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#31 Value-based pricing for your data

Where solution and value sales have replaced traditional product sales in many industries, pricing models have also changed radically. The same mindset where pricing through value is considered can also be applied to data, because according to the ideas of our other blogs, data should be considered through what benefits it provides, to whom and what the impacts are. As we said earlier, data usage planning needs to start with your own business needs and identifying these needs from potential data customers is also the first starting point from which value-based pricing begins.

That is, the first thing to think about is what kind of problems or questions someone else can solve with the data you own. This reflection becomes an understanding of potential customers or their business needs. This allows us to consider what value the answers to these questions would be for these customers, thus understanding what value the data produces and what its business implications are. This can be so difficult to comprehend at the very high level, but as a practical example, we often use the example of sales organization data. If the customer segment you have is the same as another company you know and the services offered to customers are logically similar, you have a good opportunity to exchange data with each other about how the sales organization is organized, how they are measured, how they perform, how they are compensated, etc. And if evalueted impact would be even a 10% increase in sales performance, then your data can be model another organization’s sales operations completely differently. For that 10% improvement, a value can be calculated from which to get a price for your data.

Once you know what value data generates and what its business impact is on potential customers, it’s time to think about what collecting data will cost you. For this, there is already a good blog post on cost-based pricing that lists the factors that affect costs. Once you have calculated the actual cost of producing the data and compared it to the value that the data produces, you will understand whether producing the data is profitable and if so, how profitable. If, on the other hand, it is not profitable, then you should strongly consider whether this data and these customers to whom it is worth going to sell the data.

So the chain goes

  1. Customer

  2. Business question or issue

  3. Answer and value

  4. Cost

  5. Pricing

In many cases, when considering customers and potential customer segments, it is worth looking beyond your own business ecosystem. In principle, many companies are not ready to sell or exchange their own data with competitors or actors they may not know, but when using data, it is worth looking outside your own country, industry and customer segments. It’s very likely that you don’t know and can’t even imagine who all of your data could benefit from. Also, most innovations need data for validation, which leads to the fact that active sales of data in different channels may generate revenue from completely surprising parties.


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