Reading Notes: “An Introduction to Price Optimization” by Zilliant
This post is a commentary on the article on Zilliant’s website here. Zilliant provides pricing analysis to a wide variety of companies.
In the article, Zilliant talks about using price elasticity as a way of optimizing the price of an item for a customer. This is in line with industry standards as the go-to metric for price optimization. Where Zilliant’s approach improves upon the general strategy is by optimizing not for the entire market, but separately for different customer segments based on a variety of factors related to location, sales history, and company size. This approach is more effective in business to business (B2B as they call it) sales, especially in scenarios where there is a sales rep who manages pricing for each customer.
Zilliant goes on to talk about how they divide customers up into groups or clusters. They generally limit the number of factors under consideration around 8, and create a sort of decision tree for classifying customers into groups based on the attributes they find to be most effective. This is really useful because it allows you to understand the price elasticity for the customer specifically, rather than for the market as a whole. You can then set prices or markups based on geographical area, customer type, or which distribution center your customer will be getting their order from. When pricing is set up this way, it allows the company to increase profits by charging customers exactly what they are willing to pay.
Even in situations where you generally set a single price for all customers, having the information on hand allows you to make business decisions like “Who might buy more if we send them a coupon?” or “Which customers get the most value out of our products?”. This knowledge can be paired with other data about your customers to drive even further value. For example, if you want to expand and sell to a new group of customers, you can set your price based on what you learn from the early adopters in that segment. Or if you know that customers in a particular segment are more likely to visit your site on particular days (weekend, end of the month, during the holidays, etc.) you can set up flash sales during those periods in order to move the price of an item closer to the optimal price for that group.
The pricing model outlined by Zilliant is certainly more in-depth than a general price-elasticity analysis. Depending on volume, pricing structure, and the types of data you have about your customers, you can make use of this customer group analysis type of strategy in a variety of ways. While Zilliant has been one of the first movers in implementing this strategy, larger enterprises could definitely implement the two part process on their own for greater control of which factors are considered when creating their customer groups.
