Trial coupons (like from Groupon) can be a very effective way to attract new customers, but as generally done, this process tends to attract bargain-hunters who may not be the ones a business really wants to attract. FairPay promises to enable a better way to attract your real target market.
The idea for FairPay (Fair Pay What You Want) came from thinking about the problems with digital offers and how to solve them, but it also has significant potential for use with real products and services, especially for experience goods, where the true value is only apparent after having the experience.* I suggest a Groupon-like service that similarly offers "coupons" for trial offers, such as for restaurants, service establishments, and the like, but based on FairPay pricing. Think "Groupon What You Want" or "PriceMeNot."**
FairPay is based on taking the risk of low payment on some product offers, in order to seek to build a profitable relationship with a prospect. The prospect is told that they can set their price as they think fair (possibly within limits), but that such offers will continue in the future only if the seller(s) agree the buyer's price is fair (based on individualized criteria). Unlike conventional coupon offers, which offers a pre-set discount, FairPay lets the buyer set their own discount, higher or lower, after they try the product or service. If the esperience was good, the discount is smaller, but if it was bad, the discount can be higher (possibly even 100%).
This is attractive to those seeking fair value, by eliminating their risk of buyer's remorse. It makes trying new places nearly risk free (at least as to cost), and offers a fair discount for taking the risk of a bad experience, but can be selective enough to exclude those who just want a bargain and will never be good customers.
In the case of a coupon aggregator (like Groupon), the aggregator would collect feedback from the buyer on why they set the price they did, and from the seller on whether the price seems fair, given those reasons and given other data about the buyer's values, demographics, and ability to pay. The aggregator can explain that they will develop a reputation for the buyer, and use that to target other offers (or not). Thus the buyer has a strong incentive to be reasonably fair.
For example, for the case of a restaurant (which has significant marginal costs), the offers may be framed so that the buyer is told they can pay any price they want, but if not at least a target percentage (maybe 50%, maybe more), must explain why they think it is fair (with a few multiple choice questions that are easily scored automatically). They might also be told that a suggested fair price should be between 25% and 75% of the normal billed price. This reflects the objective of providing a discount for the risk of a disappointing meal, but with the idea that even a disappointing meal is usually worth something (say 25% of full price), and a very good meal deserves a good price, even as a trial (say 75% of full price). The buyer might be free to pay zero, but only in truly rare cases (such as for buyers that usually pay well) would that not be taken as a black mark on their reputation score that might exclude them from most or all future offers. This pricing might be set directly with the aggregator right after the meal (such as in a mobile app), who would then settle with the restaurant privately. Other kinds of service establishments could use a similar process.
By doing this over a series of offers, the aggregator can characterize each buyer with a FairPay reputation, maintain that in a database (along with rich, transaction-level detail on what they pay well for and what they do not -- and why), and use that reputation data to target additional offers. Merchants most eager to attract customers will make offers to a wide range of prospects (with correspondingly high risk), while other, more successful or selective merchants might limit offers to those who have already gained a reputation for paying fairly (thus taking relatively low risk, and from more valuable customers). The aggregator can also limit the number of offers that a particular merchant makes to untested buyers with unknown fairness reputation, to limit the risk even for marginal merchants.
The benefit to buyers is that those who are willing to pay fairly when they get value can be given trial offers for quality establishments that they may be likely to revisit. It can be made clear that buyers who price at above the suggested value can generally expect to become eligible for more attractive offers, and those who price below that value will generally get less valuable offers. Some will price for quality and style, and some will price for the biggest discounts they can get (if they do not squeeze too hard). Offer flow will vary accordingly.
The benefit to merchants is that they can target the prospects most likely to appreciate what they offer, in a way that calibrates their risk. Some will seek new customers at relatively high risk, while others we be selective, and take little risk.
The benefit to the aggregator is not only a more effective coupon business, and a new broader range of consumers participating, but a valuable new database of very fine-grained data on buyer value perceptions and willingness to pay. Much like a credit rating database, this FairPay reputation database can become a very valuable asset in itself. (And the aggregator can maintain the privacy of the customer data by not revealing the data to the merchants, but just using it internally to manage the offer process based on merchant-specified criteria, much as many ad-targeting services do.)
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*As a well-tested reference point in the non-digital world, consider the experience of many restaurants, theaters, hotels, and other businesses who have tried pay-what-you-want offers. These do not include any of the reputation tracking controls that FairPay applies to limit free-riding, but even with that limitation, PWYW has proven effective in many such situations. See, for example, references cited in my Resource Guide to Pricing , such as the one that studied the restaurant Kish. For a more widespread example, check out Panera Cares.
**[Update] This can also apply to services analogous to Priceline's, especially for products like hotel rooms that have a high experience good aspect. A number of hotels already do PWYW offers at off times, so FairPay is a clearly attractive alternative. (Note that the "Name Your Price" feature of Priceline is very different from the Fair PWYW approach of FairPay, since with Priceline the seller can and will reject your price if it is lower than his secret minimum price. With FairPay your price is never rejected -- you just won't be allowed to continue setting unfairly low prices more than very rarely.)
