Steve Wakefield leads the Quantitative Center of Excellence at Fulcrum Research, part of SAI. With more than 25 years of market research experience, he has seen ATU programs evolve from relatively conventional tracking studies into something with a much more strategic role. 

We spoke with Steve about why the ATU sits at the heart of understanding brand performance, what separates a useful tracker from an “assembly-line” study, and why Fulcrum is building adaptability directly into its approach. 

Why are ATUs so important to a life sciences brand? 

Steve Wakefield: I think of the ATU as being at the center of the brand. It tells you how the brand is performing and how that performance is changing over time. 

Are people becoming more aware of the brand? Are they using it more? If they’re not using it, why not? Are sales representatives reaching the right targets? 

Those are core metrics for a brand. 

And once you understand them, other research naturally follows. An ATU might uncover something that needs deeper qualitative exploration, for example. That’s one of the reasons it’s such an important foundation for a research program. 

We’ve worked on some ATUs that have been running for over a decade and dozens of waves. That longitudinal perspective can be incredibly valuable. 

Lots of research companies offer trackers. What makes a good ATU different? 

There is an approach to ATU research that is essentially an assembly line. You establish the metrics, repeat the process efficiently and report what changed. 

There’s a place for efficiency, but our emphasis is on the quality and strategic value of the research. 

That means really understanding the brand and therapy area rather than simply asking the questions. The objective isn’t just to tell the client that a metric moved. It’s to help them understand what that movement means for the brand. 

I think clients increasingly expect that. Market research teams want partners who can bring strategic thinking to the brand rather than simply check the research box. 

Is that what you mean by an “Adaptive ATU”? 

Exactly. A brand evolves. The marketplace changes. Competitors enter. New data emerges. The questions the marketing team is asking change. 

If the ATU remains exactly the same through all of that, eventually it becomes out of sync with the brand. 

The Adaptive ATU is our way of protecting the longitudinal value of tracking while deliberately creating room for change. 

How does that work in practice? 

Think about a 30-minute questionnaire. 

You might devote around 20 minutes to the core ATU metrics that need to remain consistent and trendable. Then perhaps 10 minutes becomes an adaptive module—a sandbox that can address the most important question for that particular wave. 

Sometimes you’ll decide that module is still important and continue tracking it. But another time a competitor may be launching, for example, and you want to use that space to investigate the implications in much greater depth. 

The point is that you don’t necessarily have to commission an entirely separate study every time the business has a new question. You’ve deliberately built some flexibility into the tracker. 

We’ve found clients respond very positively to that idea because it keeps the program fresh and relevant. 

So you’re not sacrificing consistency for flexibility? 

No. The core is still the core. 

You need stable measures of things like awareness, familiarity, consideration, trial and usage because otherwise you lose the ability to understand change and progress over time. 

What we’re saying is: identify what genuinely needs to be tracked, protect it, and then don’t allow everything else to become permanently embedded in the questionnaire just because it was useful once. 

You want the ATU to evolve with the brand. 

Where does AI fit into this? 

There are several opportunities. 

One we’re already finding particularly useful is Synthetic Users in questionnaire development. We can take a draft questionnaire and pressure-test it from the perspective of a particular type of healthcare professional. 

Are we missing an important response option? Is a question worded in a way that makes sense from a physician’s perspective? Is there something about the category we haven’t considered? And with patient research, is the language understandable? 

It doesn’t replace primary research or longitudinal measurement. It’s an exploratory tool that can make us better prepared and help us design stronger research. 

We can also use Synthetic Users between waves to explore hypotheses or help us think through an unexpected result before determining what needs to happen next. 

You’re also looking beyond AI as a research tool to AI as part of the market itself, aren’t you? 

Yes. That’s another important development. 

Physicians and patients increasingly have access to AI-powered tools when they’re looking for information. So an interesting new question for brands is: what are those tools actually saying about the disease, the treatments and the competitive landscape? 

Which brands appear? How are different treatments positioned? Is the information accurate and balanced? Does it change after new data, an approval or a competitor event? 

That becomes another signal that can sit around the ATU. The tracker still gives you validated longitudinal measurement, while these additional sources can help identify emerging narratives and hypothesize why perceptions are changing. 

What role does competitive intelligence play? 

It helps provide context. 

Imagine the ATU tells you that consideration has declined among a particular segment or that perceptions of a competitor have changed. 

That’s important—but you then want to understand what happened around that movement. 

Was there new competitor data? A label change? Different messaging? A change in field-force activity or share of voice? 

Connecting the tracking data to what’s actually happening in the market makes the findings much more interpretable and actionable. 

You also talked about data quality as an increasingly important issue. 

It’s critical. 

Most of our quantitative work uses panels, and one of the realities of the industry is that there are bad actors trying to get into surveys. 

So quality isn’t simply about finding respondents; it’s also about identifying respondents who shouldn’t be in the data. A bad respondent is worse than no respondent. 

We look for multiple signals rather than relying on one rule. Completion time might be one flag. Open-ended responses might provide another. Are respondents pasting answers into an open end (we can disallow that)? AI can also help us identify suspicious patterns or assess cases where the research team is uncertain. 

Clients don’t necessarily see all of that work because it happens behind the scenes. But they certainly see the consequences when poor-quality data gets through. 

I’d rather take longer to field a study and know we have protected the integrity of the data. 

Are there particular therapy areas where this approach is especially valuable? 

We do a lot of work in vaccines, rare diseases and oncology. 

Rare disease is a particularly good example because you’re often trying to reach difficult-to-find patients and physicians treating relatively small populations. Oncology and other high-science categories also require genuine therapeutic understanding. 

These aren’t markets where you want to approach the research mechanically. You have to understand the category well enough to know what questions matter and what the findings mean commercially. 

What does the ideal relationship with a client look like? 

Dialogue is really important. 

Typically we’re working with a market research or insights team, with the marketing or brand team as the end customer. We want the opportunity to engage with both. 

If the brand is evolving, competitors are entering and the marketplace is changing, we need to understand those conversations so the ATU can evolve too. 

The worst outcome is to repeat exactly the same tracker wave after wave until somebody eventually says, “This doesn’t reflect our business anymore—we need to start again.” 

I’d much rather build evolution into the program from the beginning. 

If you had to summarize the philosophy behind the Adaptive ATU in one thought, what would it be? 

Keep what needs to be stable, but create room for what needs to change. 

An ATU should give you reliable longitudinal measurement, but it should also help you answer the questions that matter to the brand right now. 

The best trackers don’t just tell you what happened. They help you understand why it happened, what you need to investigate next, and ultimately what the brand should do about it.