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Pricer’s Points: Leveraging Big Data in a Big Way – Automating Complex Pricing With Data Science | Marc Folch

As paradoxical as it may sound, the primary role of a pricing professional is rarely to set pricing. It is rather to collect and distill the data needed to make optimal price setting as obvious as possible. If done right, setting prices becomes nearly an afterthought, and can even be automated for highly complex and dynamic environments.

Given the following information about an in-market product …

… how hard would it be to select the optimal price? Let’s break down this graphic and delve into why each component is important when leveraging big data for pricing.

COST (Fully Integrated Cost Per Unit)
Salespeople need to consider not just how much a product or service incrementally costs to deliver, but also the attributable overhead and ancillary costs such as customer service, returns, bad debt, etc., which are required to make and service that sale. This helps establish the bottom bookend of where price can be set. Read more in Stop Segmenting Customers by Revenue!

COMPETITORS (Competitive Pricing)
Competitor pricing helps establish an upper bookend for sales. It must of course take into account the intended positioning of your product (discount/value/premium/etc.).

PRICE SENSITIVITY (Gross Profit = Volume x (Price – Cost) @ Each Price Point)
Price sensitivity charts how demand changes with price and allows the calculations of a net profit curve. It can be collected via conjoint testing, micro-tests, mining big data in complex pricing environments, or several other methods.

Such perfect information is generally only found in university textbooks, but the closer a pricing team gets to this level of insight, the more accurate and effective their pricing becomes. This is the true goal.

What is different from traditional methods is that data and automation allow us to build this level of granularity and accuracy for every product, in every market segment. From there, we can build rules to automate much of the decision making and alert us to any changes of note.

The process should be automated into a perpetual stream of data collection and summarization, which in turn enables the pricing team to react quickly and intelligently to market changes. The automation also frees up the team’s resources to focus on new innovations that add incremental value to the organization. It’s a capital crime to waste innovation time on simple, repetitive tasks that can be automated, and they should be ruthlessly eradicated whenever possible. There is always a better way.

I encourage pricing professionals to look differently at how you determine optimal pricing. Increase your price quoting confidence by tapping into big data streams that continuously provide highly accurate insight. Ad-hoc analyses and setting rates based on “gut feel” won’t give you the sales edge you need to compete in today’s volatile market. By leveraging big data in a big way, the resulting pricing speed, effectiveness and flexibility can give companies a competitive advantage that can be leveraged to increase profitability or market share.

* This article was originally published on the Pricing Leadership Blog

About the Author

Marc Folch is a pricing and strategy innovator whose focus is on leveraging both big and small data, to change the game for companies. Entrepreneurial and adaptive, Folch has a passion for taming complex datasets and strategic challenges into actionable implementation plans. Folch brings a diverse background to problem solving with experience in telecom, hospitality, pharmaceuticals retail, construction, real estate, and online marketing industries. He graduated Dean’s Honors with an MBA from Ivey Business School. You can connect with Folch on LinkedIn or via his personal blog, Game Change Ideas.

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