Businesses have invested heavily in technology designed to help them understand what customers and prospects may want. We track behavior, identify visitors, enrich records, score prospects and increasingly use AI to determine what someone may want and what should happen next.
But there is a blind spot inside all that intelligence.
Technology can accurately record what someone did without knowing why they did it. A company can then act on its interpretation, change how the customer perceives the relationship and never know that its own action affected what happened next.
The systems may accurately record the outcome.
Leadership can still misdiagnose the cause.
That distinction starts with something deceptively simple:
Behavior can be observed. Intent has to be inferred.
When an Inference Becomes a Decision
A person visits a page, clicks a link, returns to a website or looks at pricing. Those are observable behaviors. Why they did those things is different.
Maybe they’re evaluating a purchase. Maybe they’re researching a competitor. Maybe they’re looking for something the company doesn’t sell. Maybe a search result took them somewhere that appeared relevant to a different question.
Businesses have always made reasonable inferences from customer behavior. The problem isn’t inference. The problem begins when an inference is treated as knowledge and then used to make a decision.
I experienced that recently while researching business checking accounts. I visited the business-related pages of a financial institution with which I already have a personal relationship. Shortly afterward, I received an email asking whether I was “still thinking about” a business financing product.
I wasn’t. I had been looking for a checking account, not financing.
I don’t know what triggered the email or what signals the company’s systems had available. But the resulting message assigned an intent and a continuing state of mind to me that weren’t there.
That’s an important distinction for leadership: what the customer actually told you, what their behavior actually showed you and what your systems concluded from it are three different things.
The company had information. The question was what it decided that information meant.
Identification Is Not the Same as a Relationship
The same problem can occur even when the technology identifies the person correctly.
Some time ago, I found a company’s website and looked around. It was my first visit. I didn’t fill out a form, request information, download something or ask anyone to contact me.
Then I received an email from the company.
I no longer have the email, so I can’t quote the exact wording, but the message was essentially, “I saw you were checking out our website.”
I ghosted them.
A first website visit can mean many things. Someone may be evaluating a vendor. They may also be casually exploring, opening several sites or spending a minute determining whether a company is relevant at all.
Technology may make it possible to identify that person immediately. But identifying someone earlier doesn’t mean the relationship has moved forward.
Technology can move identification earlier. It cannot unilaterally move the relationship earlier.
From the company’s perspective, I may have become an identifiable CEO who fit whatever criteria made me worth contacting. From my perspective, I hadn’t raised my hand. I may barely have established who they were.
In the language my kids would use, this is where a company can start acting cringe or looking sketch. The company may think it’s being attentive and responsive while the person receiving the communication is wondering why a business they barely know suddenly seems to know who they are.
Their technology detected my visit. The way they acted on that information ended my interest.
I didn’t complain. I didn’t explain. I simply disappeared.
No response doesn’t necessarily mean no reaction.
And that creates a measurement problem as much as a customer-experience problem.
Your Customer Is Interpreting You Too
Companies invest enormous resources trying to understand what customer behavior means. But the inference runs in both directions.
The company is making inferences about the customer.
The customer is making inferences about the company.
An unexpectedly relevant message, unusually well-timed email or communication that appears to know more than expected becomes information the customer interprets too. Customers increasingly have reasons to scrutinize communications that seem unusually informed.
That means a revenue system isn’t simply moving from data to action. A more complete chain looks like this:
Observed Behavior → Inferred Intent → Company Action → Customer Perception → Customer Reaction → Observable Outcome → Management Diagnosis
Notice what happens at both ends of that chain. The company observes something and tries to determine what it means.
At the beginning, it is interpreting customer behavior.
At the end, it is interpreting a customer outcome.
Neither interpretation is necessarily wrong. The risk begins when the company stops distinguishing what it observed from what it concluded.
Automation Scales the Judgment
Imagine technology identifies someone visiting a company’s website. The CRM enriches the record and, ten minutes later, an email arrives from Jimmy in Sales.
Jimmy may not have noticed the visit, interpreted the signals, written the email or pressed Send. Those decisions may have been made when the workflow was configured.
Automation doesn’t eliminate human judgment. It moves human judgment upstream.
Someone decided what signals matter, what the system should infer from them, when outreach should occur and whether a person should review it. Automation allows those judgments to be repeated at scale.
Timing itself can become a signal. A customer who has repeatedly visited, reviewed pricing and actively evaluated the company may interpret Jimmy’s email very differently from someone whose only interaction was a first visit ten minutes earlier.
The resulting experience doesn’t belong neatly to one department. Marketing may see better targeting. Sales may see an intent signal. RevOps may see an efficient workflow. Technology may see a system performing exactly as designed. The customer experiences the combined result.
Every component can appear to be doing its job.
Revenue Friction lives between the decisions.
When Accurate Data Leads to the Wrong Diagnosis
This is where the problem becomes an executive one.
Companies can track who clicks, replies, books an appointment, becomes an opportunity and ultimately buys. When technology contributes to a successful conversion, that outcome can often be observed and attributed.
The interested person who becomes uncomfortable and disappears creates a different problem.
The CRM may accurately record that the prospect didn’t respond. That is an observable outcome. But an observable outcome doesn’t necessarily reveal its cause.
Was the prospect never particularly interested? Did priorities change? Did a competitor win? Was the timing wrong? Or did something the company itself did change the prospect’s perception?
Unless the prospect provides that information, the organization may have to interpret the silence.
The CRM can accurately tell you that the prospect didn’t respond. It cannot necessarily tell you why.
That’s more than a measurement limitation. It can become a management problem.
If leadership interprets non-response as low intent, Sales may increase follow-up. If the original problem was that the company moved faster than the relationship, more follow-up may reinforce the very reaction leadership cannot see.
The data wasn’t necessarily wrong.
The diagnosis was.
That’s why this isn’t an argument against intent technology, automation, personalization or AI. Better information can create better experiences and more relevant conversations.
The issue is what happens between information and decision.
Garbage In, Garbage Out has been a technology principle for decades. But the input doesn’t have to be garbage for the output to go wrong. A website visit can be real. A click can be real. A non-response can be real.
The error can enter when the organization decides what that information means.
The question is whether the confidence of the company’s actions matches the strength of the evidence behind them.
Before asking what the next technology investment can identify, automate or convert, leadership may need to ask two other questions:
What are we treating as knowledge that is actually inference?
And:
What is happening in our revenue system that we can see the outcome of, but not the reason for?
Because while your technology is trying to determine what your customer’s behavior means, your customer is deciding what yours means too.