Premium prices and data

Putting a premium price on certain products which cost almost the same to produce is a common marketing trick which can be brought back to the price sensibility of the different consumer and is having now a boom thanks to the extremely exact consumer profiling.
Think about the cappuccino made with fair trade coffee or with vanilla flavour. By buying one of them you send the message that you don’t mind paying a bit extra.
The strategy is to charge the highest price that the consumer will pay for that product.
A pricing strategy called First Price discrimination:

Exercising First degree (or Perfect/Primary) price discrimination requires the seller of a good or service to know the absolute maximum price (or reservation price) that every consumer is willing to pay.

More generally there are 3 common techniques for finding customers who are the first degree price discrimination:

  1. to evaluate each customer as an individual and charge according to how much he/she is willing to pay.
    This takes skills and effort and is used for items very valuable (e.g. selling houses) but the same technique is increasingly used by retails with their fidelity cards and by online retailers, both leveraging all the data collected from the specific user.
  2. The second approach is to offer different prices to members of distinct groups as students and elderly.
    This is inefficient but used when the alternative of individual price targeting is not feasible.
  3. The third is the self-incrimination technique: the one used by the coffee shops or by supermarkets when they arrange differently their goods or promote sales.
    By providing a choice between a regular and premium product, consumers are being asked to reveal their degree of price sensitivity (or willingness to pay) for comparable products. Similar techniques are used in pricing business class airline tickets, for example.
    This is the easiest to implement.

These types are not mutually exclusive. for example, moderne classification techniques based on data can allow finer group pricing. Not only a group could be easily identifiable such as students but could be something like “iPhone male users who have a high bandwidth connection”.

Therefore, some talk now about complete or direct or indirect segmentation of the consumers.

Clustering and profiling the consumers is the necessary step to know the maximum price to sell.

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