Vistex Explores the Anatomy of Price Optimization
Trying to figure out what the right price should be for many products can be difficult and determining the optimal price is even tougher, according to Vistex, which provides enterprise solution extensions that manage pricing, incentive rebates, royalties and channel programs.
During the March 15 webinar “The Anatomy of Price Optimization: Practical Examples in Life Sciences,” Alejandra Garitonandia, industry principal-life sciences at Vistex, explored the concept of price optimization, analyzing how data science can help a company maximize revenue and profit when defining the optimal price.
That can be accomplished using different processes, from defining a list price to the optimal price on complex quotes and offers via different mechanisms using algorithms.
A practical example was also provided of how algorithms and technology can support contract manufacturing organizations (CMOs) to define the optimal price for quotes.
Garitonandia told viewers that she is a life science professional who specializes in pricing and revenue management. “I’m coming from a very strange background,” she conceded, noting her expertise in the health/medical sectors while also having a business educational background. And I have been working in different positions in business, but also in it. She described herself as a professional who is “trying to bring in[to] conversation the tools of business … especially in this area on the pricing and revenue management” front.
“When we talk about price optimization,” she noted that it’s a method of setting a price that takes into consideration cost, demand and the price. “And, of course, we try to maximize the market,” she said.
“The resulting price is often referred to as the sweet spot, where the demand is high and the price is still competitive, and the companies are still making money, they’re still making profit,” she pointed out. “You can also pace your decision based upon data and real insights rather than just guess work or intuition.”
She noted that’s what she would try to analyze in more detail during the 30 minutes of the webinar.
“When it comes to the types of price optimization that are available to you, you can choose from two types,” she pointed out.
“We have the traditional one or we can use the machine learning” type, she said. “In both cases, you will use mathematical analysis to determine how the customers will respond to different prices. Based on that, prices are optimized to best meet the company’s objective.”
She went on to note that, in the “second case, we have machines that will help us” through the use of machine learning.
“Machine learning is based on the idea that the systems can learn from the data and identify patterns and make decisions,” she explained. “Machine learning technology takes, for example, the forecasting data to the next step. And then you can process all this information. You can really process much larger data sets and then you can really do quick combinations and values, considering various factors. And then … with all of this information, you can try to prevent what will be the impact of these price changes.”