Fiscal policy uncertainty, changing economic direction, and even election results are perennial concerns that must be considered now more than ever as leaders maintain their focus on competitive advantage and sustainable profitability. The challenges are complicated by rising consumer expectations, market diversity, distribution mix and technology advances. In seeking new ways to improve performance, leaders are finding big data analytics to be a significant game changer.

The Source of Competitiveness
Our experience shows that market advantages and success come from a thorough understanding of markets, products, customers and distribution. Specific examples of how additional information and analytics can help companies differentiate include:

  • personalize customer bills and correspondence in a relevant manner
  • reach out to offer additional coverage at the right moment
  • quickly evaluate customer value and risk
  • provide tailored service to each individual customer
  • rapidly pay claims while aggressively lowering fraud costs
  • price coverage that provides competitive advantage

In today’s world, developing the information to guide and vector such efforts requires mastery of “big data” – a.k.a. the collective data repositories in your company and from both inside and outside sources. Effectively mining this data provides powerful insights and makes the difference between leaders and followers.

Where Do We Go From Here?
There are four areas where big data analytics provides the most significant results. In priority order based on impact, they are:

  • Underwriting - including rate assignment, achieved by modeling at a more discrete level based on deeper individual and categorical risk data.
  • Claims - emphasizing fraud detection, litigation management, subrogation, salvage and recovery, repair coordination, work distribution across staff expertise of specific claims traits, concurrent triggering of requirements and automated escalation.
  • Distribution - improving effectiveness by looking at saturation levels of agents in a region, training deficiencies based on placed business ratios, compensation mix by profitability, total portfolio performance, ideal prospect identification and profitable customer retention.
  • Service - looking at lifetime customer economic value (LEV) across product lines and distribution channels, unique service requirements by segment, awareness of differentiating service touch points, sensitivity to personalized needs and delivery of services across methods and timeframes.

Two relatively new uses of analytics have been rapidly growing in importance and use, especially with the expanded use of social media: sentiment tracking and social intelligence. Sentiment tracking involves building indicators of external feelings towards a company based on posts in social media platforms like Twitter, Facebook, blogs, and LinkedIn. Social intelligence is more controversial as it involves using publicly available data, including social media posts, for hiring, claims or underwriting purposes. Shopping habits, rewards programs, magazine subscriptions, travel habits, hobbies and social media posts are all harvested and mined for information. For both, analytics integrates the acquired data into models that inform business processes. While not all companies track sentiment or mine social intelligence, it seems inevitable as more and more data is made electronically available willingly by individuals. Especially since models using this data show it is relevant to insurance processes.

Tying It All Together
Finding competitive advantage requires starting with a strong foundation of people, processes and technology. Effective use of “big data” relies on this operational tripod. Given the speed and diversity of changes underway, agile companies recognize the importance of thoroughly reviewing business processes within this context and at an enterprise level across all product and customer lines. Market advantage is often found in discrete questions like:

  • What data is consistently available for decision making?
  • Is there a defined process for translating findings into management actions?
  • Are business processes streamlined and easily understood?
  • Are effective communication and quality checks integrated?
  • Are customer needs and expectations clearly addressed?
  • Is there a clearly understood rapid escalation path for unexpected problems and issues?
  • Are the systems and interdepartmental interfaces efficient and reliable?

Profitability in today’s rapidly advancing markets requires effective translation of vast knowledge into differentiating business capabilities that can be delivered efficiently and effectively. Anything less fails both the customer satisfaction and sustainable profitability tests.

If you’re seeking a place to start and interested in learning more about how market leaders are leveraging the operational value of analytics, what the new trends in social intelligence and sentiment monitoring are, or how companies are using big data to draw conclusions, I’d welcome the opportunity to talk with you. Two potential resources are Nolan’s analytics maturity and process maturity models – which together define what’s needed for market-leading data translation and process design.