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Twenty years ago, before today's fragmented digital landscape made attribution more difficult, Dale W. Harrison built a system that could de-anonymize roughly 90% of website traffic down to a named individual, rather than simply identifying the company behind the visit.
He tracked what those people did, built statistical models to estimate their likelihood of buying and then tested those predictions against what actually happened.
The results were less precise than expected. His two groups – people the model considered likely to buy and those it considered unlikely to buy – were each roughly 60% correct and 40% wrong. The model was doing better than chance, but only modestly. Even with extensive data and modeling, it could improve the odds without establishing certainty about who was ready to buy.
That experience provides the foundation for Episode 10 of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners, where Diego Sosa joins Dale to build on Episode 9's discussion of data and information. Together, they explore how Buyer–Seller Fit can provide a more evidence-based alternative to conventional MQL scoring.
What makes a predictive model useful?
A model can contain sophisticated mathematics, thousands of data points and a convincing explanation of why certain signals indicate buying intent. None of that establishes whether it actually predicts the outcome it claims to predict.
Diego illustrates this with an analogy from medicine. Many drugs work reliably without scientists fully understanding every mechanism involved. What matters is evidence that the treatment produces a meaningful outcome under controlled conditions.
The same principle applies to lead scoring. The important question is whether accounts identified as more likely to buy actually go on to do so at higher rates.
Dale's early experiment matters because he tested exactly that, rather than relying on how sophisticated the model appeared.
How Buyer–Seller Fit changes qualification
The framework brings together an idea developed throughout the series: the probability of winning depends on both the buyer and the seller.
A buyer may be willing to purchase the type of product you sell without being likely to choose your company. And two sellers offering similar products can have very different success rates with the same types of customers.
Why fit works in both directions
HubSpot, for example, holds roughly 7% of the overall CRM market compared with Salesforce's 30%. But among Fortune 1000 companies, Salesforce has a much stronger position.
Buyers have preferences about the companies they're willing to work with, particularly when purchases involve significant financial or operational risk.
The seller's capabilities matter just as much. Dale describes two companies selling essentially the same product but serving very different customers. One had developed expertise selling to individual university researchers, while the other understood how to navigate procurement at large pharmaceutical companies such as Pfizer and Eli Lilly.
Their products could be comparable, but their ability to win particular customers wasn't. A useful qualification model needs to account for both the buyer's willingness to consider a seller and the seller's demonstrated ability to win.
Sales is a portfolio of bets
Dale uses the analogy of slot machines to explain why probability alone isn't enough. Every opportunity has a likelihood of success and a potential payoff.
A 50% close rate on small deals can generate less value than a 5% close rate on opportunities with much greater customer lifetime value. Looking at win rate alone misses the economics of the decision.
Sales-cycle length adds another consideration. A shorter cycle isn't necessarily better if those deals are less valuable, particularly when the type of customer being pursued strongly influences how long a sale takes.
The useful measure is the return a business generates from the sales effort it invests. That requires considering the probability of winning, potential customer value and the cost of pursuing an opportunity together.
Using your own CRM data
Dale's proposed approach starts with a company's historical sales outcomes rather than a generic scoring model.
By comparing accounts that became customers with those that didn't, businesses can identify characteristics that distinguish successful opportunities from unsuccessful ones.
As the series has explored previously, simply identifying patterns among closed-won accounts isn't enough. Those characteristics need to occur at meaningfully different rates among wins and losses to provide useful predictive information.
That evidence can help establish where a business consistently wins, which customers are most valuable and how those factors should influence qualification.
Why digital engagement isn't enough
Dale's earlier validation work also revealed the limitations of using digital behavior to infer buying readiness.
Some prospects, whom he calls “click monkeys,” engage heavily with content, visit pages and download resources without ever becoming customers.
Others leave almost no digital footprint before entering a sales process. They may have used the product at a previous company, heard about it from a colleague or arrived through conversations that never touched a trackable marketing channel.
The two groups illustrate why engagement alone is an unreliable measure of buying readiness. High activity doesn't guarantee a purchase, while a lack of measurable activity doesn't rule one out.
Adding more behavioral signals doesn't necessarily resolve that uncertainty. The more useful approach is to establish what the available evidence can reliably predict and test those predictions against commercial outcomes.
From qualification to sales effectiveness
Buyer–Seller Fit shifts qualification toward a more practical question: given what the business knows about an account and its own sales capabilities, how attractive is the opportunity?
That doesn't eliminate uncertainty. It provides a better basis for allocating sales effort, taking into account both the likelihood of winning and the potential value of the customer.
But identifying worthwhile opportunities is only part of the equation. Businesses also need to understand how effectively they're converting that investment of time and resources into commercial outcomes.
That's where Dale goes next in Episode 11, How to Measure Sales Effectiveness, exploring how to measure the return a business actually generates from its sales effort.
