The November release of the S&P/Experian Consumer Credit Default Indices, a comprehensive measure of changes in consumer credit defaults, showed that most loan types saw an increase in default rates in October. After nine consecutive months of declining default rates, the national composite1 increased to 1.55 percent in October from the 1.46 percent September rate. The first mortgage default rate increased from 1.36 percent in September to 1.47 percent in October. Bankcard default rates posted the lowest post recession rate in October (3.68 percent), compared with 3.70 percent in September.

Research shows that investing in superior customer management easily can exceed returns of 20 percent in the first year of implementation.  A return that compounds in subsequent years as a results of customer-centric strategies that drive customer's loyalty, new customer referrals, and increased revenue opportunities.  Customer loyalty is a key driver that differentiates retail banks when trying to retain existing and attract new customers.   And cited by customers themselves as the way to win their business today.  Achieving superior customer management, however, can be expensive and operationally prohibitive; and let's not to forget to mention there are a number of different approaches that aim to meet such a standard, but fail because critical qualitative insights are not captured in back-end systems of record (SOR).  These "black-box" strategies struggle to be widely adopted across the enterprise and die a slow, internal political death – with wasted resources left on the floor.  It also leaves the customer feeling frustrated and dissatisfied, maybe even ready to flee. One such example was recently illustrated in an article in Credit Union Times.   Changing the retail bank's approach to adopt best practices in developing holistic customer-centric strategies is paramount to the improvement of the customer experiences, and the bottom line. Quantitative data alone can represent only a partial view of reality whereas holistic customer strategies exploit the full value of the enterprise by synthesizing customer knowledge from SOR with external off-your firm financial information and critical qualitative input from customer-facing staff.   Customer-facing staff are critical in the adoption of such strategies and need to be actively engaged to extract customer learnings that will lead to the modification and alignment of customer-level treatement strategy designs and predictive models with the real world.   A collaborative approach, blending art and science, ensures complete adoption across the enterprise and measurable customer experience improvements that can be monetized for shareholders through improved customer retention and new customer acquisitions.  Get access to details on the framework to design and deploy such customer-centric strategies. 

By: Maria Moynihan The public sector is not unlike the private sector when it comes to data. Both require accuracy and relevancy for optimized processes and decision-making. For government agencies, maintaining a holistic view of constituents is more important than ever. By linking data across department systems, governments improve operations, citizen profiling and overall record management.  No longer do agencies have to muddle through records of Maria Moynihan, Mari Moynihan, M Moynihan, and other variations of name or contact information when they all are truly one in the same. Unfortunately, without the right tools and know how, database maintenance, record deduplication, and account validation can be a daunting process.  Below are five critical steps to helping government agencies execute successful linkage of database records: Step 1: Engage stakeholders Data stewards are not mind readers. They work with finite data and rely on stakeholders to provide insight. Seek input from users across departments and functions. Step 2: Identify impacts and priorities Data errors and disparate data prevent stewards from amalgamating records and defining a master database. Focus on areas of strategic priority. Step 3: Create success criteria Look for and set quantifiable metrics for matching. Consider what data needs to be linked and what thresholds are acceptable given objectives. Step 4: Define new standards Create established workflows and guidelines for evaluating, merging and purging records. Step 5: Leverage matching technology Integrate robust deduplication tools to design multiple workflows and handle a variety of matching challenges. In short, without data stewards seeking input from commercial stakeholders, an understanding of the data impacts, and establishing a clear process including defined methodologies and technology for deduplication, government agencies will remain challenged in trying to figure out if Maria, Mari, and M are the same person in databases. Click here to see the full guide to Creating a Single View.