In Thailand, pricing and packaging decisions often fail when research relies on direct questions. Iconic Research notes a core problem: consumers routinely misreport what they want and consistently underestimate their own price sensitivity. Ask whether a feature matters and many people say yes, but that does not reveal what they will sacrifice when price changes. Conjoint analysis addresses this by presenting respondents with complete product or service concepts—specific combinations of attributes and levels—and asking them to choose, rank, or rate between packages. Because people must make trade-offs, you can reverse-engineer what actually drives choice and connect it back to pricing decisions.
For conjoint analysis Thailand pricing work, choice-based conjoint (CBC) is commonly positioned as the dominant approach. In CBC, respondents choose between product concepts the way they would in a real purchase decision, rather than rating features in isolation. SurveyKing describes the same benefit in pricing research terms: customers do not just say whether a price is “acceptable”; they choose between competing options, which mirrors real purchase decisions and lets researchers quantify the relative importance of attributes such as price, size, flavor, and packaging. This is especially useful when you need to understand how price interacts with features, since direct questioning can reduce the reliability of resulting data.
What You Actually Get From Conjoint: Numbers You Can Price With
The most useful output is not a simple list of “top features.” Iconic Research frames conjoint results as three actionable numbers that support pricing committees and go-to-market planning. First is relative attribute importance, which shows which features drive choice versus which ones consumers claim matter, and it can reveal a substantial gap between stated and revealed importance. Second is part-worth utilities, which quantify the value of each level within an attribute. Iconic Research gives a concrete illustration: a 3-year warranty can increase purchase probability by 12 percentage points relative to a 1-year warranty, while extending to 5 years adds only 4 more. Third is willingness to pay: when price is included as an attribute, the model quantifies the premium consumers will pay for each feature upgrade.
These outputs are practical across categories, from new products to secondary markets. A recommerce example shows how conjoint can quantify value drivers such as condition, certification, warranty, and price tier, and then translate them into pricing actions. In that context, statistical modeling can yield utilities indicating that buyers pay up to 25% more for “certified refurbished” laptops with a one-year warranty. While that figure is not Thailand-specific, it demonstrates the kind of defensible pricing insight conjoint can provide when you include the attributes customers trade off in real decisions. Driver Research also highlights a calibration option: if base-case market shares are not reflective of reality, interaction effects can be added to the conjoint model to make results more actionable.
Thailand-specific execution realities still matter, so teams should treat conjoint as part of a broader method choice. Iconic Research explains that in Thailand, platform behavior, cultural dynamics, and regional variance affect which approaches yield reliable data. For qualitative stages, focus groups typically include six to ten participants, but moderators may need to adapt because kreng jai can cause participants to moderate views in group settings. That is one reason conjoint can complement qualitative work: it forces choices rather than relying on direct opinion elicitation. In practice, a strong program often starts with secondary research for context and then uses primary quantitative methods like CBC to quantify trade-offs and lock pricing to evidence.
How does conjoint analysis improve pricing decisions in Thailand compared with direct questions?
What are the key outputs of a conjoint study for pricing?
What is an example of a part-worth utility result?
When might you need to calibrate conjoint results?
