Determining Customer Profitability and Improving Cross-sell and Up-sell Opportunities

Case Overview

For a Fortune 500 financial services company, we used Data Compatibility methods to provide a 360ᴼ view of customer data. The Financial Services Company was composed of several somewhat independent business units, and customer-related information was scattered across 11 major data systems. This customer information consolidation project's primary goals were to determine overall customer profitability and to discover cross-selling opportunities and upselling opportunities. To achieve these goals, the company needed a much better understanding of its institutional customers.

Case Technology

Institutional customers can have complex relationships. One company can own another company, and each company often has multiple office locations. Generally, the larger the company, the more complex the relationships. Also, professional associations can be related to many companies. To complicate matters, some of the customers were out of business but still had retirees and future retirees with which to deal.

The company first needed to standardize customer source master data making the information compatible across data systems. The D&B (Dun and Bradstreet) DUNS data registry of business locations and their data matching software were employed to help standardize customer locations and the customer parenting hierarchy. Initially, management was unhappy with our ~80% match rate against the raw incompatible source data. Data stewards curated the unmatched and multi-matched source data to improve the data quality for re-matching. At times, the data quality improvement effort included contacting customers to correct and update their profile information. In some cases, customers did not have a DUNS number assigned, so the customer information was sent to D&B for DUNS number assignment. The match rate finally reached above 97%. Each customer of the unmatchable group was assigned a non-DUNS unique identifier to maintain their individuality within the customer master data population. DUNS golden record data was also used to enrich the matched compatible source data records. While the automated matching was done within a day, the manual curating process took about three months to complete.

Case Results

The compatible customer information provided an entirely new perspective of their customers and their profitability over time. When customer profitability was aggregated to the highest level of granularity across all the business units, the companies understanding of their top customers changed dramatically. Since the source data was not changed in our customer data compatibility process, customer profitability before curation and aggregation was also available as a basis of comparison.

While the customer information consolidation system was still in the QA environment with customer data curation proceeding, test reports were used to achieve a large cross-sell, where the profits would cover a majority of the overall project cost.

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