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.
Pricing—long understood as a blend of art and science—has a profound impact on revenue generation and the ability to build an effective business case for creating data products. Generally, there are two primary ways to construct prices for data products: cost-based and value-based.
Cost-based pricing
Cost-based pricing is frequently referred to “cost-plus pricing”; the two terms can be used interchangeably without confusion. In brief, this pricing strategy determines a selling price by adding a markup to unit cost. Vendors need to assess the fixed and variable costs required to build, maintain and deliver data products on DataStreamX. These costs are classified as set-up, personnel, overhead and on-going IT costs.
The good news for Vendors is that the fixed costs associated with creating data products for sale on DataStreamX is typically very low relative to previous methods of delivery. The tech investment involved can be as simple as adding snippets of server-side code on existing infrastructure. Likewise, variable costs also could be nearly negligible.
This means that it is very economical for an organization to get started selling data, however, it also means that a cost-based pricing strategy may be ineffective. Total costs could be very small and thus the corresponding mark-up would be orders of magnitude greater than those in other industries that use cost-based pricing (such as manufacturing).
DataStreamX recommends that data Vendors complete this costing exercise for evaluating costs, but not for setting actual selling price. Vendors should use their cost structure as the absolute minimum amount to accept for their data products. If priced lower than this, the organization would lose money on every sale. Instead of a cost-based pricing approach, DataStreamX suggests using a value-based pricing strategy.
Value-based pricing
In software and services, the value a customer receives from a product is many times the cost of producing it. With value-based pricing, Vendors attempt to capture the majority of what customers are willing to pay for their data products. By no means is this an easy exercise. Value-based pricing requires many considerations and a critical self-assessment.
As a marketplace for digital assets, DataStreamX endorses a value-based pricing philosophy, we will devote the remainder of the guide to walking through the value-based approach and highlight the key considerations throughout the pricing process.
Unfortunately, we can’t say what the exact price is for a point of data itself. However, we can help Vendors consider the most important factors for determining the price to sell data at. We will cover these factors in the subsequent as we detail customer identification, value levers, product scoring and pricing strategy. Stay tuned for more as we expand more on how to price data.
You can also skip ahead and download our complete pricing guide below. As always, we encourage anyone interested in joining the data economy to register with DataStreamX and browse our marketplace. Check us out at www.datastreamx.com today!
