During the process of data servitization, you come to the situation that a price must be set for the data you are selling. But how to determine the price and how to maximize income? Let’s first tackle the pricing and then go for the tiering strategy.
Pricing your data
Here are a few factors that determine the commercial value of your data.
Your customers will look to gain an edge from your data. It needs to be either faster or more accurate than what they are using, or it must offer a unique insight previously unavailable to them.
Customers should be able to convert the edge offered by your data into trading or investment profits, via a clear and straightforward monetization strategy. The more direct the connection between your data and a profitable trading strategy, the more valuable your data.
The more unique your data, the more valuable it can be. Are there others who can replicate either the data you have or the signal you are likely to produce? Are other versions of your data available? Are there proxies that achieve the same purpose? If the answer to all these questions is no, then you are likely to have a very valuable data asset.
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Maximizing the income
What is often not yet understood is the tiering. One way to maximize income while remaining narrow is to build a variety of data products from the original source data asset.
You can slice it and dice it differently, depending on the profile of the firms you are targeting: fundamental versus quantitative, small versus large, hedge fund versus investment bank, fast versus slow access, and so on.
This allows you to sell essentially the same data multiple times without diminishing its alpha.
This is also something that 90% of the companies we assist in data monetization understand after we just get started with one product. During the process, we can introduce new opportunities and finally, the customer starts to innovate on their own.
If we agree to simplify things a lot, the data value spaces can be divided into three circles. In the center, we have data customer space. On the outer ring is the data commodity owner space. In between the two is the value co-creation space - the space in which the magic happens.
Some of the data services are focused on co-creation and thus drawn in the illustration as bigger boxes and centered in the value co-creation space. Example of such data service is visualized dashboard-ish service in which data customer analyses and shapes the data to fit for their needs as self-service. Data commodity owners main task is to develop a service that enables needed data variations.
In between the two is the value co-creation space - the space in which the magic happens.
Data streams box is the classical example of productized data offered via API. The API access to the data stream converts the data product into data as a service. In other words, data is servitized.
Dataset is the also one of the classical collected sets of data points. The customer rarely has much to say or the capability to modify settings on the fly. It is what it is and content is defined by the data commodity owner. Not so value co-creation rich approach and thus data markets are going for the servitized options.
Some say that data stories are the next-generation dashboards. Data stories can be a stand-alone service but I see those also as means to attract customers to the data-driven dashboards. Data story gives you the beef in a nutshell wrapped inside of a narrative. It also might offer links to details that can be found from the dashboard-alike service.
As you can see, there are multiple opportunities to sell essentially the same data multiple times without diminishing its alpha. You need to be obsessed with the customer needs and their processes. Each of the examples discussed serves different purposes. Be bold and explore the opportunities - we are here to help you!
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