Household Householding

When doing B2C (business-to-consumer) activities often you really want to do B2H (business-to-household). But sometimes you also actually want B2C, having a dialogue with the individual customer. So yet again we have a Party Master Data hierarchy, here households each consisting of one or several consumers (typically a nuclear family). In Data Model language there is a parent-child relationship between households and consumers.

The classic reason for wanting to identify households is that it’s a waste of money sending several printed catalogues and other offline mailings to the same household. But a lot of other good reasons based on a shared household budget exist too.

Data captured about consumers could look like this (name, address, city):

  • Margaret Smith, 1 Main Street, Anytown
  • Margaret & John Smith, 1 Main Str, Anytown
  • John Smith, 1 Main Street, Anytown
  • Peggy Smith, 1 Main Street, Anytown
  • Mr. J. Smith, 1 Main Street, Anytown

Here it seems fair to assume that we have:

  • A HOUSEHOLD being the Smith family consisting of
  • A CONSUMER being Margaret nicknamed Peggy
  • And a CONSUMER being John

(About party master data entity types please have a look here.)

But this is an easy example compared to what you see when working with names and addresses. Among complications I have seen are:

  • Households consisting of individuals with separate family names
  • Multi adult generation households and other kinds of households
  • Not having unique addresses may cause forming not existing households
  • Some addresses are not for traditional households, but are nursing homes, campus residence halls and the like
  • The time dimension: un-synchronous relocation capture, marriage (couples), divorce (split)

Families_USIn other words: The real world is not that simple and the picture of how households are forming does change.

Available composable methods for maintaining household information are:

  • Ask your customers. An obvious choice but not easy to keep on going – your ROI may not be positive.
  • Fuzzy Data Matching. The higher percent of all citizens in a given region you have in your database the better your matching may be aligned with the real world.
  • Exploiting external reference data. Having knowledge about public address data helps a lot. Such data may tell you about uniqueness of addresses and the attributes of the buildings there. Availability differs around the world, but the trend in open government data may help.

This is the second post in a series around hierarchies in Party Master Data and how this must be handled in data matching. Previous post was about B2B (E2E) data. Next post planned is about SOHO’s.

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11 thoughts on “Household Householding

  1. vishagashe 14th August 2009 / 22:23

    Nice article as usual Henrik.

    I want to ask a question / raise a point about how intent impacts householding initiative?… would love to get your thoguths (may be here as response or next blog :))

    Even if you are dealing with B2C business model, depending upon how and what you sell (For example, selling credit cards Vs Life Insurance policy), will householding data structures/contents of what you capture and de-dup on change? If it will, in what ways? If not, why not?

    Vish Agashe

  2. Henrik Liliendahl Sørensen 15th August 2009 / 07:20

    Thanks Vish.

    From my experience there is no doubt about that how and what you sell governs in what degree you have to make your Mater Data align with the real world – to be fit for purpose.

    Besides needs related to financial service products as you mention I remember needs with products in other lines of business. In charity fundraising for example you surely will try to contact a household only once in a session and you often prefer to have the dialogue with the female part (I’m not stating any attitudes of my own here, but only telling what the business told me to do).

    How you sell becomes more and more important. Doing multi channel B2C marketing and sales and applying business intelligence to the entire operation does bring up the requirement for tracking households when you want to follow impact, response and conversion.

    Yes, elaborating in a future blog post makes sense.

  3. glenn mead 18th August 2009 / 07:45

    Hi – just a minor comment on reasons for identifying households. Discussing householding with our direct marketing group, their strongest driver seems to be success reporting on direct marketing initiatives rather than better targeting / less wastage (though this is important also). I suspect that because they are measured closely on direct marketing success, they do tend to focus on ensuring they get credit for any sale even if the sale is made to another household member.
    We have implemented one household model based mostly on fuzzy matching (also including some joint product ownership relationships, with recorded customer to customer relationships to be added soon). This causes some difficulties as households do tend to change over time (couples split, family members leave home) and the algorithm reacts to these changes inadequately in some cases (e.g. when a whole household move to a new location but the address changes are revieved in a piecemeal manner over time).

  4. Henrik Liliendahl Sørensen 18th August 2009 / 15:34

    Thanks for commenting Glenn. Your point on doing householding for reporting is actually close to me as several of my clients is doing the householding for exactly that reason.

  5. John Owens 18th August 2009 / 23:39

    In order to decide what data structures are best to support the business it is essential to define who the essential revenue generating relationship is with. Is it with the household (regardless of who lives there) or with an individual that resides at that address?

    What is the nature or the revenue generating relationship and what are the drivers? Is it based on income, age, gender, something else?

    There can also be two different relationships with a household. The first in a marketing capacity where you are trying to establish a relationship. The second is where a relationship has been established and you are trying to grow that.

    Where a relationship has been established is essential to know what is the unique identifier of the “Customer” is. This will NOT be is the Customer Number!! Many business persist in making this mistake and can either spoil the existing relationship or end up doing business with people whom they previously rejected.

  6. Henrik Liliendahl Sørensen 19th August 2009 / 06:25

    Thanks a lot John. What I have seen is that the more purposes you want to fulfil the more your model will look like the real world. And that is a complicated ERD.

  7. John Owens 19th August 2009 / 11:20

    ERDs need to be able to reflect the real world – but only that part of the world with which the enterprise is doing business. Trying to map “life, the universe and everything” may be in interesting academic exercise but is of little benefit to the business. This is why it is essential for analysts to know what the world view of the enterprise is and where it starts and ends.

    Generic data structures in an ERD can make them very flexible and very powerful but they need to be very well documented or they become impossible to decipher.

    In the Integrated Modelling Method power is always derived through simplicity and elegance – not through complexity.

  8. Henrik Liliendahl Sørensen 19th August 2009 / 11:42

    Thanks again John. I am certainly not advocating for doing complex models in legacy solutions around. What I rather have in mind is that I guess we will see solutions in the cloud having strong models with rich data that you may link to in the future.

  9. Jane Sanders 21st August 2009 / 12:54

    Henrik, have you ever operated with ‘joint family’ and ‘extended family’?
    If Yes; in which relation?

  10. Henrik Liliendahl Sørensen 21st August 2009 / 17:49

    Thanks for joining Jane.

    Most reasons for doing householding I have met is related to the nuclear family and the smaller units being couples w/o children and singles. Larger households may have other characteristics that don’t fit the purpose and may be ignored upon conditions as:
    • Number of consumers on a given address
    • External reference data telling size and use registered for that address

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