The Fraud of the B2B Numbers Game

Why Lead Gen isn’t About Playing the Numbers Game, It’s about Playing the Human Game.

Hans Rosling was a medical doctor, professor of international health and well-known public educator. In his 2018 book “Factfulness” he debunks a lot of the generally held beliefs about the “state of the world” and despite his reliance on numbers, data and quantitative analysis he readily admits that “while the world cannot be understood without numbers, the world cannot be understood by the use of numbers alone.”

In the last 25 years it seems like we have tried to understand everything using numbers. We’ve reduced everything to an engineering problem. This has been especially true in business. We’ve seen the rise of the bean counters, the numbers driven MBA’s and the financialization of the western economy. No practice has been more affected than B2B sales and marketing. Despite all the application of numbers an older saying still holds true – “marketing leaders always knew they wasted half of their budgets…they just never knew which half.” The application of engineered thinking and numbers was supposed to answer this question. So, has it? Well…it has not. This is another example of our preference for extreme thinking where we’ve adopted engineering processes and overrated management science ideas while ignoring the realities of human behavior that make forecasting and predictions a fool’s play.

No worries, it isn’t over yet. We can use some number models and some probabilistic thinking to help overcome the challenge but first we can accept that this is not just a numbers game. For too long, the B2B community has been sold a lie: the idea that lead generation, for example, is just an engineering problem, that you can apply a mathematical formula to human behavior and expect reliable, predictable results. This mindset has led companies to waste vast amounts of time and resources on pouring “leads” into the top of the funnel, with the belief that the sheer volume will eventually yield deals. But here’s the truth: B2B lead gen isn’t—and never was—just a numbers game. Lead generation is about influencing human beings—who are illogical, unpredictable, and driven by emotions. The only way to truly improve results is by focusing on qualitative factors, particularly improving early-stage conversion rates through compelling, differentiated human interactions.

The Fraud of the B2B Numbers Game: Why Lead Gen is About More Than Just Math

We’ve heard it all for years - “if you pour enough leads into the top of the funnel, deals will magically fall out the bottom." This belief has shaped entire marketing strategies, pushed sales teams to the brink, and consumed countless resources. But here’s the truth: it’s a lie—a fraud that’s been perpetrated on the B2B community for far too long.

The reality is, B2B lead generation doesn’t fit neatly into an engineering problem. Human behavior, especially in a complex B2B buying process, is far too illogical and unpredictable to be governed solely by math. The numbers first approach fails because it ignores the most crucial element in sales: people. And if you don’t understand what makes people decide, no amount of lead volume will save you. 

The Numbers Game NEVER Worked

The engineering mindset has been drilled into the DNA of B2B sales and marketing. It’s a simple formula—get 1,000 leads in at the top, watch them filter down through your sales funnel, and close a few deals at the bottom. At every stage, conversion rates chip away at your lead pool until you have just a tiny fraction left.

But let’s look at what those numbers really mean, using “generally accepted” percentages from the perpetrators of these myths - what used to be “Sirius Decisions” (now Forrester), Gartner, Aberdeen and the rest of the “brains trust.”

For each closed deal I need ~4 opptys. To get to 4 opptys I need ~ 12 SQL’s, maybe 10, maybe 14? To get there I need something like 3 x to 6x MQLs so let’s say 4.5x which means say 57 MQLs? And to get there I need say 380 MLs (that’s a ~15% conversion from ML to MQL which is generous.) So if a “first call” fits into the MQL category that means we need to conduct 57 “first calls” for every closed deal. We can argue and tinker with this math until doomsday, but the conclusion remains sobering—your sales process is based on inefficiency, where failure is the rule, not the exception and the numbers at the top are just too big

Doubling Down on Early-Stage Conversion Rates

Rather than focusing on lead volume, the key to unlocking real revenue growth lies in improving your early-stage conversion rates. Specifically, the most pivotal point in the entire sales funnel is the first conversion—the jump from the first conversation to the second. This is where human emotion and decision-making first meet your product or service. It’s where you must start to create preference. It’s NOT just a “get to know you” call.

By re-framing your thinking here, you could either half the number of MQLs or first calls you need, OR double the number of closes. By improving just one conversion metric, you can double—or even triple—your closed-won revenue without any increase in lead generation costs. It’s about getting more out of the leads you already have, rather than chasing an endless supply of new ones.

Being Compelling and Differentiated: The Key to Doubling Your Conversion Rate

So how do you improve that all-important first conversion rate? The answer lies in delivering value right from the start. To do that, you need to be both compelling and differentiated. You’re not just selling a product; you’re selling urgency and uniqueness. You must answer two critical questions in the mind of your prospect:

1. Why Now? (Compelling Reason to Act)

   Human beings are driven by urgency. You need to show your prospects why they should act now rather than later. Maybe it’s a market shift, a looming competitive threat, or a time-sensitive opportunity. Whatever it is, it needs to resonate emotionally as well as logically. If there’s no urgency, the status quo remains. BUT, to do this right requires research, preparation, time and some daring.

   
   

2. Why Us? (Differentiation)

   This is where most B2B companies fail. They sound like everyone else. But differentiation is critical. You need to demonstrate why your solution is unique, why it’s the best fit for their problem, and why you’re the best choice. Whether it’s through unique features, a proven track record, or an innovative approach, your prospect needs to believe that you—and only you—can solve their challenge. That’s about connecting solutions to business drivers. Spotting synergies between your organizations. And again, to do that right takes research, preparation, time and some daring.

   
   

When you combine urgency and differentiation, you create an interaction that resonates with the human beings on the other end of the conversation. It communicates the biggest factor - TRUST. This is how you double your first-to-second conversation conversion rate—by being compelling and differentiated in a way that speaks directly to the needs and emotions of your prospects.

Victims of another numbers game

So now the problem would be – how do I prepare my team to be compelling AND unique AND personalized during all those first calls? AI doesn’t care about big numbers. So instead of keeping blindly throwing numbers at the problem, apply (I guess) another form of numbers – those of AI. Specifically, a specialized sales a.i. like Shadow.

Breaking Free from the Numbers-First Fraud

The idea that lead generation is purely a numbers game ignores the human element at the heart of every buying decision. By shifting the focus away from lead volume and toward improving early-stage conversion rates through probabilistic thinking, B2B companies can finally unlock sustainable growth.

Here’s the bottom line: you don’t need more leads—you need better conversions. The companies that win in today’s B2B landscape are the ones that think probabilistically, cast off the fictions of the past, allow for human behavior and tailor their sales processes accordingly. Focus on answering "Why Now?" and "Why Us?"

It’s time to stop playing the numbers game and start playing the human game. Because in B2B sales, it’s the only game that matters

Time to Rethink Sales Training with GenAI?

The Persistent Problem with Sales Training: Sales training has been the bedrock of professional development for decades, yet the methods we cling to haven’t evolved much. The result? Increasingly outdated and ineffective practices. Whether it’s the traditional face-to-face workshops (class – room) or the never-ending video marathons (class-zoom), the current approach fails to tackle the real enemy: "knowledge decay." Ever heard of the Forgetting Curve? You probably have, but guess what? You forgot!  Introduced by psychologist Hermann Ebbinghaus, it’s a phenomenon that shows how quickly we lose newly acquired knowledge. Think about it—within just an hour, 50% of what we learn slips away. Give it 24 hours, and 70% of that new information is gone. After a week? We’ve forgotten up to 90%.

Is This Really Working? As if the Forgetting Curve wasn’t enough to make you question traditional sales training, consider the other challenges we’re up against:

• Turnover Turmoil: It’s tough to pin down exact figures, but B2B sales turnover hovers around 35% to 45%, according to Forrester and HubSpot. That’s nearly half of your salesforce walking out the door each year. What does that mean for you? A constant cycle of recruiting, onboarding, and training—only to do it all over again. And after all that effort, it’ll take another six months for these new hires to become truly effective. Are we really okay with this?

• Mixed Teams, Mixed Results: With turnover comes inconsistency. Your sales team becomes a patchwork of varying skill levels, which is hard to manage. It’s no wonder we see the infamous “bell curve” of sales performance—some sellers excel, others flounder, and most hover somewhere between average and mediocre.

• The Death of Mentorship: Technology, budget-conscious CFOs, and even COVID-19 have changed the workplace forever. One casualty? Mentorship. Back in the ‘80s (the 1980s, to be clear), I learned more from seasoned mentors than from any formal training course. But today? Sellers are more isolated, and the opportunity to soak up that invaluable wisdom is fading fast.

Why So Complicated? Sales training has somehow turned complexity into a badge of honor. We’ve traded simplicity for a labyrinth of techniques, acronyms, and “clever” processes. It’s as if we believe the more intricate the training, the more effective it must be. But let’s be real—does anyone actually benefit from this convoluted mess? Too often, sellers are left with an overflowing toolbox, unsure which tool to use when it matters most. Isn’t simplicity the soul of efficiency? Yet, sales training has drifted far from that ideal.

The truth is, the complication of sales training, and the rise of the “deal coach,” serves another purpose: keeping consultants in business. The longer they stay involved, the more fees they collect. But is this really the best way to equip our sales teams?

A Brighter Path with GenAI: The old methods? It’s time to let them go. Instead, let’s acknowledge the limitations of traditional training and the new challenges we face. Enter a fresh approach: targeted, timely enablement and readiness. Let’s use tools that don’t just add to the noise but actually help—tools that do the research, connect the dots, and provide actionable insights right when sellers need them. Imagine getting your team better prepared, faster, and with less effort. That’s the promise of GenAI.

Conclusion: So, isn’t it time to say goodbye to outdated sales training? Let’s welcome a smarter, more efficient way to empower our sellers. With Shadow’s specialized GenAI, we can transform how we enable, prepare, and manage our teams. Curious to learn more? Visit our website to see how GenAI can revolutionize your sales training. Empower your team with the tools they need, exactly when they need them.

From Cannes to Can with GenAI. Navigating Trust and Innovation in a Land of Confusion.

As Generative AI (GenAI) continues to disrupt business operations, the opportunities for brands to innovate content, enhance productivity, and grow value are unprecedented. However, the landscape of AI adoption is anything but clear-cut.

At a recent panel discussion titled "GenAI In The Wild: Top Marketing Leaders On The Brand Experience Future," hosted by Adobe and Forbes in Cannes , France, (tres bon n'est pas?) some insights were shared about the current state and future of GenAI in marketing.

• Seamless Customer Experiences in the AI Era:

Creating seamless digital experiences is now a core part of a brand’s identity. Nothing terribly new here. People have been talking about this almost as long as they've been talking about digital transformation. Clearly, AI offers opportunities for automation, enhancing speed and efficiency, but also comes with risks that need careful management.

• Marketing in the Era of Generative AI:

This is considered the golden age of marketing, with AI playing a crucial role in reinventing business models. Many CEOs believe their business models need to evolve to survive, highlighting the growing importance of marketing in driving positive outcomes. Again the growing importance of marketing has been talked about before, but it's never really got there, has it? Will it be different this time?

• Current Maturity Level of Generative AI:

AI is still in its early stages within enterprises. It's adoption is inconsistent across different size of companies. Most companies are just beginning to integrate it into their workflows. However, there is rapid acceleration in AI application, with a shift towards customization and compliance.

• Impact of Generative AI on Marketing Organizations:

Tools like Adobe Firefly (shameless plug, but ok) are enhancing content creation and testing, significantly reducing time and effort. Internal AI tools are improving efficiency across various tasks, demonstrating substantial time savings.

• Future Predictions for AI in Marketing:

2025 is predicted to be the year of "trust" and "reinvention" in AI for marketing. The panel underscored the transformative potential of GenAI in marketing, emphasizing the need for responsible practices and strategic integration to enhance customer experiences and drive innovation.

So that's enough from the panel in Cannes...merci beaucoup mes amis! What do we see from our less glamorous perch?

The Confusion Around AI Adoption

We see a significant divide in how companies perceive and adopt AI:

1. Confident Adopters: Some companies claim to have a solid grasp on AI and GenAI, with a clear vision of its application within their businesses. They see AI as a critical tool for creating seamless customer experiences and driving business model reinvention. Whether their claims are true, remain to be seen.

2. Uncertain Explorers: Others acknowledge the potential of AI but remain uncertain about its practical application. They might understand the basics of AI but struggle to integrate it effectively into their workflows and strategies. This is where we see most companies.

3. Skeptics: A few seem to deny its usefulness altogether, possibly due to a lack of understanding or fear of the unknown. They might be hesitant to invest in AI due to the perceived risks and complexities. We think these "leaders" are also the sunset cruise crowd. In other words, they're closing in on retirement and their reluctance to really embrace A.I. is more about a quiet life until they board the S.S. Retirement and sail off into the commercial sunset!
   

The Era of Private, Specialized AI

In this landscape of mixed perceptions and adoption levels, private, specialized AI solutions play a pivotal role. Unlike generic AI models, these solutions are tailored to the specific needs and data of individual functions and businesses. They offer several advantages:

Customization and Control: Businesses can train AI on their proprietary data, ensuring that the outputs are relevant and aligned with their unique requirements.

Enhanced Security and Privacy: With private AI, companies can maintain control over their data, addressing concerns around data security and privacy.

Specificity in Application: Specialized AI can be fine-tuned to address particular business challenges, leading to more effective and impactful outcomes.

Trust as a Cornerstone

Trust is fundamental in the era of AI. However, in a world where distrust has grown, simply acknowledging the importance of trust is not enough. Businesses need actionable strategies to build and maintain trust in their AI initiatives. Here’s how:

• Transparency: Be open about how AI models are developed, trained, and used. Provide clear explanations of the data sources and methodologies to stakeholders.

• Accountability: Take responsibility for the outcomes of AI models. Ensure there are mechanisms in place to monitor and address any issues that arise.

• Ethical Practices: Prioritize ethical considerations in AI development and deployment. This includes avoiding biases, ensuring data privacy, and respecting intellectual property rights.

• Prove Trustworthiness: With new AI innovators and vendors entering the market, building trust is crucial. Transparent exchanges of information are essential to overcoming the "Mexican standoff" where neither party trusts the other, impeding progress.
  

Avoiding the Wait-and-See Trap

The confusion around AI adoption has led some businesses to adopt a wait-and-see approach, hoping to learn from the experiences of others before making their move. While this strategy might seem prudent, it risks putting these companies further behind. The rapid pace of AI development means that early adopters are already reaping the benefits of enhanced efficiency, better customer experiences, and innovative solutions.

Conclusion

The role of private, specialized AI is becoming increasingly important in navigating the complexities of AI adoption. By focusing on customization, security, and specificity, businesses can leverage AI to its full potential while addressing concerns around trust. As we move forward, it’s crucial for companies to embrace AI with transparency, accountability, and ethical practices, avoiding the pitfalls of hesitation and staying ahead in the competitive landscape.