Analytics tools are evolving and rapidly being adopted across the insurance industry. While many insurers have made significant investments and great strides in implementing business analytics software, many are also struggling with effective use of the resulting management information. A successful analytics program should provide a positive ROI, which, in turn, requires taking it beyond software and reporting and into the realm of management practices and operations improvement.
A sound approach to supporting analytics-driven improvement efforts includes the following steps:
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Success results from translating analytics into operational improvements. This is often achieved using some of the following techniques:
1. Benchmarking
Benchmarks are an element of business analytics, and they should be viewed as a means to an end. The end purpose should be to improve or validate performance, not to simply take measurements or make comparisons. If you embark on benchmarking, establish a solid link between the resulting analytics and a comprehensive activity-based management program. In other words, formally incorporate the benchmarks into your ongoing improvement initiatives. Benchmarks are often thought of as an external comparison (e.g., relative to peers, competitors, or “the industry”), but benchmarks are just as valuable when used internally to analyze different geographic locations (such as service centers or sales offices) to determine performance strengths, weaknesses, and best practices.
2. IT Assessment
IT costs and allocations are often not clearly understood. In one recent example, an analysis of resource utilization and allocations revealed that an insurer’s IT function was costing far more to operate than industry norms. The data indicated that on the development side, over 40% of the activities being performed were not critical and could be streamlined, consolidated or eliminated. This provided a business case to develop and implement a new project/portfolio management process that would result in a capacity increase of 10% to 15%. Ultimately, this lowered overall IT costs and recast IT cost allocations more equitably.
3. Cross-Functional Expense Views
Many financial service organizations view expenses from a functional or business unit perspective. Functional managers are responsible for managing these expenses within the scope of their organizational structure. Business analytics tools can provide new and different views of expenses that can, in turn, provide new insights into opportunity areas. Expenses that seem insignificant within a given unit can be significant when viewed more broadly. For example, expense line items such as travel can be selected and views created at various levels, such as enterprise, function, line of business, region, office and even purpose. By using the ability to view data in a variety of ways, potential opportunity areas can be quickly identified. Further investigation supported by management action can then drive more effective policies, improved consistency and reduced overall expense.
4. Unit Costing
Marrying expense and volume data from different perspectives can create some interesting insights into unit costs. Unit costs are an ideal metric for measuring efficiency within an operation by simply looking at how many pieces of work the organization is getting and how much it is costing to get each piece done. The key here is to focus on the core work volume and understand its definition. If a global view is desired on the expense side of the equation, all expense line items can be included, as opposed to limiting expense scope to personnel-related costs. However, both views can be valuable, depending on the audience. Limiting expenses to personnel costs provides a more precise view of productivity management effectiveness. The global view is a bit more complex in that it requires allocations of overhead, but it can provide a true view of what a function or process costs an organization.
5. Product Costing
Product costs are useful for evaluating product pricing and profitability. Accurate views of product costs require a fairly sophisticated business analytics capability, but the value can be significant. Understanding costs at this level can help to ensure that products are priced effectively and profitably in the marketplace, and to determine when it makes sense to under-price a product in order to gain market share. Identifying unprofitable products can lead to their redesign or elimination in order to improve overall profitability.
6. Incentive Plan Financial Soundness
The key here is to determine if there is a direct relationship between business results and what is being paid to employees in variable compensation. Ideally, business result trend lines and incentive payout trend lines should match one another to a degree, with the business result line climbing at a faster rate. Business analytics data can be developed for key business metrics and corresponding incentive payouts. These become the basis of incentive plan fine-tuning or redesign to ensure ongoing financial soundness and ROI.
In Summary
The examples I’ve provided here are just a few of many possibilities. With techniques and tools evolving at a remarkable pace, we have really only scratched the surface of business analytics. But the basics remain the same: organize data so it “tells a story,” and then use it to drive performance and profitability improvement. The speed, scale, and accuracy with which you do this can create major cost and competitive differentiators for your organization.
Mike Meyer is a senior consultant at
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