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To this day, I can still vividly remember the first time I saw The Matrix. One of its defining innovations was the way it slowed time to a near standstill, allowing the camera to move around the action and reveal it from multiple angles. Whether it was Trinity’s mid-air kick or Neo dodging bullets, what would normally pass in an instant became visible -- frame by frame. Movement, stripped of its speed, revealed its underlying structure.
I’ve found myself ruminating about, and being inspired by, this cinematic hallmark (that has been frequently copied in the years since) when reflecting on how we study new product adoption in pharma.
Too often, primary marketing research treats new product adoption as if it occurs in a single step. We present a target product profile (TPP), ask what stands out, probe on advantages and disadvantages, and then move quickly to intent: would you use it, and if so, how? At times, we introduce patient types to create the appearance of specificity. But in reality, the process is reduced to a set of broad questions that assume a clean, rational progression from awareness to action -- one that rarely reflects how adoption actually unfolds.
But that is not how adoption actually works.
In reality, adoption is a sequence of small, interdependent steps. It is shaped by context, friction, reinforcement, and time. And if we want to understand whether a product will truly be used -- not just considered -- we need to slow that process down and examine it frame by frame.
Traditional product testing relies heavily on abstraction. Survey respondents are asked to evaluate a profile as if they are operating outside of their own practice environment, weighing attributes in isolation. This approach assumes that behavior is driven primarily by conscious evaluation.
But real-world behavior does not unfold in that way.
It is embedded in context: within systems, embedded routines, digital environments, and other rules and constraints that shape what is possible. A physician does not simply decide to use a product. They encounter it, interpret it, test it cautiously, and, if conditions allow, begin to incorporate it into their routine. Each of these steps is influenced by factors that extend well beyond the product itself.
The more useful question, therefore, is not “what do you think?” but rather: “what would actually have to happen for this to be used?”
One of the most revealing ways to begin this inquiry is by exploring the velocity of adoption. Does the product connect to something immediate? Is there a reason to use it now, or does it fall into the category of “interesting, but not urgent”?
This distinction is more consequential than it might initially appear. In practice, many products do not fail because they lack appeal; they stall because they lack immediacy. They do not connect to a specific patient, a pressing clinical moment, or a recognizable use case that triggers action.
Nir Eyal’s Hooked model begins with the concept of a trigger: a cue that initiates behavior. In the context of pharmaceutical adoption, that trigger is often situational: a patient who does not respond as expected, a clinical edge case, or a moment where existing options feel insufficient. Without that trigger, even strong products can remain in a state of passive consideration.
With it, behavior can begin to move.
Once that sense of immediacy is established, the next step is to map the path from awareness to first use -- not in theory, but in practice. This is where many research approaches fall short.
A physician learns about the product. Then what?
At some point, the product must transition from concept to system. It must be available to prescribe, supported by coverage, and understood by the staff who will help operationalize it. Each of these steps introduces the potential for friction, and when explored in detail, what often emerges is just how many steps there actually are.
Far more than we tend to assume.
At a certain point, it becomes useful to step back and examine the process as a whole. When viewed from a distance, the question becomes less about features and more about effort.
Is this worth it?
For the respondent, this is not an abstract comparison of advantages and disadvantages. It is a practical calculation. Given the number of steps required, the uncertainties involved, and the effort needed to navigate the system, is there something about this product that makes the change feel justified?
In other words, is the juice worth the squeeze?
This is where many products encounter resistance. Not because they lack clinical merit, but because the pathway to using them is disproportionately complex relative to the benefit they deliver. HCPs are not simply asking whether a product is better; they are asking, often implicitly, whether it is worth the effort required to change.
This becomes especially challenging in crowded, well-served therapeutic categories, where current approaches are viewed as "good enough." In these situations, inertia is strong. The bar for adoption is not incremental improvement, but meaningful advantage -- enough to overcome the friction, uncertainty, and disruption associated with doing something new. Without that, even strong products can struggle to gain traction.
One of the most effective ways to ground this discussion is to move away from hypothetical scenarios and instead examine recent, real-world behavior. Asking respondents to walk through a recent experience of adopting a new product can provide a level of detail that is otherwise difficult to access.
Equally important is understanding where the process nearly broke down.
Delving into these narratives reveal not just what could happen, but what actually does happen when behavior changes.
The first use of a product is not the end of the journey; it is the beginning of a feedback loop. Behavioral science consistently shows that repetition depends on reinforcement, and the quality and timing of that reinforcement are critical.
In healthcare, feedback is often delayed. It may depend on follow-up visits, lab results, or patient-reported outcomes. But when it arrives, it carries significant weight. Positive early experiences build confidence and reduce perceived risk, making subsequent use more likely. Negative or ambiguous outcomes can stall adoption entirely.
This is where the early trajectory of a product is often determined.
Closely tied to reinforcement is the role of belief. Before using a product, HCPs develop a set of expectations about how it will perform, where it will fit within their treatment approach, and what it will demand operationally. These expectations are constructed over time -- shaped by clinical data encountered at conferences, prior experience, informal peer exchanges, and an increasingly complex informational environment.
Nir Eyal’s work offers an important perspective here. In Hooked, he describes how repeated behaviors reinforce internal beliefs, making those behaviors more likely to persist over time. More recently, in his thinking on belief formation, including Beyond Belief, he expands on this idea, suggesting that our perception of reality is constructed through the beliefs we hold -- and that those beliefs often are only reshaped when confronted with credible, experiential evidence.
This dynamic is highly relevant in new product adoption.
When a product is used for the first time, it is not simply being evaluated -- it is being tested against an existing belief system. Physicians are asking, often implicitly: does this align with what I expect to be true?
In this sense, adoption is not just about trial. It is about the gradual updating of belief, grounded in experience.
No adoption journey happens in isolation. Within a medical practice, multiple participants influence whether a product moves forward or stalls. Nurses may be responsible for patient education, reimbursement coordinators determine whether access is feasible, and office staff shape how easily a product fits into existing workflows.
But increasingly, this system extends beyond people alone. It is embedded within a broader digital ecosystem that now serves as the operating environment of care. As I’ve argued previously, the EHR has effectively become the context in which medical decisions are made. How a product appears -- or fails to appear -- within that system can materially influence whether it is considered at all.
Order sets, default pathways, alerts, and prescribing interfaces all play a role in shaping behavior. If a product is not easily accessible, not intuitively integrated, or requires additional navigation, that friction becomes part of the adoption calculus. In that sense, the digital environment is not passive. It is an active participant in reinforcing, or inhibiting, behavior.
Each of these roles can either facilitate or hinder adoption. In many cases, the physician’s intent is necessary, but not sufficient. If the broader system does not support the change, the behavior is unlikely to persist.
Understanding this dynamic requires looking beyond the individual and examining the practice as a system.
Taken together, this approach represents a meaningful shift in how we think about TPP testing. It moves us away from abstract questions about drivers and barriers and toward a detailed reconstruction of the adoption pathway.
It is a more demanding approach. It requires deeper probing, more time, and a willingness to follow the logic of behavior wherever it leads.
But it yields something far more valuable: a clear view of how adoption actually happens. Where it accelerates, where it stalls, and what it takes to make it repeat.
Returning to the idea of slowing things down a la The Matrix, this is ultimately what Habit Lens is designed to do. It takes something that appears instantaneous and reveals its underlying structure.
Because essentially, the success of a new product is not determined by how it is evaluated in isolation. It is determined by whether it can navigate the real-world pathway from awareness to action; and then from action to repetition.
That pathway is rarely simple. But it is always there, waiting to be understood, one frame at a time.
