Householding means grouping customer data into groups of family units. These units make financial and budgetary decisions together. From a marketers’ viewpoint, householding helps to understand the relationships between individuals and execute the optimal communication strategy for the unit. With an understanding of the household, marketers can build a combined offer package that is valuable at the individual and household level. Opportunities for upselling and cross selling can also be discovered. For management, householding provides a deep view into customer lifetime value, risk, compliance and reporting metrics. For operations, householding reduces the mailing costs for disclosure and other mailers. For example, the SEC allows single mailers for a household while mailing prospectuses, annual and semi-annual reports.
Householding, though highly desirable, is not easy to implement. Most business data is not segmented properly into first name, last name, suffixes, prefixes. Addresses are not standardised and remain unformatted. Missing components, abbreviations like St. for street, Av. for avenue, wrong zip codes or differing formats add to the complexities of householding implementation.
Typical rule based systems address these through parsing by lookups. Auxiliary tables with values, formats and patterns are provided as part of the software to parse the components. For example long dictionaries of first names are supplied which help lookup first name from the name fields. Similarly addresses and other fields are parsed and standardized. This exercise is pretty intensive and time consuming but as the downstream name and address matching can not work without this, it is a mandatory exercise in the traditional system. Needless to say, a vast majority of financial institutions and retailers understand the need for householding, but it remains an item on the wishlist, pushed from quarter to quarter.
With Zingg fuzzy matching, tolerance towards typos, field concatenation, unformatted records, abbreviations, prefixes and suffixes is pretty high. Learning from data means the system can generalize and find matches at high accuracy even with the raw data. Zingg mostly sidesteps the normalizing and parsing phase. Hence its much easier and faster to discover households.