If your objective is to determine a value price for a new product, then using a value model can be very helpful. That seems obvious, but did you realize that value models are used for other strategic decisions too?
Value models provide a standardized, structured approach and common language for teams discussing complex customer and competitive issues. They create an elegant visual framework that quickly summarizes a lot of data on hand while filtering out extraneous data noise. This greatly enhances team collaboration and improves the quality of output, not to mention increasing alignment. It is a beautiful thing to witness a cross-functional team experience a major “a-ha” moment when they uncover a breakthrough strategic insight through the value modeling process.
Over the past few years I’ve built and reviewed hundreds of value models as a coach for B2B managers. During this time I’ve come to appreciate the versatility of using value models to help product and marketing teams think through strategic options. They can reveal important insights about product development, market segmentation, marketing messages, and sales execution. The table below summarizes the most common use cases.
Although value models are quantitative in nature, they are very good at enabling qualitative thinking such as brainstorming, hypothesis testing, and scenarios. Often they surface as many questions as they answer – which is a good thing because the resulting questions are much more focused. Recently I had the opportunity to work with a product team who used a value model to decide whether or not to invest in building a prototype for a new product concept. Not only did the value model give the team more confidence in moving ahead to the next stage, it helped them prioritize the critical customer questions they needed to answer for their launch strategy.
Value models do not provide heavy-duty data analytics and their output is not statistically significant. When I coach customers, I like to tell them that, ultimately, the value logic is more important than the data. Updating and upgrading data is a straight-forward task. Upgrading logic (i.e., strategic thinking) is a much harder challenge. To put it another way: what would you rather have, excellent logic with less accurate data, or perfect data with faulty logic? It’s the difference between arriving nearby your desired destination versus arriving precisely at a wrong location.
About the Author:
About the Author Ed Arnold is VP, Products at LeveragePoint. Previously, he held senior positions at Communispace, Diamond Management & Technology Consultants, and OmniTech Consulting Group. He directs product design and development and drives the go-to-market strategy for LeveragePoint. Mr. Arnold holds an MBA in Marketing from New York University and MA and BA degrees in Political Science from Boston University.
