You either enjoy shopping around looking for a good deal and haggling to get it, or you want to see the price and decide to pay or walk away.
Retailers have long sought to find the best way to determine the most you would be willing to pay without abandoning the sale.
Machine learning and your willingness to share your shopping behaviour may finally allow them to do that.
Humanity has always tried to get a good deal, but it has been more art than science. Those with the gift of the gab are born salespeople and con artists are able to convince us that they will give us a great deal even if sometimes it is not.
For some a fixed price solves all the problems. The idea was first implemented in the 1800s by a US merchant. A fixed price for a specific product for all customers.
The win for consumers is they knew what they would be paying upfront and not need to negotiate a better price.
The idea spread and for many high street retailers it became the norm.
That merchant, John Wanamaker, is also credited with the first returns policy and the phrase “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” He also helped fund the movement that created Mothers' Day.
While his model became very popular, it appears that we may be moving toward a hybrid dynamic pricing model that is both fluid and static.
The concept is even older than the single price model, but without the modern sounding name. All pricing was dynamic because you always negotiated it.
But it has come to be applied in a different way. Tather than a good price for an important or regular customer, it set pricing according to demand, time of day, season, potential for bulk purchase, first time purchase, expiring date and many other variable factors.
You know that you should shop around for airline tickets; checking not only different airlines but time of the flight, if there are layovers and when in the week, month or year would be cheapest to travel. This is dynamic pricing as airlines know that it is most important to fill all the seats even if some only just cover costs, because an empty seat eats into their profits.
Prices in popular tourist areas are also dynamic. The same hotel, or restaurant, will cost significantly more in season.
Uber taxis charge a premium called surge pricing when demand is high as a way to encourage more drivers to sign-on in order to deal with the increased demand.
These recent changes have come about as a result of consumers being willing or not understanding the potential consequence of allowing their shopping behaviour to be directly linked to themselves while also providing detailed information about their habits and interests.
Companies like Feedvisor use that information to create not just a personalised price based on the maximum a retailer could hope to achieve, but also how other factors such as product guarantees, your perception of the brand and customer service levels.
The collection of data has given online retailers the edge, none more so than Amazon who uses its vast data sets to not only offer the best prices but to make bets on which products they should produce themselves and what they should be priced at.
This has set up a very real conflict in what is sometimes called the battle between brick and click. Retailers are aware than many shoppers will visit a real shop (brick) to look at a product and see if it is what they want and then buy it online (click) where they can find the best deal.
In trying to counter that; shops highlight the fact that products bought in store are available immediately and don’t have delivery fees.
One solution is for online stores to have a physical store too. Amazon opens theirs soon, while physical store retailers are closing many of their marginal shops and investing in their online offerings.
The consumer appears to be the winner set to the get the best of price, service and choice, but there is something that may be changing that.
In 2012, the Wall Street Journal reported that an online travel company had noted that users of its services who had an Apple Mac were willing to pay more that users on a Windows machine. They then directed Mac users to view more expensive hotels, in doing so they would earn better commissions while still being quite sure they would be able to make the sale.
Because you don’t know what retailers already know about you, you would not know how they may be offering you something that appears to be a fixed price, but isn’t.
The full extent of this is beginning to be implemented as retailer loyalty programs begin to use the collected data, not just to make predictions about your potential spend and so project stock inventories and distribution, but by adding personalised discounts, knowing how to get you to buy the specific products or brands that they would like you too.
If a brand wanted to increase its market share, it would advertise or discount its product or pay for a better location on the shelf. Now retailers can offer campaigns to help a brand shift their market share by targeting the consumers of their competitor brands' products and offer the special price. The till will confirm the sale and a repeat purchase would confirm you have a new loyal customer.
But it does not end there. Staffing a large supermarket to ensure enough tills are manned and shelf packing staff are on hand to respond to demand is tough as shoppers would tend to arrive at random times or be too clustered at others like month end. Using the richer data on a consumer they may offer special shopping times to those that can come when the store is quiet or avoid coming when it is very full using a surge pricing model for certain times of the day or month.
It will either make you happy or a little concerned.
The gig economy as it has been termed is a data-driven freelance market of pricing people’s pay on when and where their services are needed.
If that was extended to full time staff you may see staff returning to a model of pay based on output rather than a set salary. Machines can measure and determine if an employee is generating sufficient direct revenue or support for those that do to determine their fluid monthly salary.
You may have the belief that most prices are more or less fixed, although as you begin to look at just how each are actually set, you will realise we are heading back to a system where all prices are fluid. Unless you understand how to haggle and negotiate in this new marketplace you will always be paying more than you need to.