Increasingly, insurance agencies are realizing that the dispersion of their data across systems reduces their visibility of customers and operations, and creates the potential for inaccuracy. The question is how to address the problem. Some agencies opt for a CRM (Customer Relationship Management) system to give them an all-round view of customer interactions. Others want to stick with an existing system, like their AMS, but would like to improve the data in it. Automating the process of checking and consolidating data would make life easier and would allow agencies and brokers to focus more on customer service than on identifying errors.
In every insurance agency, the information critical to the running of the business is spread across different systems that don't talk with each other - policy management, lead generation, customer engagement, etc. Most of these systems have information about customers that is partial and sometimes wrong, which makes it increasingly challenging to have a clear view and understanding of the customer. This lack of data visibility and accuracy affects the customer experience by hindering the way that insurance agents and brokers interact with clients and manage their policies. These issues can be the reason why a customer decides not to renew a policy. With customer attrition rate averaging 10-12 percent per year, reducing attrition by a few points would result in a major improvement in profitability. The journey to unified and improved information starts with collecting clean and complete data.
Whatever Agency Management System (AMS) is used as the initial source, there is a high likelihood the data in this system will contain errors or missing entries. The data must be checked, transformed, and corrected before it can be used which can be complex. With the volume of business data increasing, unifying elements of data from different sources to achieve a more complete view of the customer is also becoming more complex. The ability to scale to larger data volumes requires an approach and a platform that can handle large amounts of data, and cleansing thereof, easily. Once this is done, insurance agencies will have a clear view of their clients and will be able to quickly identify when their policies need to be updated or renewed allowing them to retain valuable business.
Before data can be cleaned, it needs to be extracted from operational systems and stored in a staging area. This staging data store is where operational data from different sources can be consolidated under one data structure. This allows Synatic to act as a middle layer during data integration and migration stage. By ingesting data from a source system and using code-based logic and parameters, Synatic performs checks on data to ensure that the data is correct. For example, when a client's policy is due for renewal and an email needs to be sent to the client, having the correct email address is paramount. When email addresses are integrated from the insurance agency’s AMS systems into Salesforce where agents can engage with the client, Synatic can put rules and parameters in place to identify whether an email address correct by identifying if an email address is missing a .com at the end of the address or an @ sign in the middle of the email address. Rules and parameters can be specified by the agency to ensure that the data appears in the destination systems in the correct format.
In addition to rules and parameters used to cleanse data, Synatic also uses look ups to push data from one system to another while simultaneously making sure that the data surfaces in the correct fields. What is more, if the source system has specific drop-down options that do not match with options in the destination system, Synatic will look up a table and transform the options making the data compatible with the drop-down options in the destination system.
An application that can provide all these services is Synatic’s Data Integration Hub (DIH). It can integrate data from multiple systems and has a range of tools that businesses can use to consolidate and clean data. Sophisticated error management capabilities allow insurance agencies to catch duplicates and errors in the data as it is being moved between systems to ensure data quality.
Data that is extracted from operational systems can be stored in Synatic’s built-in data warehouse where data transformations can be performed, and the results stored for either reporting or onward integration. Because Synatic’s data warehouse is built into the platform, collating disparate data sources into one single central store that is cleansed of all errors is significantly simpler.
Using Synatic’s data automation functionality, organizations can lower costs, dramatically improve time to value, and simplify the transformation of data.
Insurance agencies can automate the process of consolidating and cleaning their data from multiple AMS systems including Applied Epic, AMS 360 and Sagitta to name a few, with Synatic's Data Integration Hub. Furthermore, Synatic can deal with a combination of AMS’s allowing agencies to ensure better data quality and let them decide where they want the complete and cleaned data to be stored. By going through this data transformation process, agencies will have easy access to good quality data stored in a central location. This consolidated data will provide insurance agents with the information they need to service clients and renew more policies. To find out how you can increase data quality and visibility in order to improve operations, contact Synatic today.
