If you work at a bank, you know that you essentially rent money from one person and then rent it to another person at a higher price.
Let’s say the prevailing rate is that a consumer gets 1 dollar for every 100 dollars they let the bank use every year. <ignoring compounding>
Some people will hold out for more than a dollar.
Some people would be happy with less.
On balance, a bank could be more generous with some folks and pay some folks less if they knew what people expected to get for their 100 dollars.
The same is true when the bank goes to loan the 100 dollar bill. Some people might pay 4 dollars or 5 dollars a year for that 100 dollars while others might not be willing to pay more than $1.50.
Prior to the advent of big data, it was too expensive to figure out who would take what price for what money. There was simply too much transaction, market, and competitor data to crunch to figure this out and it didn’t pencil out to spend the time and money needed to price products this way.
Thankfully we now have the affordable compute power and sophisticated software to crunch the numbers to figure out an individual’s or a group’s elasticity for a given product. For banks, this means essentially creating an efficient private marketplace for their products where more people get the price they expect for the product they are interested in and more people can participate. This marketplace mechanism is thus then fairly adjusted to improve the profit picture for banks all while meeting customer expectations.
By putting in a more efficient marketplace mechanism like this, banks can do business with a wider set of consumers and make money doing what they do best — taking in money and loaning it out. Learn more about Price Optimization »
Here are some of the ways price and offer optimization can be applied.
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