Most companies no longer need convincing that moments matter.
We know consumers don’t make choices in a vacuum. They enter them through situations, needs, occasions, and Category Entry Points. “I’m tired.” “I need something for movie night.” “I want a quick pick-me-up.” “I need something easy to share.” These moments trigger the category in the consumer’s mind and shape which brands, products, packs, and prices feel relevant.
But while the industry has embraced moment-based thinking, many research methodologies have not caught up. Hence, the real question is not whether moments matter. It is whether our research methods capture how moments shape choice.
Choice-based methods such as MaxDiff and choice-based conjoint are effective at identifying growth opportunities in established categories, but they require adaptation. When they rely on the conventions of traditional survey design rather than on how consumers decide in real life, the opportunities embedded in specific occasions are easily missed. The method is valid. It is the design that needs to evolve.
Ultimately, identifying pockets of category growth depends not just on understanding occasions, but on using research designs that allow those occasions to meaningfully shape consumer choice.
Consider MaxDiff first, a tool used for early-stage idea screening. MaxDiff can quickly identify the strongest and weakest ideas across a large set with low respondent burden and high discrimination.
A common way of building moments into the design is to run a separate MaxDiff for each one. To see what this approach yields in practice, it is worth looking at the output of a sequential design. The example below shows results from a sequential MaxDiff fielded across several moments. As expected, differences between the moments do emerge. Certain brands index more strongly in one moment, others in another, confirming that preference is not uniform across occasions.
What stands out, however, is the magnitude of these differences. They are consistently smaller than one would anticipate for a category whose usage genuinely varies from one occasion to the next. The signal is present, but it appears muted, and that compression is itself worth examining because it points less to the behaviour of consumers than to the design through which it was measured.
Now consider a parallel study using an occasion-based design. Rather than treating each moment as a separate exercise, this approach asks the respondent to evaluate the options across several occasions within the same task. For each occasion in turn, the respondent indicates which option they would prefer, so the same set of alternatives is judged repeatedly against different occasions side by side. This keeps the occasion in view at the moment of choice and makes the comparison between occasions explicit rather than leaving it to be reconstructed afterwards.
With this approach, the differences between moments emerge far more clearly. Because the respondent evaluates the options against each occasion in turn, the contrasts that the sequential design had compressed become visible. Preferences that looked broadly similar across moments now have bigger differences, and the brands or options best suited to a particular occasion stand out more distinctly from those that are not. The result is a sharper picture of how relevance shifts from one occasion to the next, which is precisely the kind of differentiation that occasion-level strategy depends on.
Why the design shapes the answer
In real life, choice begins with a cue. A consumer feels tired after work, prepares for a night in, is heading to the gym, or is having friends over. That cue activates a category in the consumer’s mind, and a set of relevant alternatives then comes to mind.
The decision unfolds in a consistent order:
Moment → Category → Alternatives → Choice
When the same person answers a survey, this order can be reversed. Under time and effort constraints, respondents tend to adopt low-effort strategies, relying on shortcuts and heuristics. Rather than processing the question fully, they take their cues from the most visible feature of the task, which is usually the set of answer options in front of them.
Survey methodology research describes this as the informative function of response alternatives.¹ Response options do not simply record an answer. They also help the respondent infer what the question is asking.
The result is that the decision order can shift:
Category → Inferred Moment → Alternatives → Choice
In real life, the moment activates the alternative set. In many surveys, the alternatives prompt the respondent to infer a moment instead. So even when the intended occasion is stated clearly, respondents may take their lead from the alternatives and answer for a different moment than the one in question.
The answer options shape how respondents interpret the question, and many base their choice on the moment the alternatives bring to mind.
There is a second reason the traditional sequential setup is less suited to identifying opportunities across moments, and it concerns fatigue and consistency of focus.² Running MaxDiffs back to back tends to introduce cognitive drift. As the tasks accumulate, respondents simplify their choice strategy through mental fatigue or pattern responding. Having worked carefully through the first MaxDiff, many give less considered answers to the second, so later moments are measured with less precision than earlier ones.
The same logic applies to conjoint.
A standard conjoint identifies which pack, price, brand, or feature performs best overall. An occasion-based conjoint goes further by showing how preferences change across usage contexts.
This reveals opportunities that traditional analyses miss: a pack that succeeds in one occasion but not another, a smaller format that unlocks an underserved moment, or where pricing can be stretched, protected, or risks cannibalising demand.
The result is a more actionable foundation for portfolio strategy. Rather than asking which product wins overall, the focus shifts to which product, pack, and price win in which moments, and where the category is underserving demand.
Results from occasion-based conjoint
That is the promise of occasion-based methodology. It moves beyond average preference to identify incremental growth opportunities. To achieve this, moments must be embedded directly into the choice task. Otherwise, respondents are likely to anchor their decisions on the alternatives presented rather than the occasion itself.
That is the promise of occasion-based methodology. It moves beyond average preference to identify incremental growth opportunities. To achieve this, moments must be embedded directly into the choice task. Otherwise, respondents are likely to anchor their decisions on the alternatives presented rather than the occasion itself.
"We’ve applied occasion-based conjoint across multiple categories, and it has uncovered growth opportunities that traditional approaches may overlook. By evaluating SKUs within specific Category Entry Points, we were able to identify potential whitespace areas, understand where consumers were willing to trade up or down depending on the occasion, and quantify the commercial potential of different portfolio strategies. The insights have provided a stronger foundation for portfolio decisions and innovation planning." - Julia Olsson, Strategy & Insight Manager, Orkla Snacks Sverige
For research teams, the implication is straightforward. If the objective is to identify incremental category growth rather than simply rank existing alternatives, then occasion-based design should not be viewed as a niche variation of MaxDiff or conjoint. It should be considered a more faithful representation of how consumers actually make decisions, allowing brands to uncover opportunities that conventional approaches can compress or overlook.
1. When researching occasions, the design is as important as the method.
MaxDiff and conjoint remain powerful tools, but to capture moment-driven behaviour, occasions need to be embedded directly into the choice task.
2. Traditional designs can hide growth opportunities.
Sequential surveys often compress differences between occasions, making white space opportunities and context-specific preferences harder to detect.
3. Occasion-based research delivers more actionable decisions.
It identifies which products, packs, prices, and innovations win in which moments—enabling portfolio growth, not just share shifting.
Reach out to Cambri to discuss how we help leading brands identify untapped demand, optimise portfolios, and find new sources of revenue growth.
References.
1. Schwarz, N. (1990). What Respondents Learn from Scales: The Informative Functions of Response Alternatives. International Journal of Public Opinion Research, 2(3), 274–285. DOI: 10.1093/ijpor/2.3.274.
2. Krosnick, J. A. (1999). Survey Research. Annual Review of Psychology, 50, 537–567. DOI: 10.1146/annurev.psych.50.1.537.
3. Holden, S. J. S., & Lutz, R. J. (1992). Ask Not What the Brand Can Evoke; Ask What Can Evoke the Brand? Advances in Consumer Research, 19, 101–107.