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Raising returns on analytics investments in insurance

By Ramnath Balasubramanian, Khushpreet Kaur, Ari Libarikian, and Noor Narula

In an era of narrow margins and slow growth, insurers’ need to invest in analytics has never been greater. A few thoughtful initiatives can accelerate the analytics journey.

Leading insurers are using advanced analytics to tap into both existing and new troves of data, in an effort to unlock value across products and business functions. In a recent McKinsey survey,1 data and analytics leaders at global life and P&C carriers reported that they were investing as much as $80 million per year in data analytics, and nearly all planned to invest more in 2017. At least half of the firms’ CEOs consider data and analytics a top-five priority.

Despite the investments and the management focus, only about one in six survey respondents reported that data and analytics were delivering high impact. The primary reasons stated for these struggles were (exhibit):

  • lack of alignment with strategic goals
  • poor integration with “business as usual” and limited frontline adoption
  • poor data quality, including fragmented or incomplete data, and accuracy and accessibility issues
Data and analytics initiatives fail for three primary reasons.

Additional barriers mentioned by survey respondents included budget constraints, talent gaps, and unclear scope.

A number of carriers, however, are finding ways to bridge this gap between the potential of analytics and the realized value. These initiatives share a common theme: insurers have to get the technology right, but it is equally important to take a strategic view of analytics efforts, and set in place the organizational, performance management, and change management processes to pave the way for true success.

Why analytics initiatives fall short

McKinsey’s survey of data and analytics leaders in the industry confirms that insurance executives are well aware of the potential for analytics to deliver value across a range of functions and products. The same research, however, points to the need for a new approach to realizing this potential. Most analytics initiatives fall short for one of three reasons.

Lack of alignment with strategic goals

Many senior insurance executives view analytics as small lab experiments or as parts of larger innovation initiatives, and fail to provide enough funding. For similar reasons, many analytics teams lack leadership support and clear mandates. Some teams attempt to solve small-scale problems without making substantive contributions to strategic goals or targets. As one insurance analytics leader put it, “It’s tempting to invest in new tools and try to keep up with technology trends. But use cases and analytics efforts need to be rooted in business strategy.”

One insurer found a way to prioritize analytics investments and align them with strategy. After an annual strategy refresh, the CEO asked each business unit to define and prioritize analytics use cases. With these priorities in hand, the senior team created a five-year analytics road map, focusing on investments with a potential for at least 250 percent annual return on investment in steady state. This clear articulation of the value at stake and investment rationales ensured that all analytics work was aligned with the organization’s strategic goals.

Poor integration with ‘business as usual’ and limited frontline adoption

To succeed with analytics initiatives, leaders need to attract the right talent, tap into the best data sources, and build accurate models, but few invest enough in driving frontline adoption, change management, or business buy-in. Without establishing new attitudes and behaviors, even potentially revolutionary insights may deliver little or no value.

About 40 percent of survey respondents cited limited frontline adoption as a leading cause of their organizations’ inability to unlock the full potential of analytics. One life carrier CIO said he now sees machine learning and analytics as commodities: “The true value lies in using data-driven insights, changing mind-sets and behaviors, and driving frontline adoption.”

Poor data quality and accessibility

About 20 percent of survey respondents cited poor data quality, data fragmentation, and lack of data access as the primary hindrances to realizing the full potential of analytics. Many insurers spend inordinate amounts of time cleaning internal data or building centralized repositories. Other carriers struggle because their analytics teams are not aware that valuable data is available in other parts of the organization—or because third-party regulations or ownership challenges prevent them from using it. A lack of enterprise data governance can cause major quality challenges for even the most advanced analytics organization.

Capturing value from analytics initiatives

Based on work with analytics leaders in insurance and other industries (from banks to grocers), McKinsey has identified five steps that can help life and P&C insurers harness the power of analytics.

Create a clear road map

The first step toward scaling analytics is creating a clear road map based on use cases that support priorities across the value chain. Each analytics initiative should be ranked objectively based on its potential value to the business. The road map should take into account industry trends, competitive factors, new business services, and possible enhancements to current products. For example, after a period of new competitors threatening incumbent players by offering “web-quick-5-step insurance,” an insurance carrier prioritized identifying prospects through the direct channel as a use case.

While each carrier will set different priorities depending on its strengths and strategic goals, several criteria typically rise to the top: the magnitude of potential benefits, feasibility, interest from the business, time to market or impact, and alignment with strategy. Prioritization discussions should involve stakeholders across the organization, including senior leaders, business owners, analytics specialists, data scientists, and project managers.

The most successful analytics efforts use early use cases to build momentum. Instead of letting a thousand flowers bloom, analytics leaders need to identify and execute one or two high-priority, high-visibility projects. A laser focus on these use cases will help the organization see the benefits of analytics quickly. The most powerful “quick wins” are typically projects with the biggest value and lowest complexity of execution. Many carriers begin with pilots in pricing and underwriting or claims, for example, where analytics have already proven their value. Some companies are applying analytics in other domains such as distribution, where they improve targeting and funnel management to boost marketing efficiency and grow the top line.

After proving success in a few areas based on these high-priority use cases, companies find it easier to build the case for scaling analytics across the enterprise. A few early success stories can also help ease fears of poor data quality, lack of talent, or inability to deliver.

Build analytics results into performance management

Leaders must embed analytics into their organizations’ DNA by making it an enterprise priority and managing to it. Across industries, McKinsey has found that success requires a robust performance management system that includes leading and lagging indicators across the business. The most effective performance management systems cascade to all levels of the organization, with metrics tied directly to business value and indirectly to frontline adoption and other goals. Many successful companies include at least four categories of metrics in their performance management systems:

  • Operational measures, such as the number of outbound calls or enrollment rates, can track the operational impact of changes during use case implementation.
  • Predictive measures, such as GINI coefficients, can reveal a model’s accuracy and power.
  • Feedback, such as voice of the customer measures and input from business stakeholders, can include qualitative and quantitative metrics to show use case implementation progress.
  • Financial metrics, especially cost management and revenue growth, are important, but lagging indicators should not be the only measures of success.

Define clear governance across the organization

Organizational transformation requires clearly defined governance, roles and responsibilities, and escalation and decision-making processes. One insurer established a tiered governance structure that included an annual review with the business sponsor to assess and prioritize the pipeline of use cases and generate new use cases. On a quarterly basis, the analytics core team meets with the business sponsor to discuss progress, flag issues, and address execution challenges. Every month, the analytics steering committee discusses the impact of individual use cases and sources of value, adjusting priorities as needed. This governance structure speeds up decision-making and forces functional and strategic leaders to align their priorities.

Launch a change campaign

Like any transformation, a data and analytics transformation requires changing the culture and the daily operating model. As one frontline employee put it, “I can’t execute it if I don’t understand it.” Partnership between business, analytics, and technology functions throughout the transformation—from selecting use cases and understanding the modeling output to executing on insights—is crucial to any analytics program.

Top management needs to role model the new ways of working, including data-driven decision making, and participate in hackathons to show support. Middle managers need to be “change catalysts,” motivating teams to work in new ways, sharing success stories, and driving mind-set changes. The front line needs to explore more collaborative ways of working. A clear and compelling change story can help in all of these instances. The most effective change stories build employees’ emotional connections with customers. For example, people who work in claims know that policyholders can be confused and upset while they wait for a decision or a check. Knowing that a data-driven claims process will be quicker and more accurate—and alleviate customer concerns—can help employees get behind the transformation.

To increase adoption, many carriers are experimenting with dashboards, iPad applications, software, and visuals to make analytics output and insights easy to understand and digest. The most valuable analytics tools integrate well with the front line’s business processes and work flow. One organization improved buy-in by including frontline staff in the design and testing of new online dashboards.

Develop new ways of working

Data access and computing power have expanded at unprecedented rates in the last two to three years, but old habits—and attitudes—die hard. About 85 percent of respondents to McKinsey’s survey said that while data guides decision making, managers often discount it because they doubt its quality and integrity. A more collaborative work environment that includes analytics experts in business discussions can help “old school” managers understand the power of analytics and make better, more data-driven decisions.

Some companies integrate analytics team members within business units to encourage information sharing and knowledge transfer. Analytics teams learn more about the business—and business leaders learn more about the power (and limitations) of analytics. More companies now put analytics experts in job rotations to immerse them in the day-to-day work of business partners and to learn about different functions. Some organizations establish formal mechanisms such as communities of practice and analytics centers of excellence to propagate knowledge sharing.


In an era of narrow margins and slow growth, insurers’ need to invest in analytics has never been greater. A few thoughtful initiatives can accelerate the trajectories of their analytics journeys.

For example, after years of experimenting with analytics, the IT team at a leading carrier was still struggling to transform the way the organization made decisions. To accelerate impact, they developed a strategic road map and established a center of excellence to serve all parts of their business. They also aggressively acquired the talent they needed. With these changes in place, they identified opportunities to deliver more than $50 million in recurring revenue. As the business recognized the power of analytics, the team more than tripled the size of the analytics organization to support the rising demands of the business.

As in other industries, the insurance leaders who systematically and comprehensively bring science to the front line will build lasting competitive advantages and deliver more profitable growth.

About the author(s)

Ramnath Balasubramanian is a partner in McKinsey's New York office, where Khushpreet Kaur is an associate partner and Ari Libarikian is a senior partner; Noor Narula is a consultant in the Boston office.
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