Understanding what consumers want….most

It is easy to rattle off a list of the factors that are important to consumers and thus influence their levels of satisfaction, loyalty and spend: low prices, quality or range of product/services, customer service, convenience of location, the list goes on. 

However, simply knowing what is important is not enough to affect company profits. Many companies do not have the financial resources to provide all desired attributes at the desired level; for example, companies offering the lowest prices in the market are often unable to also meet the expenses required for offering the best customer service staff. To be really able to influence the bottom line requires finding out not only which factors are most important, but also which factors consumers are willing to forgo or accept at lower levels to retain what is most important to them.

This kind of trade-off analysis means that companies can zero in on what it is that consumers value above all else, allowing them to focus limited resources on satisfying the most salient needs and preferences of consumers.

Since the 1970’s, conjoint analysis has been a preferred method of analysing trade-off decisions. Respondents choose the most preferred grouping of attributes, out of a number of various attribute groupings. This is particularly valuable in product development, when trying to determine the optimal pricing strategy or the optimal group of product attributes. However, conjoint analysis is not as useful when analysing trade-off decisions where it is important to know the level of importance of each attribute within the group of attributes. For example, it won’t tell you that price is more important than service, or vice versa.

Another, newer method of studying trade-off decisions that does determine the level of importance of each attribute within a group is best-worst scaling, or maximum differential scaling. It differs from conjoint analysis in that respondents choose the best attribute and the worse attribute, within a group of attributes. Thus the level of importance of each attribute, relative to other attributes that may also affect the decision, is determined. To illustrate, a best-worst analysis can reveal that although all attributes are important, such as service and quality of products, when it comes down to it, consumers will forgo these attributes (to an extent) in favour of low prices.

Conjoint analysis and best-worst scaling are flexible methods that can be applied to any scenario in which a product or service can be evaluated by consumers as a bundle of attributes. The appropriateness of each method depends on information needs: if knowledge of the optimal set of attributes is required, conjoint analysis fits the bill. However, if the level of importance of each attribute within that set needs to be known, then best-worse scaling is preferred. Thus, best-worst scaling is likely the best choice when trying to determine what consumers want more than anything else in a product or service.