One of the databases I use each day is getting a make-over. ‘Big Bird’ – as I have affectionately named the DB in question – it would seem is due for some cosmetic surgery.
I know what you’re thinking. I’m thinking the same. With a nose that size, I’m sure that a bit of rhinoplasty isn’t going to be enough. The surgeon is really going to have to pull out a hammer and chisel to make his face more streamlined. Trinny and Susannah will be hard pressed to find clothes which suck in Big Bird’s waist and accentuate his curves. Considering the magnitude of the change required to give a giant yellow feathered gawky bird a make-over, I started to ponder whether he really needs one? What is ‘beauty’ in a database? (assuming that a man in yellow stockings with giant feet it is not). And is the pursuit of beauty an appropriate one for a not for profit database appropriate?
I stop and try to imagine what a not for profit database should be. A family station wagon with a 5 star safety rating? A Datsun 120Y? A turbocharged 4wd porsche – stylish but suitable for different conditions? I’m sure we would all agree it isn’t a Bugatti Veyron.
If you had asked me 2 years ago whether I cared about how stylish my database was, I would have said NO. I was moving from Barry Fife (the bloody useless database), to Big Bird, the simple, yellow gawky database and I was happy. But over the past 18 months or so I’ve come to realise that good looks may be needed for good functioning (and probably good grammar which I’m clearly in need of at this point in the day!). When the ‘look’ of a database starts to get in the way of people using the database, then it is time for that surgery to occur. I’m pleased that Big Bird’s masters have realised this and are working on redefining him. I’m hoping that his makeover plan involves:
- a cosmetic surgeon to soften his face
- a personal organiser and psychologist specialising in hoarding to work on getting rid of some of the clutter,
- a speech pathlogist to improve his clarity and language, and,
- a GP to look at his health problems holistically and refer for treatment where needed.
I can imagine re-defining the image of your database is a tough ask. I’ll be watching with interest to see how the medical team fare on this one. I’ve seen a preview of the care plan and so far I’d say the patient is in good hands.
After a 4 month search, I have finally acquired myself a trainee data monkey. Now comes the critical question – how to train the monkey?
I’ve started by piling a number of articles and books on his desk! Here are a few that I consider ‘foundation’ pieces. I’d be most interesting in hearing from other fundraisers as to books, articles, blogs etc that you regard as essential!
These would be my top 3 books for getting a good grounding in fundraising. What is your top 3?
The Fundraiser’s Guide to Irresistible Communications by Jeff Brooks.
Since hearing Jeff Brooks speak at the Fundraising and Philanthropy forum in Sydney two years ago, I’ve been a fan.
I love Jeff’s simplicity, his wit and most of all his dislike of brand gurus! I mentioned to a colleague that I had ordered a copy of Jeff’s new book and in response, they said to me that they expected it to be just a summary of what he writes on his Future Fundraising Now blog.
Only half way through the book, I can certainly say there are many familiar themes, yet I’m thoroughly enjoying it and I think it will be a fantastic book for my trainee data monkey.
I first read this book about 6 or 7 years ago and its impact has never left me. My experience of fundraising texts to that point had been of rather vague prose banging on about how to make donors feel important.
This book combined definitive data about why donor loyalty matters with concrete strategies for improving it.
It remains a favourite to this day.
Many people recommend the work of Ken Burnett. He talks about donors as friends. When I first read this I thought it sounded nice but it didn’t actually click with me until I became a donor myself.
I support the Cat Protection Society at Enmore ever since I adopted my two tortoise shells from them in 2009. I subscribe to their facebook page and actually look forward to their facebook status updates telling my who has been adopted. I was asked to write an article for their recent book, Feline Friends, about my girls’ fascination with walking through my paintings while they are still wet. To my delight, it didn’t end up on the editor’s cutting room floor. When I approached them for an advance copy for my grandmother’s 90th birthday, they happily obliged.
I am a donor yet I feel like a friend.
Of all the areas of fundraising, I find major gifts one of the hardest to find quality reference material. I’ve directed my new colleague to a most entertaining and informative series of blog posts by Jeff Schreifels from Passionate Giving.
If you haven’t seen 10 Reasons Why Most Major Gift Programs Suck, I strongly recommend it.
Last but not least, is an interesting 2 part blog post on what makes a great database manager by Ivan Wainewright. I have to admit to reading this post with some trepidation as I am a database manager with no formal training in the area. I like the article because personally it pointed out some strengths I’d overlooked and highlighted areas where I can improve my skills.
I’d be really interested to hear from you – what books or articles would you add to this list?
Need a fundraising database? No problem. There are plenty on the market to choose from. Ring around a few mates and you’re likely to find the same names popping up again and again. From the time you start thinking about a new fundraising database, to having it installed with all your data converted you could have given birth to a sheep or a goat. In less time that it takes for a human baby to grow you can have your fundraising database safely settled into it’s new nursey.
Yet there are two sides to most not for profit Organisations. The funds and the services. If a database for your fundraising team is as quick as having a goat, how long does it take to get one for your client services department? In my experience, 9 months can come and go and there’s still no database. That’s right. If you’re planning on birthing a client services database, then start making friends with fur seals, giraffes and elephant mothers-to-be as these will be in your mother’s group. I hope for your sake that it isn’t as long as the elephant (22 months).
Part of the problem here is that there are no usual suspects. Ring around other Not for profit Organisations and you will probably hang up empty handed.
Of course, I’ve made a huge assumption here. I’m thinking that fellow charities must be using a database to track their service provision. In your quick ring around and you may find out that the ‘database’ is the paper file. The ‘database’ is a few excel spreadsheets. Or my personal favourite, the ‘database’ is something that Jane’s husband made up in two days in Access because Jane’s husband is ‘good with computers.’ Does anyone know how to change it? Yes – Jane’s husband does. Oh great. Let’s hope Jane and her husband never get a divorce.
A year in the making
So where’s the good news? I thought I had it. I thought (stupidly) that because it took twice as long to implement a client services database that it meant it would all go twice as smoothly. Twice as long means twice as good, right? Wrong!
The issues of data quality that exist with most fundraising database transfers weren’t going to plague me. After all, we had hardly any data as it was all in those paper files.
The data quality curse
Yet it seems the data quality curse knows no limits. The same curse which causes your fundraising team to put Mr & Mrs Jones on one client record, infects the clinical department as well. I’m hoping that I have the power to stop it before it gets too bad but the signs are all there.
Take this simple example. The humble look-up list. When you open your fundraising database and pull down the ‘Title’ field, if it’s been through multiple data conversions without a data tyrant at work, you’ll have an abundance of choice. In addition to the usual Mr, Mrs, Miss and Ms, it’s likely you have Rev and Reverend, Sister and Sisters, Mr&Mrs, Mr/s, Householders and my favourite Mr 7 Mrs (where someone forgot to press Shift for the ampersand).
In the case of Title, there are standards. It’s easy for a data monkey to come up and fix them all up, however what does one do for diagnosis? Or disability?
Other. It’s the answer to everything. When you just can’t decide, go with Other. If I had money for every time someone had asked, why can’t we just have Other and then write what it is, I’d be buying a house with a pool big enough to house a fur seal.
To be fair, some of these things aren’t easy. While there is an Australian standard for language and country of birth and ethnicity and god knows what else, there is not one of recognised ‘disabilities’. Or at least not one I can find. (For anyone looking the best I can find is a list from the Department of Family and Community Services of conditions recognised as eligibility for the carer’s pension. And if anyone has found one on the Australian Bureau of Statistics, or elsewhere, please send me a link!)
Another little trick the data quality curse has up it’s sleeve, is the multi-talented data field. This is a little like a bunyip, a yowie or a yeti. It must exist as people talk about it but I’ve yet to see one! It’s that field that magically transforms itself as the user’s will. When people don’t feel like typing a date, it undergoes a metamorphosis and becomes a text field. Just the other day we were having a discussion about when we should enter a date and someone came up with an ‘exception to the rule’. As it was a date field, their usual request of Other was null and void. Instead they called in the multi-talented data field and suggested they just put an asterisk after the date. Never mind that a date field doesn’t allow such deviation… the multi-talented data field lives on and intuitively changes itself to allow such a thing. Pity it doesn’t also create a data dictionary definition which explains what the asterisk actually means.
But there is one more trick the data quality curse has up its sleeve. Worse that ‘Other’ in look-up lists and data fields which can magically transform themselves from date to text is the third weapon in the arsenal. The shoehorn.
This has to be one of the most used and most spectacular methods of creating a big data quality issue. It’s when you don’t have a home for something in your database, so you find another field you aren’t using and you shoehorn the data into it. This is common when Jane’s husband built the thing and now he has run off with the massage therapist to outer Mongolia and no one knows how to change anything. This is how you end up putting the mobile phone number in the medical record number box. Or the word deceased in the title field as he forgot that us humans are mortal. And if you’re looking for the name of the next of kin, try location. Obvious really.
Next week I’ll be attending one day of the Fundraising Institute of Australia’s conference. After some recent interactions with fellow fundraisers, I’m walking into this conference trying to prepare myself for anything. I wouldn’t be surprised if the next fundraiser I met was a nuclear physicist in a former role, a funeral parlour director or a pole dancer.
It seems John Jeffries, the CEO of CBM is right. To paraphrase what I heard him say once: no one leaves school and says ‘I want to be a fundraiser.’ I certainly didn’t! (This is, of course, presuming that as a database manager / data analyst, I am a fundraiser. But that’s a whole different debate).
Given people don’t seem to ‘decide’ to become a fundraiser, people certainly don’t go to ‘fundraising college.’ When I think back to when I first started working with fundraising data, I could have benefitted from a few ideas of where to start. In that vain, I’m offering up the 3 figures every data analyst should know.
Figure 1: How many active donors do you have?
I know, this seems so basic but you’d be surprised how many people have trouble answering this question. The trouble comes not from inadequate databases (although this can be a factor and certainly a good excuse!). It usually comes from a lack of clarity about what an active donor is. Whether you decide an active donor is anyone who has donated in the last 6 months, 12 months or 24 is not nearly as important as picking a definition and sticking to it. Once a charity does this, it can know over time whether it’s going up, down or to planet Jupiter. The latter could be interesting but I’m sure your board of directors would prefer you were going ‘up.’
Figure 2: What ‘types’ of donors do you have?
When I say donor type, I’m not referring to whether they are companies or individuals. I’m also not referring to whether your donors are the ‘glass half full’ or ‘glass half empty’ types. Quite simply: it is what methods do your donors respond to or support? (There’s probably a far better word than ‘type’ for this; however it eludes me at this time. Perhaps I should have substituted that word for any random word… like ketchup maybe?)
Typical ‘ketchups’ include: Regular Givers/Pledgers, Community Fundraising Sponsors (e.g. city to surf; host a morning tea; the office staff come dressed as an antelope day… that kind of stuff…), Eventers (dinners, lunches, balls, high tea… you know the kind), Direct Mail/Marketing responsive donors), Bequests etc.
Why do you care about ‘ketchups’?
It can make an enormous difference to your results. At the very least, I think every analyst should know how many donors they have who have DM responsive, regular givers and then, if you like, the rest. If they can tell you more than that, woohoo! Chain them to a desk and don’t let them leave – they’re a keeper. (For the record, I’ve only managed to get clear in my head DM, RG and the rest, so I’m certainly not chained to any desk… yet).
Figure 3: How ‘matured’ are your donors?
I hear the word matured and I think of a fine cheese, or a red wine. With the utmost respect, I’d like to say that donors are similar. Let’s presume that we have two charities. For the first, I’ll use my fictitious charity – the flamingo protection foundation; the second, I’ll call the Quidditch Mission.
Each has 5,000 active DM responsive donors (defined as anyone who donated in the last 12 months to a DM campaign). For the sake of argument, let’s presume each charity deploys the same fundraising strategy and activities for 12 months. Let’s even presume that the donors give exactly the same average donation over the next 12 months and I’ll make that an even $100.
At the end of the 12 months, the Flamingo Protection Foundation has raised $350,000 from the donors who were active at the start of the year and the Quidditch Mission has raised $200,000.
They started with the same number of donors, did the same thing and got the same average donation (in my hypothetical world). So why are they so different? Quite simple: the Flamingo Protection Foundation donors were more ‘matured’ than those from the Quidditch Mission. Translated into numbers, those who save Flamingoes from the evil clutches of the red queen for croquet enslavement ‘renew’ at a rate of 75%. These are donors who were not ‘new donors’ in the previous year. In fact, it’s unlikely they were even ‘new’ the year before that. These people have been around for some time just like Alice herself. The Quidditch Mission donors on the otherhand are relatively ‘fresh'; a more recent invention. These guys only renew at a rate of 40% in the first 12 months and therein lies the difference. (To be more realistic, the flamingo supporters would probably also give a higher value of donation than the broomstick soccer nuts but let’s just glance over that).
So, that’s figure 3. Go quiz your data analyst and see whether they can tell you (in raw numbers or in percentages of active donors) how ‘mature’ your donors are. If they don’t know; ask for the retention rate. That alone may give you an indication. If they can’t give you either of those numbers, then I think we’ve located someone who was a scuba diving trainer in their former career and has yet to adjust to fundraising data analytics.