One of the main tasks in marketing research is to learn how buyers value products or services and their components. Buyers obviously want the most desirable features at the lowest price possible, while manufacturers want to maximize profits and/or market share.
To achieve this, manufacturers need to provide a product with greater value than the competition at competitive prices. Going further – to understand which of the product pieces drives individual buying decisions, which ones are the most appealing and how to combine these pieces to get the optimal product in terms of value for buyers and maximizing revenue for sellers. Unfortunately, asking simple questions about preferences like: how much would you pay?, or what flavour do you prefer? – doesn’t solve the problem, as buyers usually want the best options at the lowest price. Buyers would probably pay a little more for a specific component but may prefer not to state which component that would be and how much more they are willing to pay. Usually, it’s hard for them to be specific and explain which features are more important for them in a given situation.
So, instead of asking direct questions, we ask respondents to choose among a subset of products – and based on that we evaluate how much unique value each product feature adds to the combined (conjoined) value of the product. This technique is called conjoint analysis.
Which device would you choose?
1.Patient Monitoring system with 16 waves and 8h battery
2. Patient Monitoring system with 8 waves and 16h battery
If a respondent selects Device 1 it means they prefer a higher number of waves. If they choose Device 2 on the other hand, then it means that battery life plays a bigger role for them.
We present realistic trade-off scenarios and calculate preferences based on product choices. When respondents are making difficult trade-offs, we learn what they really value and what drives their decisions.
Typical choice based conjoint consists of three to four cards presenting different products with different options.
These options are attributes and each attribute contains a couple of levels.
Attribute: “Brand” could contain levels:” GE Healthcare”, “Philips”, “Mindray” and attribute “Waves” could contain levels: “4 waves”, “8 waves”, “16 waves” etc. We can also add the attribute “Price” with a defined set of levels.
Respondents select their preferred product on each page view, making these selections is simpler for respondents because it mimics what they typically do in real life while making choices – selecting products not an answer on a scale. Apart from gaining information about what respondents really value and how their preferences are distributed, we can also simulate how much value they assign to a product with different combinations of features and compare different products with different features vs. price, which helps us learn which combination of product features will generate the highest revenue for a company.
If you want to learn more about specific applications of conjoint analysis or discuss your business needs, please feel free to contact us.