Data Monetisation through embedded analytics is the business model that drives revenue channels by leveraging data and analytics in a company’s product/application/website. According to a survey from McKinsey, the practice appears to correlate with industry-leading performance, driving growth in areas such as customer acquisition and retention; value derived to the customer; revenue; and migration to profitable segments. And more and more businesses are signing up – statistics show the data Monetisation market is estimated to reach $15.4 billion by 2030.
But to make the case for data Monetisation to your company, you’ll need to first understand what it looks like in practice, the potential benefits it holds, and which tools to adopt.
Data Monetisation models
The first thing to understand is that there are two main models of data Monetisation; direct and indirect:
- Direct Monetisation: Pertains to external approaches such as selling data directly, information bartering, offering value-added products or services, or sharing data for better conditions in a business arrangement.
- Indirect Monetisation: Using analytics as a proof point that your product or service has made a positive impact on customers, thereby influencing areas such as customer acquisition and retention, engagement, and adoption.
Examples of data Monetisation
As mentioned, your unstructured data might hold unknown value to different stakeholders within or outside your given vertical. Example use cases could include:
- Retail: Leveraging customer browsing and purchase histories to provide personalized recommendations, thereby enhancing the customer experience and increasing sales.
- Healthcare: Sharing anonymized patient data for clinical trials or medical research, creating revenue through partnerships with clinics or hospitals.
- Financial services: Using transactional data to develop more robust risk assessment models and fraud detection systems, then offering these services to other financial institutions for a fee.
- Manufacturing: Utilizing sensor data and ML to offer subscription-based predictive maintenance services to other manufacturers.
- Logistics: Using real-time traffic and transportation data to optimize delivery routes, reducing fuel costs and improving efficiency, and offering this as a service to logistics providers.
Implementing your data Monetisation strategy
Google Cloud provides a suggested hierarchy for how different Monetisation strategies should be laid out. This can help your company to investigate which option (or options) are best suited to monetizing your data:
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Indirect Monetisation |
Direct Monetisation |
Diamond |
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