Conjoint Analysis
Part I: Developing New Products
Conjoint Analysis, specifically Choice-Based Conjoint Analysis, is a valuable quantitative research technique that Forethought Research employs to assist clients in gaining a better understanding of their competitive market, consumer preferences and consumer decision-making.
Conjoint Analysis can also aid in the development of a marketing strategy to enable clients to determine the viability of expanding into an existing competitive market both immediately and in the future.
In the context of developing new products, the following are some examples of key business questions that Conjoint Analysis can help to answer:
- If we introduce a new product into the market, how will it compare with existing products in that market?
- How should we design our product? Which attribute features (ie, colours, model size) should we choose?
- Which product attributes are going to be the most attractive to customers?
- Are there segments for whom the most attractive attribute differs?
When developing a new product, organisations seek to understand the decision-making process of the consumer, the likely demand and preferences for new products, the attractiveness of various product attributes and the value consumers place on specific product attributes.
Products and service offerings alike are made up of a number of attributes of which consumers place varying value on, within the decision-making process. Therefore, one way of assessing the attractiveness of a new product is to measure the attractiveness of specific product attributes relative to other attributes, and in particular the attractiveness of a new product attribute.
Conjoint measures this value by presenting respondents with a number of product profiles, each with varying levels of attributes. They are then asked to choose the product profile they would most prefer to purchase or adopt. Conjoint evaluates the trade-offs respondents make between attributes and as such the highlights the joint effect that multiple product attributes have on each other. This then determines the value respondents placed on the various product attributes and therefore their preferences.
The following is a hypothetical example that illustrates the value of using Conjoint Analysis to better understand the demand of new products. As part of a customer acquisition strategy, a credit union is proposing to introduce a ‘Bonus’ to its debit card account product and as such wanted to identify the appeal of a ‘Bonus’ relative to the cost of adopting a debit card account.
Using Conjoint Analysis, Forethought explored the effect a ‘Bonus’ had on consumer preferences for a debit card account relative to other debit card account attributes. From past experience and previous qualitative research, Forethought in consultation with the client, identified that there were five important product features (attributes) for a savings account with varying levels within each attribute:
- Supplier (Brand)
- Fees
- Accessibility
- Interest earned
- Bonus.
Segmentation analysis was first conducted to identify natural groupings in the targeted population. For the key groups identified, the relative Share of Preference of product attributes were profiled. It was hypothesised that some segments in the targeted population would value and trade-off the ‘Bonus’ offered in the savings account product, more than the other product features when deciding which savings account to adopt. The exhibit below illustrates the outcomes of the research.
Figure 1 indicates that Group B had a higher preference for a ‘Bonus’ attached to their debit card account than Groups A and C. For the other groups, other product attributes were relatively more important and therefore would be more inclined to trade-off a ‘Bonus’ for other attributes. By this comparison of the relative share of preference each group had for the debit card account attributes, Forethought is able to identify which segment most valued a ‘Bonus’ attribute in the purchasing decision .
Another feature of Conjoint Analysis is that of the Decision Support Simulation. This simulation can be run on an existing or hypothetical product and allows Forethought to identify specific product attributes that are most attractive to consumers and in turn identify specific products most likely to gain a client further market share. Having identified which segment most valued a 'Bonus' attribute in a purchasing decision, Forethought can use the Decision Support Simulation in recommending how to best package the product, given the product limitations and trade-offs consumers are willing to make for particular product attributes.
Note that if the credit union cannot provide the optimal product profile as identified in the analysis, Forethought will then run further simulations to identify another combination of product attributes that would be equally appealing to this segment.
Although Conjoint Analysis plays an important role in assisting new product development and guiding accompanying marketing strategies, a number of assumptions need to be taken into account when making decisions based on the results. These assumptions are:
- The environment remains unchanged to that described during the Conjoint research;
- Consumers will make their evaluations based upon 'perfect' knowledge of the market;
- All products presented in the conjoint research are equally available;
- Respondents accurately reflect the potential buyers;
- Consumers will make rational evaluations; and
- The sample selected reflects the potential market.
Whilst in the real world not all these assumptions hold true all the time, Conjoint Analysis still offers exciting insight into the decision-making process of purchasers. The primary virtue of Conjoint Analysis is that it can provide durable, cost-efficient information on new product concepts or alternative marketing scenarios.
Part II: Optimum Pricing
Forethought Research (Forethought) uses Conjoint Analysis, specifically Choice-Based Conjoint Analysis, to assist our clients in gaining a better understanding of their competitive market, consumer preferences and consumer decision-making. As discussed in Conjoint Analysis Part I, Forethought has been using Conjoint Analysis to aid in the development of new products and to determine the viability of expanding into an existing competitive market with that product. However, when developing a new product, businesses not only seek to understand the decision-making process of the consumer, the likely demand for and attractiveness of various product attributes, they also seek to establish the optimum price point at which a product should enter the market.
Conjoint Analysis can help answer this, plus numerous price-related queries such as:
- How much are consumers willing to spend on the product?
- How will a product perform in the market if its price is decreased?
- If our competitors change their price offering, how successful will our product be?
- What if we change our price to match our competitors?
It is difficult for a business to know what price to place on a new product when entering into a competitive market or how demand might be affected if the price of an existing product is altered. One way of assessing the best price for a product is to measure the attractiveness of the price relative to other product attributes. Conjoint analysis can explore the trade-off of price and product attributes in a purchasing decision.
As discussed in Part I of this article, product and service offerings alike are made up of a number of attributes on which consumers place varying value as part of the decision-making process. One such attribute is the price of the product. When Forethought conduct Conjoint Analysis, respondents are asked to choose the product profile (a combination of attributes put together to form a product profile) they would most prefer to purchase or adopt. Conjoint then evaluates the trade-offs respondents make between the price and product attributes, highlighting the effect price has on multiple product attributes.
The following is a hypothetical example that illustrates the value of using Conjoint Analysis to the price consumers were willing to pay for a new service in which they could pay there bills. Figure 2 shows the relative share of preference (meaning the percentage weighting cost ranked in relation to other attributes in the product profile) at various price levels. As expected, when the price attribute appears at no cost in the profile the share of preference was at its highest (35%). On the other end of the scale, the price option, Share of Preference was just 10%.
However, these results don’t necessarily mean that the lowest price is the best price with which to enter into the market. Figure 3 shows the impact on utility (or preference) at the four price points. This graph is interpreted by looking at the slope of the line as price increases. The slope of the line from ‘No cost’ to $2.00 is relatively flat; suggesting the share of preference at these two price points is similar. Share of Preference starts to drop beyond $2.00 and most dramatically from $3.50 to $5.00. So in fact the results suggest that respondents are willing to trade-off and pay a price of $2.50 for the product option they prefer. However, at a higher price, respondents are likely to change their behaviour to avoid a paying higher price.
Figure 3 also features a table of Elasticity Co-Efficients. This table shows how elastic or inelastic the price changes are. Co-efficient scores between 0 and -1.0 are inelastic. This means that the increase in demand in going from the higher price level to the lower price level is not sufficient to re-coup losses in per unit revenue. An elastic co-efficient (magnitude greater than -1.0) suggests a drop in price is economic because of the increased level of demand. Therefore, the results suggest that there is greater market responsiveness in changing the price from $5.00 to $3.50, and little or no response at the other price levels.
Also discussed in Part 1 of this article was the Decision Support Simulation (DSS). This allows Forethought to identify specific product attributes with price levels that are most attractive to consumers and in turn, identify the products that are most likely to gain a client further market share. Further, DSS can illustrate the change in Share of Preference with increasing price while maintaining other product attributes constant. Forethought employs DSS to recommend how to best ‘package’ the product, given the product limitations and trade-offs consumers are willing to make for particular product attributes with price.
However, Conjoint Analysis is based on some underlying assumptions, also highlighted in Part I of this article. In relation to price it is important to keep in mind that consumers are not always educated about the price range of products. In addition, intangible factors such as convenience are not measurable through Conjoint Analysis and in reality consumers may trade-off paying higher price for convenience. Nonetheless Conjoint Analysis conducted by Forethought offers clients insight into potential market share of a hypothetical product, be it new or a product with a new price and as such is an invaluable tool when developing, refining and improving products.
