Five Donors, One Household
Spouses give separately, children inherit accounts, and families move house. Without knowing who lives together, you mail the same home five times, misjudge what a household is worth and miss the relationships that drive loyalty and giving.
Five pieces of mail to one home, and five separate records that hide one valuable family.
Households Hide In Plain Sight
A household is not a field in any source system. It has to be inferred: two people at the same address, a joint gift, a shared surname, a spouse listed on a form. Each clue is weak on its own and often written differently in each system.
Without that inference, every person is analysed alone. A family that gives generously looks like several small donors. Mail and catalogs go to the same address many times. Targeting misses the relationships that explain behaviour, such as one partner buying for both.
Householding also has to be careful in the other direction. Two people at the same apartment building are not a family, and grouping them wrongly is worse than not grouping them at all.
Invisible Relationships
Spouses, parents and children are separate records with nothing linking them.
Undervalued Households
Giving and spend are split across people, so the family’s real value is never seen.
Duplicate Mail
The same home receives several copies of every mailing and appeal.
Resolve People, Then Group Them
Resolve individuals first, so each person is one entity across every system. Then resolve households on top: people who share an address, a last name or a joint record, matched with the same care as people themselves.
Keep both levels. Analyse and contact the household when that is what matters, and the individual when it is not, without losing either.
Each person keeps their own ZINGG_ID; the household links them.
Person First
Every individual is resolved to one entity before households are built.
Household Second
People are grouped using addresses, names and joint records.
Both Levels Kept
Reports and campaigns can work per person or per household.
The Identity Layer Under Customer Analytics
Zingg runs as a workload inside your data platform and gives every entity one persistent ZINGG_ID. Everything above it reads the same resolved entities.
The same layer powersFinancial Crime & RiskPrivacy & ComplianceSupply Chain & ProcurementProduct 360Master Data ManagementAI & Data Foundations
Households On Resolved People
Train On Your Data
Label pairs of people and households so Zingg learns what a household means for you. How Zingg matches
IDs For People And Households
Persistent ZINGG_IDs for individuals, and a household entity that links them. ZINGG_ID
See Why People Were Grouped
Every link has evidence, so you can check a household before acting on it. Explainability
In Your Data Platform
Matching runs inside Databricks, Snowflake, Microsoft Fabric, Google Cloud or AWS.
We moved from using Zingg Open Source to the Enterprise version on Snowflake, making it the engine powering our golden records. Zingg’s powerful features and clean results have really set us apart.
Householding Questions
What is householding?
Grouping the people who live or give together into one household, so you can analyse and contact them as a unit.
Why resolve individuals first?
If one person exists three times, they can end up in three households. Resolving people first means each household is built from real individuals.
Can I still market to individuals?
Yes. People keep their own IDs. The household is an extra level, not a replacement.
Who uses householding?
Retailers, banks, insurers and nonprofits. The Orthodox Union uses Zingg on Snowflake to understand households and donors.
See The Whole Household
Resolve people and households inside your own data platform.