The annoyance of Direct Mail: blink and you’ll miss it
In a week where some fundraising bloggers got extremely excited and used phrases like ‘ignorance & sloth are ruinous‘ and it’s ‘time to rebel‘ (well, The Agitator does like to Agitate), there was a very small number which may have gone unnoticed.
It’s thank to Queer Ideas‘ coverage of the UK’s Fundraising Standards Board (FRSB) 2010/11 annual report, that I have the pleasure – and displeasure – of seeing this number.
The FRSB receive “1 complaint for every 2,439 [Direct Mail] packs that drop through the country’s letterboxes.”
Being a data monkey, I have had the opportunity to calculate how many donors request removal from a charity’s database; how many people ask for less mail and it just never sings from the same page as what I hear in the corridors from those who answer the phones and open the mail. Many a time a fundraiser has said, yes, I know when you answer the phones it seems like everyone is annoyed but you are only talking to a small percentage. Now, hallelujah, there’s a report that shows just how small.
I know that someone will point out firstly that this is a UK figure and secondly, not everyone who is annoyed by Direct Mail will complain. Both true. So I ask myself, what would it look like if for everyone 1 person who complained there were another 4 who were unhappy but didn’t go to that effort. Hmm… we’d have 2 in 1,000 people. Still, not so shabby, eh?
So if it’s so tiny, why am I bothering to write this post at all?
Mark Phillips of Queer Ideas writes why:
Around 60% of all DM complaints were related in some way to data issues. It could be poorly addressed communications or mailings sent to deceased individuals. It could be a failure to ensure non-responsive recipients are removed from mailing files (frequency concerns) or simply not taking enough care of the data entrusted to us. And that’s the biggest surprise for me. A robust data process is a fundraising no-brainer… Add the increased income a sensible data strategy can add to your bottom-line and you find little reason not to invest in ensuring you get your data right.
Of course this data monkey wholeheartedy agrees. So I’ve resolved to stop my venting (as per previous posts on text fairies etc) and try to formulate a posting which may assist someone… somewhere. So watch this space for a ‘monkey see monkey do’ style guide to data cleaning.