Demographics are a key source of targeting data for marketers planning mass campaigns. They are also useful as evidence in decision making- we use them to enhance lead data.
Demographics are a key source of targeting data for marketers planning mass campaigns. They are also useful as evidence in decision making - we use them to enhance lead data.
But demographics on their own aren’t always that accurate.
Here’s my profile, based on my home postcode, from upmystreet.com:
|**Interest in current affairs**||Low|
|**Housing with mortgage**||Medium|
|**Educated to degree level**||Low|
|**Couples with children**||Medium|
|**Have satellite TV**||Medium|
And here is the disclaimer posted below it:
“This is a description of the type of neighbourhood to which this postcode has been matched, it is not a description of the postcode. The overview describes characteristics frequently found in these neighbourhoods. Since most postcodes include a mix of people we don’t expect everyone there will fit the description perfectly.
You should not base important decision-making on the ACORN classification alone. ACORN © CACI Limited 2010 All rights reserved; no right to publish is granted.”
(I hope they will forgive me for publishing this small excerpt of data).
So, the providers of this data acknowledge its limitations. I know the plural of ‘anecdote’ is not ‘data’, but certainly the experience from my own street bears this out. Average (modal) family income is pretty low. But three houses out of the 10 closest have significantly higher than average income. Out of the 20 or so adults in that group of houses, there are at least six degrees and soon to be two PHDs (sadly not in our house). Eight out of the 10 households have children.
So demographic data is a rough targeting tool at best. Fine when your campaign can take account of the balance of probabilities - e.g. outdoor advertising. But what if you are targeting people one to one? Getting the targeting wrong in a potentially high proportion of the target audience will reduce the response rate, annoy and potentially even offend prospective customers.
The promise of the web - and of CANDDi specifically - is the ability to understand individuals much more intelligently and treat them as such, tailoring your proposition and approach to their needs. Since every interaction with a customer online is trackable, why not track them and begin to understand prospects and customers as an individual rather than relying on broad brush demographics?