Every organization I know of wants to treat its data as an asset. But they lack the imagination of what that means. It can be interpreted as having robust Data Governance. Or it could mean improving Data Literacy across the organization.
We are collecting and storing zettabytes of data. But we are also failing at organizing the data to gain insights and build trust. Treating data as an asset means being able to trust the data enough to be able to monetize it – putting your money where your mouth is, so to speak.
Let’s look at four ways to monetize data and improve the revenue model.
1. Internal Data Marketplace
Imagine the scenario: A new project is about to start in your organization. To fulfill the end goals, you need good data available. Where do you go? You reach out to your data engineering or business intelligence teams. You reach out to the application operations team if source data is required to get extracts.
Activities such as these have a cost associated with them. You would be paying a fee to an internal or external operations team. Or you would be burdening an already overwhelmed engineering team.
The creation of an internal data marketplace solves this problem. It is a method of shopping for the data you need in an internal portal with a clear articulation of:
- What is the data about?
- What is its intended purpose?
- What are its limitations and risks?
- Who are the key subject matter experts of this data?
This information allows the new project team to check in/out the data.
The project team incurs a fee from their capital expenditure budget for this convenience. This fee is used to fund the running of the data marketplace. The key here is not to profit from internal teams; it is to change the organization’s culture. People start getting used to the idea of self-serving this data.
2. External Data Marketplace
This step will be easier to overcome if data is already monetized internally. The internal data marketplace will help iron out early challenges such as bad data quality. Sharing data externally will also have regulatory implications.
All regulations must be followed; the data needs to be anonymized and aggregated. It can then be offered to other businesses that would benefit from the information. The revenue model could be based on perpetual licenses of rarely moving data or a subscription model for fast-moving data.
Royal Mail has done a great job of this by monetizing their PAF (Postcode Address File). Each organization is collecting enough data in this day and age to make it monetizable. This can be classed as a data-as-a-service model.
3. Products and Services
Think outside the box – the data you are collecting and storing may only be worth thousands to you. It may be worth millions to someone else. How can you productize this information? WorldPay did an excellent job nearly 10 years ago when they published this research paper.
WorldPay provides payment facilities to major retailers. They capture millions of transactions and their data points through the payment machines. Using this information, they can show the retailers which segments of customers are the best to target and at which time of year. This information helps the retailers take tangible actions ahead of the next sale/shopping event.
Although it seems like WorldPay conducted this research for free, they could have monetized it by charging the retailers for the insight. So, can you create an “insights as a service” model using your data for other businesses?
4. Personalized Services
The top three items on this list are best monetized with business customers. What about direct customers (i.e., B2C)? It is hard to sell raw data to a consumer as they don’t care about it as a business would. You must understand what consumers care about and enable it using your data, whether that’s personalized information or insights for their service.
Personalizing services to your customer will build loyalty, as people value the personal touch in customer service. Starbucks has cracked this code with a fairly successful 25 million member-strong loyalty program. The key to this is personalization.
I use Hilton’s loyalty program; the points, free nights, and personalized hotel experience make this attractive. By personalization, they even know I’m not too fond of sparkling water, which is always replaced with a bottle of still water.
Personalization is key to customer retention and conversion in your data monetization strategy.
Conclusion
Although data ethics was not the focus of this article, it must be said that before you monetize data, clear ethical boundaries must be established, ensuring your data monetization strategy agrees on principles such as data protection regulations and the brand ethical statements.