The Challenge of Data Governance
Customer-centric strategies are evolving with the ever increasing amount of available information, including data that is generated outside of bank interactions with other third-parties such as communications providers or social networks. This ubiquity of data has decreased the cost of the data itself, as well as the speed to store, process, access and analyze it. With more data available and fewer regulatory limitations around storage and processing, it is much more important for financial institutions to identify the specific sources that will help them attract, grow and retain profitable households.
The key question is: “What data can – and should – an institution use to help drive valuable insights about potential and existing customers?” Access, ownership and use of the data means financial institutions should be stewards and carefully appropriate its use to drive insights. A review of the drivers behind this shift towards insights suggests that the need to address this question moving forward will only intensify. These include:
Primacy: Most financial institutions have adopted success metrics around primary households, which are households that rely on a single institution to provide for and meet its primary financial needs. Specific customer behaviors include: frequency, type and amount of transactions; presence of direct deposit; tenure; and the number of products and services utilized. In most cases, primacy metrics are tracked for existing customers, but new data is available that helps financial institutions to also understand a prospect’s propensity to become a primary household. Such information has the potential to improve targeting, messaging and return on investment for prospect campaigns.
Trajectory: Big Data storage and analytic capabilities are enabling financial institutions to look at traditional data elements in new ways. One example is trended, or time series, data. Customer behaviors and preferences can now be monitored over time, allowing institutions to better predict their financial trajectory. As a result, banks can fine tune treatment strategies to become more surgical in cross-sell and up-sell activities.
In Motion: It is more difficult than ever to attract new households. Customers continue to consolidate, traditional marketing channels are becoming obsolete; and competition for new marketing channels makes it hard to stand out from the crowd. Now, more than ever, timing of marketing efforts, particularly with prospects, is critical. “In motion” customers are those who are experiencing a significant life event that impacts their financial profile, and identifying this group helps give banks the best chance of success. Traditional examples of “in motion” measures include changes of address, marital status or the number of dependents a consumer has. On the other hand, newer communication channels, such as social media, can capture timely events such as a promotion or a negative client experience at another financial institution.
With an understanding of the aggregation process and the type of data that is kept on consumers, financial institutions can better connect the data points and help maximize acquisition and account management strategies. These insights help identify consumers’ long-term trajectory and enables an institution to make the right offers at the appropriate time. However, the complexities of a consumer-centric approach to data governance and usage are constantly increasing.
The general assumption is that data governance is a practice that focuses solely on the proper use of data, but this is only partially accurate. In addition to ensuring proper use, proper data governance also focuses on achieving quality and accuracy of information. Due to the significant implications for consumers’ financial health, the quality and accuracy of data are paramount to all aspects of the customer-lifecycle and the consumer experience.
Working with regulators and internally monitoring data programs on existing regulations and best practices is critical to helping establish a solid foundation for data quality. A good data quality program starts with identifying customer data and understanding what key elements drive the biggest impact on your internal marketing and risk processes, as well as the overall consumer experience with your company. Some areas of consideration include how recent and up-to-date your data cycle is, the accuracy of delinquency information, quality and completeness of contact information or filling in the gaps on your customer’s demographics and product purchase behavior.
Once the baseline quality of the data is established, the focus should then be on measuring the accuracy of the insights that are collected and extracted. These deep studies of your data will guide you on the journey to developing improvement plans to correct errors and provide more complete coverage of your most important data assets. These internal systems must be managed as if they are enterprise programs, while also receiving appropriate visibility from senior management, to help improve the quality and accuracy of your internal and acquired data.
In addition, the issue of determining what data is or is not permissible and appropriate for use in your customer applications must be considered. Sometimes new ways of applying data may promise to reveal significant insights, but companies must be cognizant of their bandwidth and capabilities when evaluating new data-driven applications. For instance, some data is inaccessible in its raw form and only available through scores; other data may only be available for use in marketing applications or have contractual restrictions. In any scenario, a financial institution that wants to build its own model should understand these considerations and utilize appropriate data for each new project and use case.