Food and health innovation has a hand-off problem. Consumer insight helps explain what people want and do. R&D tests ingredients, mechanisms, and product performance. Marketing translates evidence into a proposition. But people do not experience a product through separate departments. They experience it within the context of their biology, routines, goals, and daily lives.
This article explores why the question “Does Qina sit in consumer insights or R&D?” may be too narrow, and how bringing consumer understanding, nutrition science, and behavioural expertise together earlier can help teams make better product decisions.
The person behind the persona
For years, innovation teams have used consumer personas to make audiences easier to understand. Demographics, attitudes, and purchasing behaviour still have value. They can help teams identify needs, preferences, and the situations in which a product might be considered. But they do not always explain why a solution works for one person and not another, or why someone stops using it after the first few weeks.
Nutrition research offers one reason to take individual context seriously. In the PREDICT 1 study, researchers observed substantial variation in post-meal glucose, insulin, and triglyceride responses among 1,002 UK adults eating the same meals. The findings were validated in a separate US cohort of 100 people. This demonstrates that responses to food can differ between individuals. It does not, by itself, show that a personalised product will improve long-term health or behaviour. Berry et al., Nature Medicine.
The practical question for an innovation team is not whether every product needs a detailed biological profile. It is whether the team understands the conditions in which its solution is likely to be useful. What is the person trying to change? What might make that behaviour easier or harder? Which nutritional or health factors are relevant to the product’s intended benefit? What evidence would show that it is working?
I think of the combination as a biological-behavioural persona: a working lens that brings together behaviour, goals, relevant biological context, and a person’s environment. It is a prompt for stronger research and product decisions, not a validated classification system or a substitute for clinical assessment. The distinction matters. A useful framework can guide questions without pretending to offer more certainty than the evidence allows.
More data is not the same as better insight
Wearables, health apps, and other digital tools can give teams information beyond a one-off survey or purchase record. Repeated observations may help researchers explore how behaviours and signals change over time. They can also create a more nuanced picture of the setting in which a product is used. But a stream of data is not automatically an insight, and a signal is not automatically a health outcome.
The value depends on the question being asked, the relevance and quality of the data, and how it is interpreted. Teams need to consider consent, privacy, representativeness, and whether different data sources can be meaningfully connected. A dashboard cannot repair a weak research question. Nor can an algorithm make an unsupported product claim credible.
There is a second risk: confusing measurement with understanding. A device might record a physiological signal, but it cannot explain on its own why a person changed their routine, whether the change was desirable, or what support would make it sustainable. Behavioural expertise helps teams interpret the human context around the data. A recommendation may be nutritionally plausible and still be difficult to follow if it ignores a person’s preferences, resources, motivation, or everyday constraints.
That is why personalisation should not be treated as a technology feature. Adding a wearable, AI tool, or health questionnaire does not make a solution meaningfully personalised by itself. The important test is whether the information helps a team make a better decision for a defined need, and whether the resulting product or support fits into someone’s life.
Put the connection into the product process
Bringing consumer and health research together should change more than the final marketing message. It should shape the brief, the evidence plan, and the way a product is tested. If the disciplines meet only after formulation or development is complete, the team may discover too late that the proposition is difficult to understand, use, or substantiate.
A more integrated process starts by agreeing on the intended user and outcome. Teams can then make explicit what is established, what remains a hypothesis, and what evidence would change the decision. Consumer researchers, nutrition scientists, product developers, and behavioural specialists should help frame that work together, rather than pass a partially formed brief from one function to the next.
The team can also define what success means before choosing the tools. Is it comprehension, adoption, continued use, a measurable nutritional change, or another outcome? Those are different questions and may require different methods. Matching the evidence plan to the intended benefit helps prevent teams from relying on an attractive metric that does not answer the question they actually care about.
This approach asks consumer insight to do more than inform messaging, and R&D to do more than validate a formulation at the end. Both can help define the problem, identify relevant evidence, and set realistic conditions for success from the start. It also gives marketing a stronger foundation: a proposition built around what a product can responsibly support, rather than what sounds most compelling in isolation.
The Qina take
The challenge is not to replace consumer insight with biological data, or to ask R&D to solve every behavioural question. It is to connect the disciplines around a shared decision: what is likely to work for whom, under which conditions, and how will we know?
That shift changes the role of a persona. Instead of being a static description used mainly to target a message, it becomes a practical hypothesis about the interaction between a person, a product, and their context. Teams can use it to decide what to research, what to build, what to test, and what they can responsibly promise. It should evolve as evidence improves, not harden into a label.
For food, nutrition, and digital health leaders, the commercial implication is straightforward: integration belongs at the beginning of product development, not at the hand-off between research and marketing. When consumer needs, nutrition science, behaviour, and product decisions inform one another early, teams have a stronger basis for building solutions that are relevant and evidence-informed. That is where I see my work: not neatly inside one silo, but at the point where those disciplines meet.
If your team is developing a food, nutrition, or digital health solution, where do consumer insight and R&D meet in your process today?
References
Berry, S. E. et al. “Human postprandial responses to food and potential for precision nutrition.” Nature Medicine, 2020. Read the study.
By Mariette Abrahams PhD MBA
Food and health innovation has a hand-off problem. Consumer insight helps explain what people want and do. R&D tests ingredients, mechanisms, and product performance. Marketing translates evidence into a proposition. But people do not experience a product through separate departments. They experience it within the context of their biology, routines, goals, and daily lives.
This article explores why the question “Does Qina sit in consumer insights or R&D?” may be too narrow, and how bringing consumer understanding, nutrition science, and behavioural expertise together earlier can help teams make better product decisions.
The person behind the persona
For years, innovation teams have used consumer personas to make audiences easier to understand. Demographics, attitudes, and purchasing behaviour still have value. They can help teams identify needs, preferences, and the situations in which a product might be considered. But they do not always explain why a solution works for one person and not another, or why someone stops using it after the first few weeks.
Nutrition research offers one reason to take individual context seriously. In the PREDICT 1 study, researchers observed substantial variation in post-meal glucose, insulin, and triglyceride responses among 1,002 UK adults eating the same meals. The findings were validated in a separate US cohort of 100 people. This demonstrates that responses to food can differ between individuals. It does not, by itself, show that a personalised product will improve long-term health or behaviour. Berry et al., Nature Medicine.
The practical question for an innovation team is not whether every product needs a detailed biological profile. It is whether the team understands the conditions in which its solution is likely to be useful. What is the person trying to change? What might make that behaviour easier or harder? Which nutritional or health factors are relevant to the product’s intended benefit? What evidence would show that it is working?
I think of the combination as a biological-behavioural persona: a working lens that brings together behaviour, goals, relevant biological context, and a person’s environment. It is a prompt for stronger research and product decisions, not a validated classification system or a substitute for clinical assessment. The distinction matters. A useful framework can guide questions without pretending to offer more certainty than the evidence allows.
More data is not the same as better insight
Wearables, health apps, and other digital tools can give teams information beyond a one-off survey or purchase record. Repeated observations may help researchers explore how behaviours and signals change over time. They can also create a more nuanced picture of the setting in which a product is used. But a stream of data is not automatically an insight, and a signal is not automatically a health outcome.
The value depends on the question being asked, the relevance and quality of the data, and how it is interpreted. Teams need to consider consent, privacy, representativeness, and whether different data sources can be meaningfully connected. A dashboard cannot repair a weak research question. Nor can an algorithm make an unsupported product claim credible.
There is a second risk: confusing measurement with understanding. A device might record a physiological signal, but it cannot explain on its own why a person changed their routine, whether the change was desirable, or what support would make it sustainable. Behavioural expertise helps teams interpret the human context around the data. A recommendation may be nutritionally plausible and still be difficult to follow if it ignores a person’s preferences, resources, motivation, or everyday constraints.
That is why personalisation should not be treated as a technology feature. Adding a wearable, AI tool, or health questionnaire does not make a solution meaningfully personalised by itself. The important test is whether the information helps a team make a better decision for a defined need, and whether the resulting product or support fits into someone’s life.
Put the connection into the product process
Bringing consumer and health research together should change more than the final marketing message. It should shape the brief, the evidence plan, and the way a product is tested. If the disciplines meet only after formulation or development is complete, the team may discover too late that the proposition is difficult to understand, use, or substantiate.
A more integrated process starts by agreeing on the intended user and outcome. Teams can then make explicit what is established, what remains a hypothesis, and what evidence would change the decision. Consumer researchers, nutrition scientists, product developers, and behavioural specialists should help frame that work together, rather than pass a partially formed brief from one function to the next.
The team can also define what success means before choosing the tools. Is it comprehension, adoption, continued use, a measurable nutritional change, or another outcome? Those are different questions and may require different methods. Matching the evidence plan to the intended benefit helps prevent teams from relying on an attractive metric that does not answer the question they actually care about.
This approach asks consumer insight to do more than inform messaging, and R&D to do more than validate a formulation at the end. Both can help define the problem, identify relevant evidence, and set realistic conditions for success from the start. It also gives marketing a stronger foundation: a proposition built around what a product can responsibly support, rather than what sounds most compelling in isolation.
The Qina take
The challenge is not to replace consumer insight with biological data, or to ask R&D to solve every behavioural question. It is to connect the disciplines around a shared decision: what is likely to work for whom, under which conditions, and how will we know?
That shift changes the role of a persona. Instead of being a static description used mainly to target a message, it becomes a practical hypothesis about the interaction between a person, a product, and their context. Teams can use it to decide what to research, what to build, what to test, and what they can responsibly promise. It should evolve as evidence improves, not harden into a label.
For food, nutrition, and digital health leaders, the commercial implication is straightforward: integration belongs at the beginning of product development, not at the hand-off between research and marketing. When consumer needs, nutrition science, behaviour, and product decisions inform one another early, teams have a stronger basis for building solutions that are relevant and evidence-informed. That is where I see my work: not neatly inside one silo, but at the point where those disciplines meet.
If your team is developing a food, nutrition, or digital health solution, where do consumer insight and R&D meet in your process today?
References