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For centuries, bloodletting with leeches was a common medical treatment. Even as physicians began to recognize its limitations and potential harms, the practice persisted. Patients expected treatment to look like treatment: if a vein hadn't been opened, it didn't necessarily feel as though anything had been done.
The fact that people kept asking for it, and that doctors kept providing it, wasn't evidence that it worked.
That's the analogy Dale W. Harrison uses to illustrate a recurring issue in B2B marketing: a product can have satisfied customers, strong renewal rates and excellent reviews without those things necessarily proving that it delivers what it claims.
In Episode 9 of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners, Dale is joined by Diego Sosa to explore why data and information aren't the same thing. Building on Episode 8's discussion of market trends and branded search, they examine why the data marketers collect doesn't always support the conclusions drawn from it.
Why more data doesn't necessarily mean more information
B2B marketers can collect enormous amounts of behavioral data, but the number of signals doesn't necessarily reflect how much they've learned about a prospect.
Why ten downloads don't mean ten times the certainty
Consider a prospect who downloads two eBooks instead of one. A conventional lead-scoring model might treat that as twice the evidence of buying intent. Download ten, and the score continues to rise.
But the information contained in those downloads doesn't increase at the same rate.
The first download may tell you very little. Was it the right person? Was the content relevant? Was the download deliberate? As more activity accumulates, confidence may increase, but eventually each additional interaction tells you less that you didn't already know.
Someone downloading ten pieces of content about the same subject might provide useful evidence of interest. The hundredth download probably adds very little. Yet a points-based model can continue assigning points as though each action provides entirely new information.
The name tags at a networking event
Diego and Dale illustrate the same distinction with a networking exercise.
Imagine meeting someone wearing a name tag that says Mary. You have one piece of evidence about her name. If she's wearing ten identical tags, you don't become ten times more certain. The additional tags simply repeat the first.
The reverse is also true. If someone is wearing a name tag that says Mickey Mouse, your existing knowledge tells you to question what you're seeing. The data doesn't exist in isolation; its meaning depends on what you already know.
Information therefore comes from the relationship between new evidence and existing knowledge, rather than simply the volume of data collected.
What does the data actually tell us?
The next challenge is establishing whether the patterns marketers observe have any meaningful relationship with the outcomes they're trying to predict.
Intent data's unanswered question
If engagement genuinely predicts buying, there should be a measurable relationship between the two. Compare engagement with actual purchase outcomes over a sufficiently long period and, if the relationship is strong, you should see a meaningful pattern.
Dale challenges intent-data providers to demonstrate that relationship. He describes examining real-world vendor data and finding something much less convincing: a cloud of points rather than a clear association between engagement and eventual purchase.
That doesn't make engagement data useless. But observing someone interacting with your website or content doesn't automatically establish their buying intent. That claim requires evidence connecting the behavior to commercial outcomes.
As Diego puts it, extraordinary claims require extraordinary proof. In this case, the proof should be relatively straightforward: demonstrate that the signal reliably predicts the outcome.
Why you can't work backwards from success
Another common analytical mistake is to look only at successful customers and work backwards from their behavior.
The medical analogy makes the flaw clear. If you studied 5,000 people with measles, you'd find that almost all had a fever. But that doesn't mean everyone with a fever has measles. You've identified a characteristic of people with the condition without establishing whether it distinguishes them from people without it.
The same mistake can appear in B2B analysis. Suppose 20% of closed-won accounts visited your LinkedIn company page. That might sound like evidence that LinkedIn engagement is associated with winning.
But what if 30% of closed-lost accounts visited it too?
The signal exists, but it doesn't distinguish successful outcomes from unsuccessful ones. To establish whether a characteristic is useful, you need to examine how frequently it appears among both wins and losses.
Why attribution can tell the wrong story
Even when an activity genuinely contributes to an outcome, the measurement system may assign the credit elsewhere.
Billboards, branded search and the attribution problem
Dale draws on his experience with out-of-home advertising. When a billboard goes live, branded search and website traffic can increase as people see the message, remember the brand and search for it later.
The search is measurable. The billboard's influence may not be.
That creates an attribution problem: the system can see the final click without necessarily identifying what prompted it. The measurable interaction receives the credit even when another channel helped generate the demand.
A similar issue arises in paid search. Dale describes branded terms, where people are already searching for a specific company or product, being combined with generic search terms in the same campaign.
Because branded searches typically convert at higher rates, they can make overall campaign performance look stronger without demonstrating how much additional demand the advertising created.
The question is what would have happened without the intervention.
Ask the question before looking at the data
Diego encountered another version of this problem early in his career. A manager encouraged him to examine the data first and see what it revealed. He came to recognize the weakness of that approach: without a clear question or hypothesis, almost any dataset can produce an apparently meaningful pattern.
A more useful starting point is to define what you're trying to establish.
Did the outcome change because of the marketing activity, or would it have happened anyway? Answering that question requires more than observing what happened afterward.
It also requires appropriate comparisons. A new marketing channel running for three months isn't necessarily comparable with an established channel that has spent years building awareness and demand. Without accounting for those differences, a comparison can confuse channel maturity with effectiveness.
The underlying data may be accurate. The conclusion drawn from it may still be wrong.
Turning data into better decisions
Across these examples, the central distinction is consistent: collecting data and extracting useful information are different activities.
A thousand signals can tell you less than one well-chosen signal if they don't help answer the question you're investigating. The challenge is identifying which observations provide meaningful evidence and which simply add volume.
Rather than asking “What data do we have?”, marketers need to ask “What information does that data actually give us?”
That becomes especially important when deciding which accounts are worth pursuing. Engagement might suggest that an organization is interested in a category, but it doesn't necessarily tell you whether that organization is likely to buy from your business.
Answering that question requires evidence about the relationship between the buyer and seller, including where your organization has demonstrated that it can compete successfully.
That's where Dale and Liam go next in Episode 10, The Buyer–Seller Fit Model: What Is It? How to Measure It?, exploring how Buyer–Seller Fit can provide a more reliable basis for assessing which opportunities are worth pursuing.
