I have been thinking a lot about customer analytics recently, especially in the context of value pricing, and perhaps more pointedly, the determination of the right price to charge for an item. I am in the middle of reading an interesting book about the theory of prices called “Priceless: The Myth of Fair Pricing,” which describes some interesting concepts regarding anchoring that are used to set the context for establishing prices in a somewhat arbitrary manner.

Informally, anchoring is a method of influencing individual decisions about numbers. Higher anchors will lead people to inflate the numbers used in a response, while using a lower anchor will influence people to choose lower numbers. As one example, if you were to present someone with a fancy necklace and ask how much it is worth, you might get a wide range of answers, some high, some low, and some in the middle.

However, if you took the same piece of jewelry and asked people if they thought the jewelry was worth more or less than $1,000 (which is the anchor price), and subsequently asked how much they thought the item was worth, the average price hovers much closer to the provided anchor price of $1,000, even if the item was only worth a few dollars.

This is a rather subtle effect that is put into use in many different ways and forms, ranging from the concept of a sale price that is marked down (does anyone ever pay the manufacturer’s suggested retail price of a car?), the perceived discounts of bundled packages (such as combining telephone, internet and television service – it is hard to determine which package is the best deal), or even the price scale for buying one of the multiple configurations of an iPad.

But from a customer analysis perspective, I wonder about the degree to which external influences such as anchoring can be combined with customer centricity and knowledge to drive two different aspects of the decision-making process for making a purchase. In other words, can you adapt the impacts of influencers such as anchoring in different ways depending on the different customer classifications? And if so, would that also influence a company’s decisions about setting prices in a way that is variable in relation to the different customer types? Considering answers to this question requires some additional thoughts about customer types, which will be the focus of the next entry.