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Over the past several years, as we’ve worked with clients to apply Habit Lens across a wide range of therapeutic arenas and marketing challenges, one question surfaces with remarkable consistency: how do you actually measure the strength of a habit?
It’s a deceptively simple question. Because if you ask a physician or a patient directly -- “Is this a strong habit of yours?” -- you are unlikely to get a reliable answer. Not because they are unwilling, but because habits, by their nature, sit just out of range of conscious awareness. They are felt more than they are articulated.
And yet, if we are serious about understanding behavior -- and more importantly, impacting it -- we need to find a way to quantify just how deeply embedded that behavior really is.
This essay is an attempt to begin that conversation and to reflect on what we at ThinkGen have been learning from putting Habit Lens into practice. Note: this is by no means the final word. If anything, it is the first in what will likely be a series of reflections on how we might better quantify habit, not just as an academic exercise, but as a critical component of understanding (& quantifying) brand equity in the real world.
One of the most persistent misconceptions we encounter is that habit strength is simply a function of repetition. How often does someone do something?
Undoubtedly frequency of behavior certainly matters. But the academic literature has been clear for some time that habit is a more complex construct. Work such as Georgiev et al.’s Daily Habit Scale emphasizes that habits are defined not just by repetition, but by automaticity, resistance, and the extent to which behavior continues irrespective of consequences. Similarly, the Creature of Habit Scale distinguishes between routine, i.e., the structured, repeated nature of behavior, and automaticity, i.e., the degree to which behavior unfolds without conscious thought.
That distinction is more than just academic. A physician may frequently prescribe a specific medication and still be making a conscious, deliberative choice each and every time. We've seen that in qualitative research very clearly. Conversely, another physician behavior may be triggered almost reflexively by a patient cue, with little to no cognitive effort. From a behavioral standpoint, those are very different forms of habit; and they carry very different implications for what it might take to effect change.
This creates an inherent tension when it comes to conducting research investigation into habit. If habits are automatic, how do we measure them through self-report?
The answer is that we don’t measure them directly; we infer them.
Much like economists infer latent constructs through observable indicators, we look for patterns that indicate the presence of habit strength. Over the years, both academic tools like the Self-Report Habit Index (SRHI) and our own Habit Lens framework have converged on a similar insight: habit strength reveals itself through a constellation of signals, not a single answer.
Within Habit Lens, we tend to look at several dimensions in combination.
Perhaps the most important factor, however, is feedback. How quickly does feedback arrive? How clearly is it interpreted? Behaviors that generate immediate, unambiguous feedback tend to become habitual far more quickly than those where feedback is delayed or ambiguous. This is something we’ve seen repeatedly across therapeutic areas.
When these elements come together, they create what we think of as investment: a concept we adapted based upon Nir Eyal’s Hook Model.
Investment we define as the accumulation of effort, experience, and reinforcement that taken together establish and crystallize behavioral inertia. It is what transforms a repeated action into something more durable, more resistant to change.
In healthcare, investment often goes far beyond simple usage. A physician who is truly invested in a therapy may begin to layer additional behaviors on top of it. She may recommend it to colleagues, speak about it in peer forums, or train others on how to use it effectively. In more complex cases, she may even advocate for or help build the infrastructure required to administer that therapy within her practice.
At that point, the product is no longer just a choice. It has become embedded in how care is delivered. And that is a very different echelon of habit.
This is where marketers often get tripped up.
We tend to evaluate new products in relative terms: improved or superior efficacy, better safety, more convenient dosing, fewer drug interactions. But the real competition is not another product in isolation. It is an existing behavior that has been reinforced over time, supported by peers, validated by systems, and, in many cases, mastered. I just wrote an article last week about the power of mastery as a key contributor to behavioral inertia.
When we underestimate that level of investment, and mastery, we overestimate the ease with which behavior will change.
This is why some launches that look theoretically compelling might struggle to gain traction. Not because the product lacks merit, but because the existing habit is a lot stronger than anticipated.
If we accept this, then it follows that our measurement frameworks need to evolve. Habit must be added to the roster of KPIs.
Traditional KPIs, like awareness, recall, stated intent, remain necessary but not sufficient. They tell us whether a message has landed, but not whether behavior is changing in a reliable and durable way.
What we need to understand is whether a new product is becoming part of the physician’s automatic response.
These are the early signals of habit formation.
And they are far more predictive of long-term success than many of the metrics we currently rely on.
This is also where technology is beginning to change the equation.
With advanced analytic platforms like ThinkGen’s ThinkAEI, our team is increasingly able to synthesize multiple behavioral signals -- across surveys, qualitative inputs, and behavioral data, like claims data -- to build a more nuanced picture of habit strength. Rather than relying on a single proxy, we can now look at patterns across dimensions: reinforcement, automaticity, alignment, and investment.
In many ways, this brings us closer to measuring habit as it actually exists: in context, over time, and as part of a system.
If habits explain how behavior is sustained, then habit strength tells us how difficult it will be to change. That alone makes it necessary to measure -- and monitor.
But more importantly, it gives us a novel way to think about brand equity: not as a function of awareness, perceptions or beliefs alone, but as a function of how deeply a product is embedded in behavior.
This essay is a starting point. There is much more to explore. How habit strength evolves over time, how it varies across segments, and how it can be influenced more deliberately.
But if we want to understand behavior in healthcare -- and shape it in meaningful ways -- we need to move beyond what people say, and begin to measure what they actually do.
Repeatedly. Automatically. And with conviction.
Because that is where habit resides.
