With traditional sources of revenue decreasing over time, insurers are looking at new avenues of revenue growth. This brings in a shift in the mindset of perceiving insurance as services as against a product (or policy package). Value-enhanced services offered through insights generated from right combinations of data and algorithms are on the rise. The next step in data and analytics value enhancement is to monetize data to reap business benefits.
How to monetize data
Value from data can be derived in different forms. As insurers create and deploy data-centric business strategies, the following forms of value generation should be closely looked into:
- Differentiation - Using analytics, insurers can build distinctive capabilities in the market and establish new revenue streams. Common examples include IoT solutions and subscription services through connected car, connected home and connected health measures.
- Incremental increase - Driven by data insights, insurers can create new business use cases and offerings and help generate incremental growth. Common examples include value added affinity products and new lines of business.
- Resilience - Using right data, insurers empower agents and field force to build resilience in demand generation. Common examples include targeted marketing based on segmentation.
- Value enhancement - Improve accuracy in predictions and identify areas of missed opportunities. Common examples include underwriting efficiency, accurate business services classification and subrogation.
- Disruption – Insurers now have an opportunity to leverage Investments in big data and analytics to offer new services that redefines and addresses customer risks in new ways. Some examples include on-demand mobility risk exposure insurance, multi-mode journey risk insurance, risk avoidance solutions and smart contracts.
Other data monetization use cases in insurance include -
- Establishing an effective risk-based pricing system driven by fundamentals and supported by new age data collection systems such as IoT and telematics
- Identifying opportunities for co-creating new insurance products with industry partners
- Building new age insurance products such as cyber insurance
- Using weather data patterns and prediction models for risk assessment
The hurdles to data monetization
Some of the typical challenges faced by firms in initiating efforts to monetize data include:
- Data privacy - Insurers are obligated to ensure complete protection of customers’ personally identifiable information. In addition, certain privacy laws restrict use of customer data for further aggregation, analytics, and prediction activities.
- Data quality – In order to derive value, firms should have trust in their own data. Ensuring the correctness of data is the important first step. While Digital first systems have an advantage of starting with clean data, many business critical systems use inherited data, systems and policies that need to be treated before they are put to use.
- Algorithms - Selecting the right algorithm to provide predictable and auditable results is critical to the adaption. While algorithms are updated at regular intervals, the verifiability and auditability of results over time ensure reliance on the algorithms.
How to deliver data monetization initiatives
- Define and set meaningful and audacious goals for value creation
- Establish business, organization and analytics capability paths to meet the goals
- Establish an analytics platform and a measurement framework that provide the foundation for technology, process and performance
- Create data and information assets for long term consumption and governance
- Identify business use cases and engage through a compatible business model
- Establish a formal operations center to build and scale the use-case deployment model
Conclusion
For data monetization efforts to be successful, insurers should explicitly identify growth areas, design right framework, conduct experiments, deploy suitable analytical platforms, implement and industrialize solutions, and fine-tune strategies. In a hyper-personalized customer environment, pursuing data monetization programs is not just an opportunity to look for new revenue streams, but also a necessity for sustenance.
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