Listen to the Founder of Engage3, Ken Ouimet, discuss the science powering Price Image Management. Using Harry Markowitz’s Nobel Prize-winning research on the Efficient Frontier, you can boil down your pricing strategy to how much risk you are willing to take (Price Image) for a given return (Profitability).
The following is a transcript of the audio podcast:
In finance you need to manage a portfolio, you need to determine how much to invest in each asset. So, if you have a thousand assets, it’s a very complicated decision on how much to invest into each one. The Efficient Frontier allows us to boil that down into a strategic decision of how much risk I am willing to take for a given return.
Similarly, in retail we’re managing a portfolio of products and the products, instead of an investment decision, it’s a pricing decision – how much am I going to invest into each product to lower the price. The Efficient Frontier allows us to boil that pricing decision down into 2 dimensions – price image and profits. We use it in retail pricing – you have to manage thousands of prices or thousands of any decision you have to make. How do you roll that up into a strategic decision? Because as we look at retail pricing becoming faster, more localized, being split across different channels, we need a tool to pull all that together and manage a consistent strategy across all these different price types. This is the most powerful tool to do that.
So, if we look at the Efficient Frontier for a retail product pricing, then the dimensions as we’re looking at are the price image along the x-axis and the profits along the y-axis. The highest point of the curve is where you make the most profits. Those are short-term profits. When we get the point-of-sale data that’s recording short-term decisions from customers, and that’s what the demand models are modeling. There’s a risk if you price at the top of the curve that while you make the most short-term profits, you’re burning your price image and the customers aren’t going to come back.
Then the strategic decision is how far do you go down and how much do you invest into having a lower price image, and that’s unique to each retailer, each strategy. It’s actually even unique to each category, fulfillment type, each channel that you’re pricing. To give you an idea of how much information and the power that goes into creating one of these curves: let’s imagine we just have two products, and each product has ten possible price points. If you look at all possible ways to price those two products, you’re going to make a grid of prices, and it’s going to be a 10×10 grid. That’s going to be a hundred different price points you need to evaluate for just two products. We add a third and there’s another ten price points, now it’s a cube and it’s got a thousand elements in that cube. You add a fourth and it’s ten to the fourth.
So now we get to a category of a thousand items. Just one store, one category, a thousand items. It’s ten to the thousandth power number of pricing scenarios we need to evaluate. That’s more atoms than are in the universe. These are huge computational problems, and this is where algorithms become important—algorithm scalability become really important in these optimization algorithms.
Now what excites me about this problem: to manage a hundred million prices in a dynamic market is a really complex problem. And how do you boil that down to something you understand and make a strategy around? What really excites me is this is a tool for collapsing all those decisions into strategic decisions where somebody, an executive, can get their head around and be able to integrate the strategy all the way to tactical execution and automate those decisions. And that really excites me.
For more information on Price Image Optimization, you can request a demo here or learn more about the platform here. To learn more about the history of pricing strategy, watch Tim Ouimet discuss how a simple change revolutionized the retail industry here.