When we price products, we’re largely constrained by the manufacturing or wholesale cost. When it comes to services, it’s more about perceived value, and there’s more flexibility. But things can become intriguing where offers and packages are concerned. What’s known as “decoy pricing” is a fascinating technique here.
An example is when you have a plain product A and an enhanced product B, which you’d prefer customers to buy. By adding an even better product (or package) C, but at an exorbitant price, product B appears to be a more attractive option than it did before.
Behavioural economics researcher Dan Ariely describes a variation here. He unpicks a seemingly strange offer where product A is $59, product B is $125, but product A and product B together are also priced at $125! What’s going on? When he did some testing, he found that when faced with the three choices, most people went for the combined “product A + product B” offering, presumably because of the perceived value. But when the middle option was hidden, most people went for the cheaper “product A only” option. When we’re not sure of the relative value of an offer, we go for safety.
The decoy effect is also known as asymmetric dominance, which describes it more clearly. Suppose you have a cheap model of blue widget which is low speed, and a more expensive model which is high speed. Some customers will be inclined to pay the extra for the higher speed, some won’t. What happens if you introduce a third model which is medium speed, between the two, but priced higher than the high speed model? Sales of the high speed model increase, because there’s a basis for comparison between the first two models; one is cheaper than the third model, but the other is cheaper and faster.
Instead of the medium speed model, what happens if we introduce a model which has a lower speed than the original two, but priced between them? This causes sales of the original low speed model to increase, as it’s cheaper and faster than the new one, whereas the original high speed model is merely faster.
Of course, in industry, purchases are less discretionary, as the product has to meet a technical need, but then again, engineers and scientists are more likely to plot a price/performance graph in their heads and assess the impact of an outlier. I’ve seen companies use this technique to great effect with packages and volume pricing. Could you be doing this?