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This is absolutely the type of headline we would write in order to be contrarian and provocative (not to mention, accurate).  Alas, we can’t take credit.  This is the headline from a recent article about Netflix whose headline reads, in full,

“Netflix says geography, age and gender are garbage for predicting taste.” 

Blame Netflix for rendering the vast majority of demographic data append services worse than useless.

(Note: A nod to Nick Ellinger, newest member of the DonorVoice team who alerted us to the article)

Or thank them. Or us…

What gives?  How is this possible?  You know your donors are older and that older donors stick around much longer than younger ones so how can age, among other demos be garbage?

What Netflix knows from their mountain of data (movie selection behavior mostly) is that people are in fact different but not in the ways you’d imagine.  Your donors are also different but not in any of the ways they are typically segmented – e.g. by RFM buckets or acquisition source.

Here is the critical nugget – the Netflix mountain of data, just like your mountain of data, is 99% garbage, 1% gold.

In the garbage pile are age, gender and geography.  Think about this, geography is what makes up the cluster codes (e.g. Prizm) with the rationale being that people who live near each other are similar.

That this is partly true misses the much larger reality, those similarities to our neighbors are dwarfed by the differences with our neighbors.

To recap, here is the Netflix reality and yours: differences within a general, stereotypical group (e.g. females) are greater than the differences between two general, stereotypical groups (e.g. females and males).

But what about romantic comedies, i.e. “chick-flicks”?  An entire genre tagged by gender.  It is absolutely true that if Netflix served up romantic comedies to a group of females and males who otherwise looked demographically the same (e.g. age, geography, income) they would see much greater uptake by the female group.

Done, declare victory, a brilliant A/B test proves the generalization.  Not so fast.

The fact that more females ‘converted’ on romantic comedies than males obscures the missed opportunity; there are females who don’t like ‘chick-flicks’ and some males who do.  In the first case you’ve served up irrelevant content and in the latter, missed a sales opportunity.

This same reality applies to the gender based testing done in the charity sector.  For example, the mailing label test using generalized, stereotypical assumptions about male preference – e.g. anchors and boats – and female preference –  e.g. flowers.

Seeing that females respond better than males to mailing labels with flowers is an example of the very limited, skim the surface type of mentality that keeps the charity sector on a slow decline trajectory while alternative philanthropic options grow like crazy – e.g. direct to donor models, social good stock funds, social good bonds.

Using these weak, demographic profiles and thinking you have some unique insight is akin to renting names to mail and thinking you have a unique universe of donors.  Everybody does this, it is a ‘best practice’ to nowhere.

What is the alternative?  Here is the upshot, Netflix does have customer clusters (i.e. segments).  Those clusters are based on likes (and by extension, dislikes).  In short, preferences.

Think about it, a group of donors on your file who:

  • Have disease X that you are in the business of curing.
  • Like big predator birds (not small ones)
  • Know someone who was a beneficiary of your charity
  • Have a strong religious motivation to give even though your charity is secular

If you profile these groups by their demographics you will see that they skew this way or that way (e.g. older, female, whatever…).  That is as inevitable as it is irrelevant.

Why in the world is it beyond the capacity of a charity or any business to group folks based on preferences like the examples raised?

This is a mindset shift, not one of resource or database functionality.  It is one that requires not thinking of donors as widgets on an assembly line.

Or maybe it is two assembly lines; males and females…or is it old people and not old people…

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