.png)
Dale W. Harrison likes to run a poll: imagine a company about to sign a $5 million annual contract, renewed every year for five years. Who would be involved in the buying decision? Most people guess the CEO, certainly the CFO, perhaps a CTO or another senior executive.
Then Dale reveals the deal: Google's contract for toilet paper in its employee restrooms. Google spends roughly $5 million a year on it. Nobody's CFO is sitting in the meeting.
The point isn't the toilet paper. It's how quickly we fill in the blanks when imagining who makes a B2B buying decision. We assume senior executives are involved, construct buying groups around those assumptions and build targeting and lead-scoring strategies accordingly.
In Episode 7 of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners, Dale and Liam Moroney examine what happens when those assumptions meet the available evidence.
Can better lead scoring improve targeting?
Most organizations approach targeting and qualification as two separate steps. First, they define an Ideal Customer Profile (ICP) and build an audience around it. Then, they use lead scoring to decide which prospects are worth passing to sales.
Dale argues that the relationship can work in both directions. Historical data from accounts a company has won and lost can reveal which characteristics distinguish stronger opportunities from weaker ones. Those findings can then shape future targeting, creating a feedback loop that improves as more outcomes become available.
Why lookalike lists can be misleading
Consider what happens when a marketer uploads a list of closed-won accounts to an ad platform and asks it to build a lookalike audience.
The platform can identify shared characteristics such as employee count, revenue, industry and geography. But without comparing these with accounts the company didn't win, it can't establish whether those characteristics actually distinguish successful customers.
If 90% of closed-won accounts are based in California, for example, that might look like an important characteristic of an ideal customer. But what if 90% of closed-lost accounts are also based there?
The pattern is real. It just isn't useful.
It's the same issue explored in Episode 6 with the deliberately absurd example of vowels in company names. A characteristic shared by your winners only becomes useful when it helps distinguish them from your losses.
Why buying groups are harder to identify than marketers think
Even when a company can identify attractive accounts, there's another challenge: understanding who actually influences the purchasing decision.
Buying committees aren't new, but the growing availability of individual-level data has created an impression that marketers can identify and track the people involved more reliably than they often can.
Buying committees aren't a new phenomenon
A lot of current B2B marketing language treats buying groups as a relatively recent development. Dale points out that a 1965 Harvard Business Review article discussed buying committees in large organizations, drawing on research and practices dating back even further.
What has changed is the amount of data marketers can collect about individuals, rather than the existence of complex buying processes.
Dale's experience working with Fortune 500 procurement teams illustrates the gap between assumed and actual buying behavior. Vendor targeting often focuses on senior decision-makers, but the people reviewing and distributing supplier materials may be junior employees who have never visited the vendor's website.
The buying process may be complex, but that doesn't mean the people involved leave a clear digital trail.
How much of the buying group can you actually see?
That limitation becomes particularly important when account-based targeting treats inferred buying groups as established fact.
Dale estimates that de-anonymization rates of around 65% may be achievable at the account level, compared with just 5–7% for identifying individuals. Knowing that activity is happening within an organization is very different from knowing exactly who's involved in a purchase.
The danger is building a highly detailed targeting strategy around assumptions that the available data cannot reliably support.
Using sales outcomes to improve targeting
The alternative is to work backwards from what the business has actually achieved.
Examine closed-won and closed-lost accounts, then identify the characteristics that meaningfully distinguish them. The aim is to find patterns associated with successful outcomes, rather than simply cataloging everything existing customers have in common.
Buyer–Seller Fit adds another layer. An account might be attractive based on its size, industry and apparent demand, but that doesn't mean your company is well positioned to win its business. Historical sales performance can reveal where your organization consistently performs well and where it struggles.
Together, those insights provide a more evidence-based picture of the accounts worth targeting, informed by what the business has demonstrated it can win.
How do you know if it's working?
One practical measure is whether the quality of leads reaching sales improves.
If a Business Development Representative (BDR) spends five minutes qualifying a lead only to reject most of those passed across by the scoring model, the process isn't adding much value. A better indication of progress is whether sales acceptance rates improve as targeting and qualification become more informed by actual outcomes.
Dale uses the example of two slot machines. If one pays out once every 100 pulls and another once every 50, the second is twice as productive, even though neither looks particularly impressive in isolation.
That matters when the starting point is already low. If only around 1% of leads ultimately become customers, as long-running B2B benchmark data suggests, even modest improvements in qualification can have a meaningful commercial impact.
And even perfect information about buying intent wouldn't solve the whole problem. Knowing that someone wants to buy doesn't tell you whether they'll buy from you. An excellent prospect for the category can still be a poor prospect for a particular seller.
More data doesn't necessarily mean better information
This brings the episode back to a central theme of the series: the amount of data available isn't the same as the amount of useful information it contains.
Knowing the last hundred results on a roulette wheel tells you nothing about the next spin. Knowing the last million doesn't change that. You have more data, but no additional information that helps predict the next result.
The same applies to B2B targeting. Millions of firmographic and behavioral data points can create a detailed picture of an account without necessarily improving the ability to predict whether it's worth pursuing.
What matters is identifying the signals that genuinely distinguish outcomes and using them to improve both targeting and qualification.
That also requires understanding the wider context in which those outcomes occur. Even a well-targeted campaign can perform differently depending on whether the market itself is growing, shrinking or experiencing major competitive shifts.
That's where Dale and Liam go next in Episode 8 of B2B Effectiveness, exploring how changing category demand and competitive dynamics can influence business growth independently of marketing activity.
