Everyone Says They Do “Implicit.”
Icebergs… what lurks below that surface?
Everyone Says They Do “Implicit.”
What That Actually Means, and What Clients Should Ask Before They Buy
Over the last decade, implicit has become one of the most overused (and least interrogated) words in consumer and sensory research.
Vendors promise access to the “unconscious,” “System 1,” or “what consumers can’t tell you.” Clients, understandably, are intrigued. After all, we know people often struggle to articulate why they like something, why they choose one product over another, or why they behave inconsistently with what they say.
But here’s the uncomfortable truth:
Many companies that say they “do implicit” are either using very different definitions of the term or outsourcing the actual implicit work to a third party behind the scenes.
Neither of those is inherently bad.
What is risky is when clients don’t know which is happening.
This post is about:
The different types of vendors that claim implicit capability
Why third-party “backroom” implicit vendors are common
What that means for data quality, interpretation, and ownership
The questions clients should ask before commissioning implicit work
And how working with an independent advocate or “research doula” can help you get real value, not just fancy outputs.
First: “Implicit” Is Not One Thing
Before talking about vendors, it’s critical to clarify that implicit can mean very different things in practice.
Broadly, most offerings fall into one of four buckets:
1. True Implicit Association Measures
These are rooted in cognitive psychology and rely on reaction time differences to infer associative strength (e.g., brand ↔ attribute, product ↔ emotion).
Examples include:
IAT-style tasks
Go / No-Go Association Tasks
Other inhibition- or speed-based paradigms
These are the closest thing we have to measuring automatic mental associations.
2. Fast or Forced Choice (But Still Explicit)
Paired comparisons, binary choices, or rapid responses can reduce scale bias and overthinking, but they are still conscious decisions.
They are useful, but they are not implicit in the cognitive sense.
3. Behavioral or Physiological Proxies
Eye tracking, facial coding, GSR, heart rate, etc. These capture responses people don’t explicitly report, but they do not measure associations.
They answer different questions:
Attention
Arousal
Engagement
Processing load
4. “Implicit” as a Marketing Term
Sometimes “implicit” is used to mean:
Less leading questions
Better study design
Smarter analysis
Or simply “not a Likert scale”
This is where confusion (and risk) creeps in.
The Vendor Landscape: Who’s Actually Doing What?
First, there are vendors in the market who genuinely specialize in implicit, association-type methods, including those commonly used in sensory, CPG, and brand research. Over time, a small ecosystem has developed around these tools, and many are both thoughtful and rigorous in how they apply them.
Where things get complicated is that not all vendors who say they “do implicit” are actually doing the same thing… or even doing it themselves.
In practice, there’s an important distinction between firms that own and operate their implicit technology, those that partner with a third-party specialist to run the implicit component, and those that use fast or forced explicit methods but describe them as implicit. From the outside, these approaches can look very similar. From a methodological and interpretive standpoint, they are not.
It’s especially common, particularly in sensory and full-service MRX, for firms not to run implicit tools in-house at all. Instead, they design the broader study and outsource the implicit portion to a specialized provider, sometimes in the background. This is often a practical decision. Association-based implicit methods can require millisecond-level timing precision, proprietary scoring algorithms, and careful task construction to avoid introducing noise or bias. Building and maintaining that capability internally isn’t trivial.
This model isn’t inherently problematic. In fact, it can work very well when roles are clear and communication is transparent.
The challenge is that clients aren’t always aware of how the work is actually being done. They may not know who is collecting the implicit data, how standardized versus customizable the task is, or whether the team interpreting the results has deep familiarity with the underlying method, or is primarily relying on a vendor-supplied output.
That lack of visibility matters, because implicit data is rarely self-explanatory. Its value depends heavily on design choices, context, and interpretation. When those details are hidden, it becomes harder for clients to ask the right questions, challenge assumptions, or understand the limits of what the data can (and cannot) tell them.
For that reason, I’m intentionally not calling out specific vendors in this post. The goal here isn’t to rank or critique individual companies in public, but to help clients understand the landscape well enough to engage more confidently and critically. If you’re evaluating a particular vendor or approach and want to talk through how they handle implicit work, what’s in-house, what’s partnered, and what that means for your project, I’m always happy to do that in a one-on-one conversation.
Implicit research works best when it’s built on trust. But trust is strongest when it’s paired with clarity.
Why the “Backroom Vendor” Model Exists
There are actually very good reasons this model exists in the first place. Implicit tools, especially association-based ones, are technically complex, expensive to build, and even more expensive to validate properly. Many insights firms choose to remain tool-agnostic by design, focusing on study design, storytelling, and client relationships rather than owning and maintaining specialized platforms. And in truth, there are specialists who do this work extremely well.
I know this because I’ve spent a good portion of my career working in those backrooms.
I’ve designed and helped build implicit platforms that never carried my name, or sometimes even the name of the firm ultimately selling the work. I’ve partnered with MRX teams who wanted to offer implicit research responsibly but didn’t want to reinvent the wheel. In those cases, the separation of roles was clear, the collaboration was strong, and the end result served the client well.
So the risk here is not outsourcing.
The risk is opacity.
Problems arise when clients are led to believe, “My vendor does implicit,” when the reality is closer to, “My vendor subcontracted an implicit module they don’t fully control or explain.” When that gap exists, interpretation becomes fragile. Accountability becomes diffuse. And trust, both in the data and in the relationship, can quietly erode.
Implicit data requires context. It requires understanding how the task was constructed, what assumptions are baked into the scoring, and where the method is strong versus fragile. When those details are hidden or glossed over, even well-intentioned research can lose its footing.
The takeaway isn’t that this backroom model is something to fear. It’s that transparency matters. When everyone understands who built what, who ran what, and who is responsible for interpretation, implicit research can be incredibly powerful. When they don’t, it becomes much harder for clients to use the results with confidence, or defend them internally.
That’s the difference between implicit research that informs decisions and implicit research that simply looks impressive on a slide.
What This Means for Client-side
Implicit data is rarely self-explanatory. Unlike many explicit measures, it doesn’t come with built-in intuition about what a number “means.” Its value depends heavily on how the task was designed, which stimuli were chosen, and what assumptions were made long before a single participant ever clicked a button. Small decisions early on (often invisible to the client) can meaningfully shape the patterns that emerge in the data.
This is also why implicit results are so easy to over-interpret. When outputs arrive as clean scores or rankings, there’s a temptation to treat them as definitive signals rather than probabilistic indicators of association strength. Without context, nuance can disappear quickly, and implicit measures can be asked to answer questions they were never designed to address.
That’s where guidance matters.
Having someone involved who understands both the methodological mechanics and the business question (whether that’s an internal expert, a highly collaborative vendor, or an independent advisor like a research doula 😉) can make the difference between insight and confusion. This kind of partner helps shape the design so the method actually matches the question, and later helps interpret the results in a way that’s responsible, defensible, and useful.
In other words, implicit research is less about the tool itself and more about stewardship. When it’s treated as a black box, it’s easy to misuse. When it’s treated as a shared responsibility, designed thoughtfully, interpreted collaboratively, and integrated with other data, it becomes a powerful complement to explicit research rather than a risky replacement.
That’s the difference between implicit data that merely looks sophisticated and implicit insight that genuinely informs decisions.
The Questions Clients Should Ask (and Be Encouraged to Ask)
If you’re considering implicit research, here are questions you should feel comfortable asking. And, honestly, vendors should feel comfortable answering.
About the Method
What type of implicit measure is this, exactly?
Is it association-based, behavioral, or physiological?
What cognitive mechanism is it designed to tap?
About Ownership & Execution
Is this tool run in-house, licensed, or provided by a third party?
Who designed the task?
Who controls stimulus selection and timing?
About Outputs
What is the actual metric produced?
How should it not be interpreted?
How stable or noisy is this measure?
About Validation
Has this method been validated internally or externally?
What kinds of questions does it work poorly for?
About Integration
How will implicit data be combined with explicit data?
Will modeling, segmentation, or advanced stats be used to reconcile divergence?
What happens if implicit and explicit disagree?
If a vendor struggles with these questions, or treats them as “too technical”, that’s a red flag.
Red Flags to Watch For 🚩
Speaking of which… Here are some patterns that should prompt caution:
“It tells you what consumers really think.”
“It accesses the unconscious directly.”
No explanation of reaction time, inhibition, or task mechanics
No discussion of limitations
Implicit results presented without explicit context
One-size-fits-all tasks regardless of research question
Overconfident causal claims from associative data
Implicit research should add nuance, not certainty.
Why More Customized, Collaborative Work Delivers More Value
When I look back at the implicit projects that have truly worked, the ones clients still reference months or years later, they tend to share a common thread. They were never templated or plug-and-play. Instead, they were built deliberately around a specific question, context, and decision the client needed to make.
In these projects, implicit data wasn’t treated as a final answer or a verdict. It was used as a way to surface patterns, tensions, and possibilities, often generating new hypotheses rather than closing the book on a question. Those signals were then grounded by pairing them with well-designed explicit measures, allowing teams to explore not just what aligned, but where things diverged and why.
The real insight often emerged in that space between measures. Thoughtful analytics helped connect the dots, showing when implicit and explicit data reinforced one another and when they told different stories. And critically, those projects were collaborative. Clients, vendors, and method experts stayed in close conversation throughout, adjusting design choices, sense-checking interpretations, and resisting the urge to oversimplify complex signals.
This kind of work does take more time than off-the-shelf research. It requires more discussion, more iteration, and more shared responsibility. But in return, it delivers something far more valuable: insight that is defensible, nuanced, and genuinely actionable, rather than just fast and impressive-looking.
Where a “Research Doula” Comes In
This is where someone like me fits into the process.
A research doula sits alongside the client, not to replace vendors, but to help clients get the most out of those relationships. Often that starts upstream, by helping teams understand what’s genuinely possible with implicit methods, where the risks lie, and how to frame the right questions before a vendor is ever selected. A small shift in how a question is posed at the beginning can prevent much bigger problems later on.
Along the way, I act as a translator between cognitive science and MRX language. That means helping clients understand what a method is actually measuring, and helping vendors understand the decision the client is trying to make. When tools, claims, or outputs start to feel fuzzy, my role is to slow things down, ask the uncomfortable but necessary questions, and advocate for clarity.
The same support matters on the back end of a project as well. Implicit results need careful interpretation and thoughtful communication, especially when they’re being shared with internal stakeholders who may be tempted to overgeneralize or oversimplify. Helping teams tell a responsible, defensible story with the data is often just as important as collecting the data itself.
Most importantly, my role is to help clients trust without being naïve. Vendor relationships are, at their core, trust-based, and they should be. But trust works best when it’s paired with understanding. When clients feel informed and confident, they’re better partners, better consumers of insight, and ultimately better decision-makers.
That’s the value of having someone in your corner who understands both the science and the reality of how research gets done.
The Takeaway
Implicit research is powerful when:
It’s used for the right questions
It’s designed and interpreted with care
It’s integrated… not isolated
And when clients are empowered, not dazzled
If you’re considering implicit work, don’t just ask who can run it.
Ask: “Who will help me make sense of it and protect me from misusing it?”
That’s where real insight lives.
