From Product Experience to Consumer Satisfaction
Combining consumer-relevant usage profiles with SDT-based satisfaction measurement to understand not only what changes, but what matters.
Products are not experienced at a single moment.
Consumers form expectations before use, encounter different sensory and functional cues while using a product, and form an overall impression afterwards.
Identify what consumers judge before deciding what to measure
Before asking whether a product performs better on an attribute, first ask whether that attribute is part of how consumers judge the experience at that moment.
These are the consumer decision criteria. They define the sensory, functional and experiential aspects consumers use to evaluate a product at a particular stage of the journey.
The broader opportunity is to move from asking “What changed?” to understanding “What changed in the consumer experience — and does it matter?”
Start with the criteria consumers actually use
Before asking whether a product performs better on an attribute, first find out whether that attribute is part of how consumers judge the experience at that moment.
For a household cleaning product, consumers helped identify which criteria were relevant at different stages of use. The criteria that mattered during use were not necessarily the same as those that mattered when the experience was complete.
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A broad semantic library was first developed from more than 300 descriptors collected from sources including consumer language and product communication. These covered sensory quality, safety and health, convenience, functionality, sentimental consequences and overall product impressions.
Consumers identified which attributes were relevant at different stages of product use. The results showed clear usage-stage dependency: some criteria were more relevant during use, while others became more important when the experience was complete.
Only a small number of attributes remained relevant across all stages. This showed that the criteria consumers use to judge a product are not necessarily fixed throughout the experience.
This creates an important opportunity: instead of beginning subsequent consumer testing with a fixed list of attributes, first identify the decision criteria consumers actually use at the relevant stage of the experience.
Subsequent testing can then focus on evidence that is meaningful for that decision.
Measure the product experience with enough sensitivity
Identifying the right attributes is only the first step.
When products differ subtly, conventional consumer scales may not discriminate sufficiently between alternatives.
The next challenge was to measure how strongly consumer-relevant attributes were reflected in the experience of each product, while reducing response bias and inconsistent scale use.
This led to the development of the Double-Faced Applicability (DFA) approach.
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DFA combines two features.
First, consumers make a forced Yes/No judgement about whether an attribute applies, followed by a short sureness rating.
Second, each attribute is examined from both sides. For example, instead of asking only whether a product is convenient to use, consumers also evaluate its negative counterpart, inconvenient to use, separately.
This structure was designed to make consumers’ judgements more consistent and to reduce response bias and variation in scale use.
Compared with a conventional one-step applicability scale, DFA provided better product discrimination, particularly during later stages of the usage experience where the simpler scale detected few or no differences.
The sureness judgement also added information beyond the initial Yes/No response, further improving discrimination.
Move from significance to magnitude
Finding a significant difference is useful. For product development, we also want to know how large that difference is.
Within the Signal Detection Theory framework, DFA allows differences in attribute applicability to be expressed as d′, providing an effect-size measure rather than only a significance test.
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Using d′ provides an effect-size measure of the difference between a reference and another product for each consumer-relevant attribute.
That changes the question from:
“Is there a statistically significant difference?” to: “How large is the difference in the consumer experience?”
It also places results on a common perceptual scale, making it easier to compare and integrate evidence across studies and methods.
This is especially useful when product teams are comparing several prototypes or evaluating incremental formulation changes, where significant/not-significant conclusions alone provide limited guidance.
Profile the experience of the product itself
Traditional d′ analysis tells us how much products differ from each other.
Sometimes the question is different: “What kind of experience is this product creating for consumers?“
Using the same DFA data, d′A, affect magnitude, was developed to quantify the direction and magnitude of the experience associated with each consumer-relevant attribute, for each product independently.
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DFA captures the positive and negative sides of each consumer-relevant attribute separately.
Those responses can be combined to estimate d′A, representing the relative positive or negative valence associated with that attribute for an individual product.
This makes it possible to construct an affective product usage experience profile, rather than only comparing products pairwise.
For example, a product might be experienced positively on simple to use, negatively on removing ingrained dirt, and less clearly on another dimension.
These profiles can also be followed through the usage journey. In the cleaner study, the experience changed across initial spraying, cleaning, rinsing and completion rather than remaining constant throughout use.
The result is closer to a dynamic map of the consumer experience than to a conventional end-point product score.
Interpretation note: A d′A value around zero can have more than one meaning, so as with all research, the underlying response pattern should also be considered.
See where product differences appear — and disappear
Two products can reach a similar final evaluation while creating very different experiences along the way. An early difference may also disappear by the end of the experience.
That means timing matters. A difference that is important during one stage of use may become less relevant later, while another may only emerge as the experience progresses.
A usage-stage profile helps identify where in the journey products differ, and where improvement is most needed.
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In the household cleaning study, product differences were particularly visible during some earlier usage stages, while by clean to completion the profiles had become more similar and generally positive.
This has practical consequences for product development. A formulation could ultimately deliver the desired result while still creating unnecessary effort or frustration during use. Another could improve part of the experience without changing the final outcome.
Looking only at an overall post-use measure could conceal both effects.
Connect product experience to satisfaction
This is where the work becomes particularly powerful for decision making.
DFA can show which aspects of the consumer experience change, in what direction and by how much.
Product development then needs another answer: “Does that change affect whether the product still meets consumer expectations?”
This creates a natural connection with the Degree of Satisfaction Difference (DOSD) approach.
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DFA and DOSD address different levels of the decision.
DFA: What changed in the consumer experience?
DOSD: Did that change alter satisfaction?
Used together within an appropriate research programme, they can create a chain from product experience to consumer consequence:
Consumer decision criteria
→ attribute-level experience
→ magnitude and direction of change
→ satisfaction consequence
→ product decision
The original DFA research already pointed towards this opportunity: temporal attribute profiles could be linked to overall satisfaction or hedonic measures to help identify what drives satisfaction.
Interpretation note: The DFA and DOSD methods were developed and validated in separate studies; their combined use represents a logical extension of that research programme rather than a single integrated validation study.
From measurement to diagnosis
The value is not another profiling method.
It is the ability to diagnose why a product change matters, or why it does not.
A product difference can be examined at several levels:
– Is this a criterion consumers actually use?
– Does the product experience differ on that criterion?
– How large is the difference?
– At what point in the usage journey does it occur?
– Does it ultimately change consumer satisfaction?
Together, these questions provide a much richer basis for a product decision than either a sensory difference or an overall liking score alone.
The broader lesson
