As with any product or service, generating demand relies on clear marketing. Marketing messages that communicate benefits, features, attributes and emotions that potential buyers easily identify with have higher likelihood of conversion. This holds true with data products as well. One of them most useful pieces of marketing content is a clear data schema describing the information contained in a data product.
Hello, again! This is the third part of our four-part series on pricing data products. So far, we have discussed the methods to price data as well as a a framework to thoroughly understand customers and what value a data product is actually delivering to them. In this installment, we will discuss nine (9) different price-to-value tradeoffs that determine how a given data product will compare to a customer’s maximum willingness to pay. Many of these value drivers are influenced by customers expectations, i.e. what are minimum requirements or benchmarks for data products of a given type.
Read on for more on how to price data! As always, subscribe to our blog to stay informed and continue on below for detail on the nine pricing levers of a data product.
Publicly-traded or privately-invested, all companies seek growth. Companies measure growth through a combination of variables: revenue, profits, user base, market share, share of wallet, share of closet, share of etc. The key similarity between these variables is their measurement of today’s metrics versus yesterday, last week, last month or last year. Growth manifests in a few forms: organic (growing existing businesses), inorganic (acquiring new businesses), new ventures (diversification) and “eureka moments”--surprising capabilities that companies quickly embrace and institutionalize. We call this phenomenon "Bolt-On Revenue."
Welcome to the second part of a four-part series on pricing data products. In the first installment of this series, we explored the two key methods for pricing any product: cost-based pricing and value-based pricing. For data products, value-based pricing represents the most logical approach as the marginal cost of reproducing data approaches zero quite rapidly. In this posting, we will provide a framework to thoroughly understand customers and what value a data product is actually delivering to them.
As always, subscribe to our blog to stay informed and read on for more detail on knowing your customers after the break.
Welcome to the first part of a four-part series on pricing data products. Setting the price of data products will be among the most important decisions to make as a data Vendor on the DataStreamX marketplace. This series will deconstruct the pricing process into a framework around value drivers, pricing levers and strategic decisions on pricing. Subscribe to our blog to stay informed and read on after the break.
