Improving Business Analytics Through A Single Customer View

Retail banks rely on business intelligence and analytics to inform decision making and comply with federal regulations. These decisions range from where to establish new branch locations to how to prioritize marketing offers and what to consumer information to report on.

Terms like business intelligence or big data are being used across more departments than ever and are no longer viewed as solely IT responsibilities. Harnessing intelligence improves staff efficiency and consumer interaction, allowing banks to not only have accurate information for internal uses, but also provide a better experience to the consumer.

But creating valuable, consistent analytics is not without its challenges. In fact, a lack of a single customer view is hurting these analytics efforts. A single customer view is an aggregated and holistic representation of the data known by an organization about its consumers. Essentially, each customer has a single record containing all of the information that the organization knows about that consumer, be it account history and holdings to demographic details or even preferences.

According to a recent Experian QAS study, 87 percent of financial institutions will eventually attempt a single customer view. However, only 16 percent of those surveyed believe they have an effective strategy in place. That means 84 percent of institutions currently lack visibility into their customer base.

There are several barriers to ensuring a consolidated database, many of which deal with data quality. Banks are also plagued by inaccurate data. 91 percent of financial institutions suspect some of their customer and prospect data might be inaccurate. On average, respondents in the survey thought as much as 18 percent of the data might be wrong.

These data quality challenges link directly back to the lack of customer visibility. If information is collected inaccurately, it can result in poor information and duplicate records. That could be why 37 percent of organizations have a data quality strategy in place to support a single customer view. The reason is simple: the more accurate the information, the easier it is to add information to existing accounts and consolidate duplicate records.

There are several factors that contribute to accuracy. The addition of so many channels, like digital and mobile, lends itself to inconsistent data entry. Information is entered by trained and untrained users with various abbreviations, formats and spellings. In fact, human error is the main barrier to maintaining accurate contact data and alleviating duplicate records.

In addition, the different skill sets required to operate each channel results in different departments and frequently different databases. This spreads information out into siloed channels.

Financial institutions as a whole need to improve the accuracy of information collected. IT departments can implement data verification techniques to prevent human error and enhance the searching capabilities within CRM systems. In addition, departments that collect data can improve training. Institutions should also maintain one central database c an contains accurate information and can feed analytical tools.

While many financial institutions are relying more on business analytics and intelligence for decision making, they need to first ensure the accuracy of the information feeding these statistics and ensure that each customer has a single record within a central database.

Thomas Schutz is SVP General Manager at Experian QAS