Conjoint Analysis in the Pharmaceutical Industry
The pharmaceutical industry is governed by rules and regulations often not present in other product and service industries. Generally for prescription only drugs, consumers have decreasing input into the decision of which brand is supplied. This choice is complicated further by the rising substitution of generic pharmaceutical products on the market. Also, price to the consumer and price to the pharmacist are becoming increasingly important in choice of brand.
Pharmaceutical companies are prohibited from directly advertising their prescription drugs to consumers which means that manufacturers and wholesalers have a finite opportunity to influence purchase behaviour for most prescription products.
In light of this, the question becomes, ‘how can quantitative marketing research assist pharmaceutical marketers’?
What is Conjoint Analysis – what is it testing?
Conjoint analysis is a quantitative technique used to better understand individuals’ preferences and the utility placed on various product and/or service attributes in decision-making. Conjoint analysis evaluates the trade offs that individuals make by estimating the joint effect of multiple product attributes simultaneously. For example, typical attributes in pharmaceutical products may include the brand and price to consumer. Also important are the patient characteristics such as patient co-morbidity and history of medical compliance. Such patient characteristics can also be factored into the conjoint analysis to determine for which patient type GPs would most likely recommend a particular product.
Price, both to the consumer and to the Government, can be an important factor in pharmaceutical brand decisions. It is particularly important in situations where there is a generic equivalent available in the market. Conjoint analysis can provide an indication of the price range that best affects positive product demand. Questions frequently asked by manufacturers include ‘What will be the effect on market share at various brand price premium levels?”, and “How can we maximise our return from investment?” Conjoint analysis can address questions such as these and consequently, is a valuable tool during new product development and the development of new pricing and communication strategies for existing products.
The Conjoint Design and the Choice Tasks
There are a number of alternative models for conjoint analysis. The model that is most popular for approximating purchasing decisions is known as choice based conjoint (CBC). In CBC, the respondents are shown a small number of products and asked to state discrete product preferences. This is repeated a number of times in a series of choice tasks. The products (or services) are described using a number of product attributes (e.g. price, formulation), and each attribute is composed of several possible “levels” ($3, $4 or $5; tablets, capsule or syrup).
In a purchasing decision, often only the most important product attributes are in the consideration mind-set and traded-off against each other. Consequently, qualitative research is often used to initially identify which attributes are likely to be most important in the conjoint choice design. Therefore, the research process often involves two stages, the first being a qualitative discovery phase followed by the second, quantitative conjoint phase.
Conjoint Outcomes
The relative utility of the choice attributes provides an understanding of the importance of each attribute. Referring to Figure One, this exhibit illustrates the relative importance for the attributes in a hypothetical conjoint design. The results suggest the attribute “Formulation” has a greater influence on the decision-making process, with 49% share in the decision-making process. The pie chart on the right of the exhibit illustrates the relative share of preference of the different levels for the “Formulation” attribute. This example suggests there is stronger preference for ‘Capsules’ (worth 45% preference share) than for ‘Tablets’ and ‘Syrup (worth 36% and 19% preference share respectively).
A central component of product development and subsequent marketing is price setting. Conjoint analysis was the first popularly applied marketing research technique that enabled the monetisation of the respondents’ choice utility. Indeed, until this day calculating price elasticity coefficients has been an output largely germane to trade-off analysis. Price elasticity is used to determine how a pricing range will affect total revenue (refer Figure Two). As the price increases towards the upper limit (i.e. from $0.15 to $0.25), the effects on preference are more dramatic than are those towards the lower end (i.e. from $0.00 to $0.15). This information enables the marketer to estimate the optimum price before preference is likely to be significantly affected, which can assist the development of a pricing strategy to maximise return.
Decision Support Tool: The Conjoint Simulator
The outcomes from conjoint may be summarised into a decision support tool or simulator. Such a tool is valuable for product development and strategy refinement. The simulator is an interactive program that utilises the results of the conjoint component to enable real-time product or service comparisons. By manipulating different combinations of product features in the conjoint design, the marketer is able to estimate preference for a limitless number of hypothetical products or services. Such a tool can be easy to use yet powerful for aiding strategic business decisions.
Trade off analysis remains as popular in the pharmaceutical setting as ever before. The restrictions on marketing activity coupled with the relatively finite populations of pharmacists and prescribing doctors, along with the limited number of alternatives listed on the Pharmaceutical Benefits Scheme, means that the assumption of perfect information is perhaps reasonable.
The author wishes to acknowledge the assistance of her colleagues at Marketing Science, Forethought Research.
