As new competitors continue to emerge and buyers are given more opportunities to compare and switch services, financial institutions must consider how to best remain attractive to a more sophisticated set of consumers. Customers now have greater access to information and are always after the best value for their money. Rewarding and recognizing loyal customers has become increasingly important for customer retention and revenue growth.
To do so, financial institutions are increasingly relying on customer loyalty programs to retain and keep their current customers happy. Unfortunately, loyalty programs are not a one size fits all approach. Different segments of the population require different approaches. According to banking.com, “Every customer is different, and should be treated as such. A loyalty program is made up of a diverse collection of consumers, from baby boomers to digitally native millennials, all with different expectations of loyalty programs and what the accompanying rewards and promotions should be.”
Prominence should be placed on enriching the customer experience by analyzing customer data to reward and recognize customers the way they expect. Arguably, the most important building block in implementing successful loyalty programs is starting with a baseline of good quality customer data.
Data quality scores are important measures that businesses can use to determine their ability to leverage enterprise information in strategic planning, tactical decision making and day-to-day operational activities. A lack of data quality continues to pose a major problem for many financial institutions today as information environments become larger more sophisticated. Incongruent applications, databases, systems, messages, and documents make it more difficult than ever to identify and control data quality on an ongoing basis.
Make no mistake, quality data doesn’t happen by chance. Quality data comes from the data being checked numerous times, authenticating that transposed numbers, invalid account numbers and empty values aren’t disrupting your data. Automating this process allows the organization to repurpose resources that would be otherwise tied up in manual efforts to ensure data quality.
A Data Quality Solution to Aid Customer Loyalty Programs
To prevent human error from destroying data integrity, financial institutions should implement a data quality suite that can validate that information is correct upon entry from the various data feeds and sources. The data quality suite should verify, balance, reconcile and track data in the quest to eliminate errors. Examples of errors to look for automatically with a data quality solution include duplicate data files, data completeness checks, reasonability checks and conformance checks. Additionally, file monitoring to ensure that file and data processing in both batch and real time is accurate and timely. An automated solution can help ensure that delayed or latent files aren’t being processed.
Financial institutions need to stay diligent and consistent with their data quality efforts to map a path forward that leads to pro-actively identifying potentially exorbitant data errors. With a data quality suite that delivers a standardized, auditable, and automated end-to-end data quality framework, financial institutions can catch errors before they impact their bottom line – and their reputation. Data errors will happen, but what differentiates the good from the bad, are those financial institutions that institute data quality as a culture, based on principles that are implemented in technology.
To learn more about ensuring data quality for loyalty programs, download the data sheet below.
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