So, what is householding and how would you define it? Simply put, householding is the process of identifying and combining data of members of a “household,” hence the term householding. A popular process used in customer marketing, householding helps companies understand the purchasing dynamics of their customers,  deliver personalized experiences and lower  mailing+marketing  costs.

Why Data Householding is Important for Businesses

Customers today want personalized experiences from their favorite brands. They want their brands to identify their needs and provide them with a solution before they even search for it. Take for example an insurance company. Householding will help the company identify if their customers have kids and whether those kids are of college-going age. With this information, the insurance company may offer additional education insurance or health insurance packages for the kids. Householding, therefore, benefits both the consumer and the business.

Additional benefits to householding include cutting down on ad spent and marketing costs. For example, the same insurance company would not target the same household with home insurance or health insurance if all members of the family are already availing it. This consolidated data allows for focused content marketing and ad spent, which means gaining a competitive edge over competitors, enhancing customer service and getting higher customer satisfaction ratings.

The Problems with Householding

Although householding sounds seemingly simple, it is not. The very fact that in the real world, not all couples share the same last name and not all homes are inhabited by members of a family. You could have wed or unwed couples living together under separate names. You could have roommates living together in a single address. You can also have people living in a home as tenants. These are just some of the basic issues with householding. The real issue is the quality of your data.

For householding to be successful, data has to be clean and should be able to show the interconnectivity between entities. To achieve this you must first create a single household view, aggregating different behavioral metrics on a household level. You can:

  1. Identify and eliminate duplicate records. This step is crucial to your success.
  2. Determine the level of accuracy of your records. This can be increased, thus directly related to your data quality and the sophistication of your matching rules.
  3. Determine what makes up a group or household. The quality will vary depending on the grouping rules and quality of the data.
  4. Determine grouping rules. The level of accuracy achievable when grouping common records will also vary based on the sophistication of the grouping rules and the quality of your data.
  5. Consider data confidence factors. The assumption is that every item of data used in the householding process may be invalid.

As mentioned, business rules must be set in order to guide the householding routines. In this example below of householding with a 100% business match or no match, the following two records would not be considered a match:

Bob Smith           555 Main St.       Anytown, CA     12345-1234
Bobby Smith      555 Main St.       Anytown, CA     12345-1234
 
However, a rule of last name and address must match or no match would indicate the above records are the same.
Having these rules defined and the ability in place to verify, validate and use the grouped information is the key to successful household logic. The sheer complexity of this simple example proves that data householding is not an activity that can be accomplished by a company’s IT or database management team. You will need third-party tools and experts who can further refine data and allow you to connect the dots at a deeper level beyond just addresses or last names.

In Conclusion: In an age when consumers want personalized experiences, it makes all the more sense for businesses to understand not just the behaviors of their immediate customers but also the people close to them. When you become aware of the ‘household’ factor of your entity data, you will be better positioned to give your audience better services and one that can place your business ahead of its time.

A wanderer at heart, Ehsan specializes in exploring how people interact with product-focused B2B companies across various touch-points and leveraging that insight in content marketing. In his spare time, he dabbles in data science and learning how to get the most out of data.