Behavioral pricing has long become a part of pricing mainstream, and many experiments and practical examples underline its validity. One prominent example involves two bottles of wine – and a third more expensive bottle that serves as a decoy to affect customers’ preferences of the two other bottles. Figure 1 illustrates the effect. Consider a wine vendor who offers two different types of wines, a good wine A at a price of €10 and a better wine B at a price of €30. It turns out that 54% of customers buy wine A and 46% buy wine B. Most customers find it difficult to judge the quality of a wine and they don’t want to overspend. Then, the vendor adds the additional wine C at a price of €50. It serves as a decoy, so that the price of wine B appears to be much more reasonable. Now, 15% of customers choose wine A, 73% wine B, and 12% wine C, as customers do not only switch from A and B to C, but also from A to B. This phenomenon is behavioral, as it violates the ‘independence of irrelevant alternatives’ axiom of classical economics.
This example and the decoy effect are well known among pricing professionals, and they are frequently used to justify adding a further premium option to a product range. However, beyond this more anecdotal use, it is not clear how to make the best use of such behavioral effects. For example, is €30 the best price for wine B? Or, what is the optimal decoy price? If it’s too detached from the other prices, it loses effectiveness.
To really work with such behavioral effects, pricing professionals need to be able to model how customers react to different prices of the decoy C as well as of wines A and B. Figure 2 shows the example described above on the buynomics pricing platform. Based on the specifics of the example above together with general insights from the behavioral pricing database, we created 1,000 virtual customers that make purchase decisions like those underlying the above example. Their willingness to pay for wine B is influenced by the introduction of the decoy.
Once available, these virtual customers can be presented with alternative prices or product offers. As an example, Figure 3 shows that the profit optimal price of wine B – if the prices of wines A and C are kept constant – is €36, rather than the original €30. This leads to an improvement of total profit of 8.5% (from €11,795 to €12,797).
Knowing the relevant behavioral effects is the first step, working with them to optimize prices the second.
In July, we will launch the beta version of Predict@buynomics. Predict@buynomics is a pricing solution that helps pricing professionals optimize their prices and product offer at scale using virtual customers. Virtual customers act like their real-life counterparts – and you can turn their susceptibility to a variety of behavioral tricks, like a price decoy, on and off.
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