Rapidly advancing online and mobile technologies continue to empower global customers and propel changes in customer expectations. In addition to competitive prices and features, customers are looking for insurance products and services tailored to their individual needs. The insurance industry, like many others, faces the challenge of rapidly and effectively adapting to changing customer needs.
To achieve profitable growth, with continuing pressure to manage cost, insurers need to rapidly innovate their business models. In line with this, insurers also must implement carefully crafted strategies to provide better customer experience and deliver tailored products and services. An important step in this direction would be to harness the power of “big data" and advanced analytics.
To leverage the power of big data or analytics, insurers should begin by integrating data silos, improving data consistency and distribution, and using new sources of both internal and external data. The next step will require employing advanced analytics to extract critical customer insights from the data and exploit the extracted insights to improve products and services, optimize processes, and enhance customer satisfaction.
Employing advanced analytics can help insurers in multiple ways, such as:
Customer segmentation and targeted strategies: Historically, insurers have segmented their customers based on socioeconomic factors and product holdings. However, today leading and competitive insurers are leveraging predictive analytics to segment customers based on values and behaviors. Predictive analytics allows insurers to segment their customers into homogeneous groups which share similar characteristics, expectations and needs, and to develop strategies targeted towards specific customer segments.
Predictive analytics also allows insurers to:
- Devise and implement strategic actions to improve customer experience and design specific offers for each segment. In addition, predictive analytics assists in identifying opportunities to cut cost.
- Align customer microsegments with a modular distribution model. This enables customers to access services through multiple channels and helps insurers improve customer experience.
- Reach out to the right customers and ensure effective service by aligning their sales and service agents using insights on agent traits and demographics.
- Identify reasons that trigger customer attrition, create retention strategies, implement agent behavior modifications, and devise incentives to reward customer loyalty.
Mining unstructured data: The exponential growth of data is pushing insurers to turn available data from a liability into strength. To understand customer behavior and gain valuable business insights, extracting information from unstructured internal and external data sources has become very critical for insurers. For example, mining call center logs enables insurers to identify and mitigate key customer problems for better customer experience and opportunity identification.
Real-time analytics: Social media and other digital channels of customer interactions have created opportunities for real-time data analytics. Analysis of this data generates real-time customer insights and assists in creating better customer experiences. For example, real-time analytics can be used to reduce the risk of fraud while accelerating speed of processing claims, which, in turn, improves customer experience. Self-learning models can provide guidance to adjusters, handlers, and representatives for continuous improvement while distinguishing fraud from non-fraud cases.
Internet of Things: Sensors record and transmit data in structured and unstructured form in huge volumes. Employing big data technologies and advanced analytics to analyze this data will provide insurers with a significantly more accurate picture of the exposures, hazards, and risks of what is being insured. This in turn can help insurers to reduce cost and provide policy holders better premiums.
Web Analytics: Methods such as search engine optimization (SEO) and paid search campaign effectiveness analysis help to enhance and improve the extended network of customer touch points that can significantly improve service, sales and reputation of insurance companies.
Measuring and evaluating business “actions": Scoring and analyzing the impact of implemented actions helps insurers to understand successes and failures. Evaluating business actions can ensure continuous improvement for enhanced customer segmentation, customer experiences, and targeting. “Test and learn" approaches are essential to determine which interventions are most effective.
Insurers are at different stages of this journey towards customer centricity. This evolution requires significant change in culture, new technology adoption, and the development of new capabilities. A powerful strategy to make this journey more efficient and effective will be to collaborate with established partners across the value chain and outsource non-strategic business tasks.