1. The source of business value
Every analytics project should start by identifying the business value that can lead to revenue growth and increased profitability (for example, selecting customers, controlling operating expenses, lowering risk, or improving pricing). To make the selection, business-unit managers and the frontline functional managers who will be using the tools need jointly to define the business problem and the value of the analytics. Analytics teams often begin building models before users in sales, underwriting, claims, and customer service provide their input.
2. The data ecosystem
It is not enough for analytics teams to be “builders” of models. These advanced-analytics experts also need to be “architects” and “general contractors” who can quickly assess what resources are available inside and outside the company. Unlocking the business potential of advanced analytics often requires the integration of numerous internal and external data assets. For instance, risk pricing and selection often can be improved significantly by mapping the data from internal customer-management systems with traditional third-party data providers such as credit bureaus and data exhaust from new digital sources. Given the diversity of data sources and vendors, carriers must continually scan the ecosystem for technologies and partners to take full advantage of new analytical opportunities.
3. Modeling insights
Building a robust predictive model has many layers: identifying and clarifying the business problem and source of value, creatively incorporating the business insights of everyone with an informed opinion about the problem and the outcome, reducing the complexity of the solution path, and validating the model with data.
Close collaboration among the analytics professionals who build the models and the functional decision makers who use them combines a “black box” data-modeling process (pure statistical analyses of large amounts of data) and a “smart box” filled with the knowledge of experienced practitioners. Experienced claims adjusters, for instance, have an intuitive sense about which injuries have the highest probability of escalating. Often, a hypothesis based on judgment still needs to be validated against external data. Data from claims histories will not reveal that employee relations with management or the commuting time between home and the workplace can also be factors in how long claimants stay away.
4. Transformation: Work-flow integration
The goal is always to design the integration of new decision-support tools to be as simple and user friendly as possible. The way analytics are deployed depends on how the work is done. A key issue is to determine the appropriate level of automation. A high-volume, low-value decision process lends itself to automation. A centralized underwriting group, for example, which had manually reviewed thousands of insurance-policy applications, needed only to review 1 percent of them after they adopted a rules engine. At the other end of the spectrum, automation can never replace the expertise and judgment of managers handling multimillion-dollar commercial accounts.
Integrating a new decision-support tool into a work flow can pose significant behavioral challenges. One insurer in commercial- and specialty-insurance lines tested three different ways to display information—a numerical score, a letter grade, and colored flags—to see which one led to the highest adoption and most accurate results. This kind of detail might seem minor, but such choices determine whether a decision maker uses a model or ignores it. Claims adjusters, underwriters, and call-center representatives will only incorporate analytics into their decisions if the tools address the issues in ways that make sense to them and if it is easy to integrate the tools into their work flow.
5. Transformation: Adoption
Successful adoption requires employees to accept and trust the tools, understand how they work, and use them consistently. That is why managing the adoption phase well is critical to achieving optimal analytics impact. All the right steps can be made to this point, but if frontline decision makers do not use the analytics the way they are intended to be used, the value to the business evaporates.
An insurance carrier developed a model to predict which injury claims would escalate based on the conditions and circumstances of the claimants. The system provided claims adjusters with different ways to work with claimants to help them with their recovery. The model was painstakingly constructed and efficacious, but getting adjusters to use the model proved as difficult as constructing the model itself. Successful adoption requires collaboration up front, follow-up communication as to the model’s value, and investment in training people to use it. Equally important, the heads of sales, underwriting, and claims need to be engaged so that their visions of success and expected results are built into their business plans. Business leadership is needed to ensure that all players are asking the right questions: What does successful adoption look like? Where will it have the most impact?
A center of excellence
In any major change effort, there is value in starting small and experimenting in order to learn what will work in a given company. Several companies achieved success by forming a small team that demonstrated to specific user groups the impact of analytics in two or three use cases.
The advantages of this approach are that it builds conviction and provides insights into what works and what does not. It also helps expose business needs and build an understanding of how a centralized analytics group might help meet them. Where should analysts and data scientists reside? Where should data management reside? How should the business be supported with work-flow integration and adoption? These questions can be best explored by an internal analytics center of excellence (see sidebar, “Building an advanced-analytics center of excellence”).
Weaving analytics into the fabric of an organization is a journey. Every organization will progress at its own pace, from fragmented beginnings to emerging influence to world-class corporate capability. As participants gain experience, pilots help shape an operating model for future rollouts. In the discipline of analytics, the more testing that is performed, learning that is achieved, and new data and knowledge that is applied within the organization, the better the decisions and the outcomes will be.