“Wait!”, I hear you cry.  “You rail against segmentations that aren’t predictive.  But transactional RFM segmentation is not a bad predictor.”

And I will stipulate that:

  • A person will tend to give the amounts that they have given in the past.
  • A current donor is more likely to give than a lapsed donor.
  • A donor who has given multiple gifts is more likely to give than someone who has given only one gift.

So, by all means, include transactional information when you set your ask string and when you decide whether to send a communication or not.

BUT… somewhere along the line, we confused transactional analysis as merely useful and elevated it to a position of being “primary”.  Sometimes, we even confuse being primary with being the only.

This is wrong.

Transactional analysis should absolutely not be the only segmentation you do.  Neither should it even be the first segmentation you do.

This type of transactional analysis focuses on the wrong question:  “what people does my organization want to receive this communication?”  Rather, the first question you should be asking about a communication is “what people will want to get this communication?” or “how can I make this communication something these people want to get?”

This order is important.  You want to have the variables that make the most difference at the beginning of your decision tree so you can create different versions or include/exclude audiences based on the most important information.

Let’s take a trivially simple example. The most important factor in determining if someone will respond to a communication is whether they are alive.  If they are, they can respond.  If they aren’t, they can’t.

If you were segmenting based on RFM + are they dead, it would make no sense to create 64 different variants of RFM, then apply the “are they dead” filter to every one of those 64 segments.  Rather, you can first remove the dearly departed, then do the rest of the segmentation.

There are a few of these gatekeeper questions (e.g., “did they opt out of this channel? No”, “are they on the seed list? Yes”) that are the most important things.  They come first.

What comes next?  What makes the most difference in both who will get a communication and what it contains?  It’s not RFM segmentation.

Yes?  You in the back of the room?  Channel?

Yes, that’s better.  If you are sending a mail piece, you are going to send it to a far greater proportion of mail donors than of online donors.  Channel is usually very predictive of response and it is a tactic-based answer to “which people will want to get this communication?”

But it doesn’t address why someone gives to an organization.  This is the central point on which all messaging pivots (and thus segmentation should pivot).  Until you segment by donor identity, you will always have more difference among people in the segmentation bucket than between buckets.

We said that channel beats RFM in predictive power; how does donor identity do versus channel?

Here’s an example from a health charity (some numbers tweaked for anonymity):

Channel Event giving Direct marketing giving Total giving Commitment score (out of 10)
Mail 10% 90% $400 7.6
Event 80% 20% $325 7.8
Digital 5% 95% $275 7.9

Pretty thin gruel here. What you might learn is that mail donors are a bit more valuable and that people tend to stay with their own channel.  But when writing the appeal, you have no idea what to say for the mail group that is different from digital.

Now let’s look at this same audience. This time let’s view it from the perspective of  whether they were a direct beneficiary of services, indirect beneficiary, or no connection:

 Identity Event giving Direct marketing giving Total giving Commitment score (out of 10)
Direct 48% 52% $500 8.6
Indirect 34% 66% $400 7.9
No connection 30% 70% $250 7.1

Now we have some insights that are actionable.  Direct beneficiaries are the most valuable and most committed by a significant margin — there’s far more differentiation here than by channel.  They are also more likely to attend your events.  Each of these donors has a mailbox, email box, and phone number and it’s just a matter of their preference by which they give.

In fact, when we dug into the details behind these groups, we found that by far the most important factor (60%) in creating the donor’s relationship with the organization was patient care services.  If they hadn’t been or loved a patient, they didn’t care about patient care services at all.

With identity segmentation you know which group is most valuable and responsive. You also know what to say to them.  Then… if you do RFM segmentation after segmentation-by-identity, you can go deeper into the file of those with a direct connection than those with no connection (with entirely different messaging).

This is segmentation at its best – a segmentation focused on the recipient.