are legally accountable for data quality and governance failures.
The Delaware ruling isn’t just a legal theory. It’s already leading to lawsuits. Mark shared an example that illustrates just how serious this is getting.
"A recent shareholder lawsuit named the CIO, CDAO, and CRO over CRM data quality issues. During discovery, a fraud detection tool was used to analyze the CRM, revealing that two-thirds of the data was manipulated, often to take advantage of sales incentive programs. That level of individual accountability simply wasn’t a risk five years ago."
Mark Stouse
This case revealed a stark reality: CRM data is a mess, and the legal risks of poor data quality are growing.
AI-driven audits are exposing fraud, inaccurate records, and manipulated pipeline data, which is leading to shareholder lawsuits and regulatory action.
CDAOs oversee data quality and governance, but CEOs, CROs, and CMOs are just as exposed. Bad data now impacts revenue, compliance, and investor confidence.
Executives who ignore AI-driven accountability won’t just lose credibility, they can also face legal consequences.
The companies that take AI-driven accountability seriously now will be the ones that stay ahead of lawsuits, regulators, and market shifts.
The AI accountability era has arrived. Executives who take data governance seriously will mitigate the inherent risks and avoid serious consequences.
In Part 2 of our next AI Accountability session, Mark and I discuss the legal risks executives face after the Delaware ruling.
Stay tuned.
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