Data pricing is part of the marketing mix
In business and economic activities where data are shared, exchanged and reused, it is essential to measure the value of data properly. While there exist many possible ways to appreciate and represent the value of data, a general approach that can be scalable for massive applications and acceptable to many parties is to set a price at which data can be sold or purchased, that is, data pricing.
The importance of pricing in business is well recognized in financial modeling, as price being one of the four Ps of the marketing mix. The four Ps are product, price, place and promotion. Lets have a closer look at the pricing element.
A series of pricing strategies and models may be considered in data markets. Keep in mind that your data product might have different kind of plans, a mix of the options.
Watch the video below or continue reading
Here's a summary of pricing strategies
1. Freely available data is often referred as open data. Free data may be obtained from public authorities, may help to attract customers and suppliers of commercial data, and may be integrated into private and not-free data products. Free data should not have price other than 0.
2. Prices can be based on usages, such as charging customers per hour of data usage.
3. Package pricing allows a customer to obtain a certain amount of data or API calls for a fixed fee. A few studies try to optimize package pricing models.
4. In the flat fee tariff model, a data product or service is offered at a flat rate, regardless of usage. It is simple, easy to use. The drawback is the lack of flexibility, particularly for buyers. A modification of the flat free tariff model is to offer subscription and limit the data query consumption based on API calls or data amount. Some kind of cap for calls is necessary to set to be able to guarantee service level. Also limiting the frequency of the calls might be needed.
5. Combining package pricing and flat fee tariff results in two-part tariff, that is, a fixed basic fee plus additional fee per unit consumed. This model is popular in data services.
Studies show that, under zero marginal costs and monitoring costs, flat fee and two-part tariff pricing are on par, and two-part tariff is the most profitable strategy.
6. In the freemium model, users can use basic products or services for free and pay for premium functions or services. Consumer might be given only limited amount of data updates during a time period as "free". More frequent updates would require payment
It is important to keep two things in mind
Firstly, your data product might have different kind of parallel plans, a mix of the options discussed previously. It rarely makes sense to have just one plan as offering.
Secondly, Your data product plan formatting should reflect the needs of your customers and their business models. Your data product design and development should be customer driven, thus your plans are aligned with their needs as well both in content and business.
Example
What about in practise? How would I use the pricing strategies? Lets have a look at truly imaginary “My data product” pricing example.
The consumer can learn and try the product since we offer freemium for them. This is also expected by the markets. Offer freemium if there is no really strong reasons not to.
After evaluating the data product value proposition with freemium, they can choose which paid plan to go forward with. They can choose either usage based pricing and pay as they go forward. Customer would be billed once a month. The other option is to subscribe the the data product and get value more than in the other packages. In Premium customer gets huge amount of data updates, can do it frequently and all this just for fixed flat fee.
What happens if the customer exceeds the 50 000 updates? Well, they are served but any additional updates cost 0,05€ per update.
As a data product owner I want to push all my customers towards Premium, not just because the name is cool, but because it means reoccurring revenue.