Customer Data Monetization

Customer Data Monetization means that the user gets the service (for free) and the company sells the data to a partner.

Description

The idea behind the “Data Monetization” pattern is to generate revenue out of available data or real-time streamed data. Examples of data sources are social media, portable devices, products or services accessed by the user and used in financial transactions. These data can be combined with external sources, like geodata, weather and data from objects (Internet of Things).

There are different kinds of value propositions that can be created by selling:

  1. Raw data
  2. Processed data
  3. Insights

The aim of this pattern is the sale of data. For information on creating additional value out of data, see “Data Leverage” business model pattern (follows soon).

When collecting, processing and selling user data, it is important to respect ethical considerations, like data privacy and the sensitivity of data.

Success factors

Internal data

Identify data from your customer that you are already collecting or that you could collect from now on regarding the digitalization of customer touchpoints.

Combine internal and external data

Identify external sources that may enhance the quality and value of your data.

Define value

Define the value / product / service you want to create by selling the data to a defined target group. Consider the amount, quality and uniqueness of your data.

Understand regulations

Understand regulatory restrictions related to data acquisition, use and disclosure. Data regulation can be complex and is changing rapidly.

Transparency

Transparent and respectful use of user data.

Examples

Facebook
Analyze and sell user data to third parties in order to place personalized advertisements.

PatientsLikeMe
Gather customer data on diseases, anonymize them and sell them to pharmaceutical and health companies

Telefonica, Verizon, Orange
… and other telecommunication firms sell data on the locations, movements, web browsing habits and other data categories to third parties.

Payback
Loyalty card for different retail businesses: customer can collect points and redeem them – Payback tracks customer behavior and sells the information back to retail businesses.

Chances

  • Create new / additional revenue streams
  • Offer a user service for free
  • Create win-win business models for users and customers
  • Let users participate by taking a share from the revenue created

Risks

  • Disrespect of data privacy and loss of reputation
  • Cyber attacks

Responsibility

Respect of data privacy and sensitivity, ethical use of data. 

Be careful with using, combining, selling and storing personal data from your customer.

Transparency towards users

(what data is sold to whom): information about privacy and security practices should be easily understandable and accessible.

Resources

More inspirations

Share your thoughts and experiences

1 Comment

  1. Katrin Mathis

    Great summary of this business model pattern! Data has the potential to become a true alternative to financing free offerings through advertising. In my opinion trust will become a key resource for companies. The more trust they earn also through transparency, the more data users will be willing to provide. I’m looking forward to further patterns to be described.

    Reply

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