Conjoint analysis is probably the most successful marketing research tool used for measuring customer preferences as well as demand fluctuations caused by changes in product and service features and price adjustments. It is also a very powerful instrument for predicting the demand for a new product and the effects of the introduction of that product on sales of existing products. However, conjoint analysis cannot provide answers to two of the most crucial questions: How to set the right price to achieve the desired business results? and What is the optimal price? PriceOptimization can help you answer both these questions.
Optimizing overall business decisions
In choice-based conjoint analysis, CBC, survey participants are asked to choose between similar offerings with varying value-added services and attributes. As in real life, the participant has to make trade-offs between the attributes of the different offerings. For example, will you buy a weekend-only newspaper subscription for $30 per month or a full-week subscription for $50? By varying combinations of attributes in the questionnaire, you can identify the attributes that have the greatest impact on the participants’ choices. This makes it possible to build a simulation model illustrating how customer preferences change as a consequence of attribute variations, at the same time establishing the relationship between demand and price. In our example, the simulation model demonstrates how the demand for the weekend-only subscription declines as its price increases. It also reflects the change in demand for competing products.
The result of conjoint analysis is certainly very useful but it does not in itself identify what the price of each product or service should be or what bearing the price has on the overall business outcome. To get the whole picture, you need to understand the impact that a change of price has on revenues, costs, profits and market share. Additionally, if you offer an array of similar products and product packages, you need to evaluate what effect a price change for one product will have on all the other products in order to identify the combination of prices that will optimize profits for the whole product portfolio. This can’t be achieved using conjoint analysis alone.
While using the demand and price relationship established in the conjoint analysis, PriceOptimization therefore additionally incorporates parameters such as the value-chain conditions that have to be fulfilled for a new set of prices to be accepted. For instance, the sales channel parameters include costs, margins, contributions, revenues and sales volumes. The model also calculates the effect a price change has on these parameters. With this model, it is possible to determine how price changes impact not only the demand for each product but also the overall profit, revenue and other business parameters that are built into the model.
The following questions can be answered through PriceOptimization:
- Which combinations of product prices will maximize profits?
- What will be our market share at these prices?
- What prices will maximize profits if we don’t want to lose more than X% in market share?
- If we introduce a new product, how much will profits increase or will the new product cannibalize our existing products?
- What would the impact be on profits, revenues and market share if we stop selling one of the products in our portfolio?
- How can we increase our market share by X% with minimal effect on our profits?
Conjoint analysis is probably the best market research approach for understanding customer preferences, but it falls short of providing the vital information you need in order to make the right business decisions. PriceOptimization builds on the results of conjoint analysis by transforming the choice-based data into invaluable business-decision information, thus adding substantial value and reducing risks. Depending on the specifics of the business decision, PriceOptimization projects typically improve profits by 10 – 30% with limited or no loss in market share.