Data Mining: Digging for Nuggets to Make Pricing Decisions

Posted by Jenifer Thomas, Oct 04, 2012 2 comments

Jenifer Thomas

Every time someone questions the value of data mining, I can’t help but hear the Gold Rush-era adage, “There’s gold in them hills!”

The wealth of information gleaned from data analysis can provide great guidance in decision making, especially in relation to pricing. And if you’re a data junkie like me, you might enjoy data mining, too.

Analyzing data gives insight into how the audience values our product. We can then price according to that value.

For example, an organization may assume that its box seats are the best in the house, and price them accordingly. But as the first performances near it’s clear that total sales are increasing, but the boxes aren’t selling. Often this prompts a frantic decision to discount those seats to encourage sales. But hold steady! A more reasoned approach is to ask a few honest questions:

  1. Is the box ticket price too high?
  2. Is our perception of the value of a box seat too high?
  3. Are the range and relationship of the prices out of whack?

Here’s where data comes in—mining into where people are choosing to sit in the house and what they are paying often gives answers.

For example, if we look at the data and see that demand is actually strongest in front-and-center orchestra radiating out, and there is little demand for the boxes, then the audience is spelling it out for us. They value the orchestra seats more and are willing to pay a price they deem reasonable for that value. The box seats are not as valuable to our audience, and the pricing is not reflecting that difference in value.

Or, the difference in price of those two sections is not adequately differentiating that value. Maybe the prices are too close together and the audience doesn’t perceive enough difference in value to warrant the upsell to a box.

So what can be done?

A discount could always be issued and maybe that will sell the boxes, but that runs the risk of devaluing those seats in the process. Better yet, the house could be rescaled to reflect the audience’s demand and pricing could be adjusted to match that demand.

Perhaps this data might even lead to raising prices for the seats that are the most in-demand, like front orchestra, and then adjusting the price of the lesser-valued sections—like box seats—in relation to the price of the in-demand seats.

The value of data mining is to use data to price the house more in line with how the audience values the seats.

As a result, you can kill two birds with one stone: you maintain accessibility by offering an appropriately wide range of prices, and maximize revenue by not leaving money on the table for the seats that are in the highest demand—without unnecessary discounting.

Data mining provides the opportunity to make evidence-based pricing decisions. Rather than making assumptions, let the data do the talking.

So mine on, data junkies. There’s gold in them hills!

2 responses for Data Mining: Digging for Nuggets to Make Pricing Decisions


Jenifer Thomas says
October 08, 2012 at 3:57 pm

Thanks for the comment, Ron. Glad you liked "the data never lies." Unfortunately, though, the audience sometimes might, which it makes it difficult to get that kind of information. We tend to see that it's difficult to ask patrons about price, because they usually respond with a lower answer than what they might actually pay.

Basically, what we say we're willing to pay and what we actually pay are often two very different numbers.

  • Please login to post comments.
October 05, 2012 at 12:01 am

Hello Jenifer, this is a great post outlining the benefits of data mining -- helpful both to those who understand pricing and those who don't yet but should! "The data never lie" is a good motto. In your research, have you found qualitative research such as asking patrons about their pricing preferences to be complimentary to your box office data? Or do you find that people say they think one thing, and then their actions say something different, which is often the case? Ron

  • Please login to post comments.