Quick answer: Asking consumers what matters gives you stated importance, which tends to rate everything highly and favours obvious basics like price and quality. To find what really drives choice, also measure how each brand performs on each attribute and link those ratings to preference or purchase, which gives derived importance. Comparing the two shows which attributes are table stakes, which are hidden drivers, and where your brand should focus.
Ask consumers to rate how important taste, price, health, packaging and trust are when choosing a brand, and most will say almost everything is important. The list that comes back is long, flat and rarely surprising. It does not tell you which attribute would actually win you share if you improved it.
Clients often ask us how to rank needs more sharply, particularly in categories where several brands score well on the basics. The answer is to combine what people say with what their ratings reveal, then compare the two.
| Approach | How it works | Strength | Limitation |
|---|---|---|---|
| Stated importance | Respondents rate or rank how important each attribute is | Simple and easy to explain | Everything looks important, and socially desirable answers rise |
| Derived importance | Brand ratings on each attribute are linked statistically to preference or purchase | Reveals hidden drivers | Needs a good sample and careful analysis |
| Forced trade-off | Respondents choose between attributes, for example in a best-worst exercise | Sharp ranking with clear gaps | Longer questionnaire and more design work |
Using two of these together is usually more useful than any one alone. Many brand teams pair a forced trade-off question with brand-attribute ratings in the same survey.
Plot each attribute by how much it drives choice against how well your brand performs on it, relative to competitors. The four corners tell a clear story.
| Quadrant | Meaning | Typical action |
|---|---|---|
| High driver, strong performance | Your core strength | Protect it and keep it at the heart of messaging |
| High driver, weak performance | Your biggest opportunity | Improve product or communication here first |
| Low driver, strong performance | A strength consumers do not reward | Avoid over-investing |
| Low driver, weak performance | Low priority | Monitor only |
Normalising brand scores helps here. Bigger brands score higher on almost everything because more people know them, so comparing each brand's attribute profile against its own average shows its relative strengths more fairly.
Take a facial skincare category where consumers rate price, sun protection, brightening, gentleness on skin and a light texture. Stated importance puts sun protection and price at the top, with almost everything else close behind. Derived importance tells a different story: gentleness and texture are much more strongly linked to which brand people buy most often, while price matters, but mainly as a threshold.
Now add brand performance. A challenger brand scores well on sun protection but trails the leader on gentleness and texture after normalising for size. The priority becomes clear: protect the sun protection equity in communication, but invest in texture and in proving gentleness, for example through dermatologist claims or trial packs, because that is where choice is actually being won. Without the derived view, the team might have cut price instead, which would have cost margin without fixing the real gap. Repeating the analysis by age group then showed younger buyers weighted texture even more heavily, which shaped the media plan too.
If you run a regular tracker, a driver analysis once or twice a year keeps your priorities current without adding length to every wave. For the metrics that sit around it, see our list of brand metrics for Southeast Asia campaigns.
We build Vase.ai to run quantitative consumer surveys on a verified panel of Southeast Asian consumers, with AI-validated responses and a real-time dashboard. Brand-attribute ratings, trade-off questions and segment cuts can sit in the same study, run DIY or with our research experts.
If you want to know what really drives choice in your category, send us your research question, the consumers you need to hear from and when you need the answer. We will come back with a suggested design and sample.
What is key driver analysis?
It is a statistical method that links consumers' ratings of brands on specific attributes to an outcome such as preference or purchase, to estimate which attributes have the most influence on choice.
How many attributes should I include?
Enough to cover the main functional, emotional, value and trust benefits, but few enough to keep the grid short. Many studies work with a focused list rather than an exhaustive one.
Do I need a large sample for driver analysis?
A larger sample makes driver estimates more stable, especially if you want to compare drivers across segments. Ask your research partner to size the sample for the cuts you need.