Analytics is emerging as a critical capability for growth in the insurance space. Over the past decade, most of the advancements in analytics have been made by the consumer P&C companies, like Progressive, AIG Direct and Allstate. Increasingly, however, commercial insurers are also catching up and leveraging analytics for different areas of business in addition to pure actuarial analysis – Fireman’s Fund has been developing predictive models to address loss from fraud and subrogation; AIG employs data mining technology to target cross-selling opportunities, as a result, around 45% of its customers buy three or more products; AXA is using data mining to provide leads to the agency force.

• Prioritize To Improve Life-Cycle Analytics – Conduct a strategic review of the consumer life-cycle areas and focus on areas that will generate maximum returns. The prioritization will depend upon a host of factors – competitive situation, strategic goals, etc. Once the initial strategic priorities have been set, create a well defined strategic imperative with clear company wide communication and change management. Upfront C-level sponsorship is critical
  
• Improve Cross-Organizational Data Collection -- Collect cross-organizational data from different internal systems. Getting the right data is just as important, and build the data asset in a way such that future augmentation to conduct other analysis is easy. Design a comprehensive data gathering and storing process, in addition, develop technology to transform, clean, and link all data components. Sometimes it is prudent to create this data asset outside of the existing production systems in the short term given integration challenges. Establish guidelines for data/model access and use
  
• Augment External Data – Supplement internal data with external information, such as credit bureau data, demographic information, vehicle information, and / or sector specific statistics, to improve the accuracy and effectiveness of the marketing models
  
• Analyze Data – Develop analytical models to generate specific lists, guidelines, and rules that the frontline staff should act on, e.g., retention lists and cross sell lists. In addition, establish well-defined feedback cycles to ensure that there is a test and learn culture and models can be refined based on new information. Set up guidelines for analytics processes and enable best practice sharing across functions/ businesses.
  
• Monitoring and Governance -- Last but not the least, set up an ongoing monitoring and accountability process to ensure meeting ROI. Appropriate governance and funding mechanism is needed to ensure appropriate resources for these tasks. No amount of analysis can drive success unless the right information from customers is not being used to build the models – frequent reviews (e.g., rate pursuits) need to be conducted to ensure that the data that is being collected by agents is accurate