Category Archives: Direct Mail

There’s something about Dorothy

Oh no, it’s that time again. Training of new staff. You’ve been given the brief – go explain what fundraising does. You’ve done this bazillion times before and you know how it will play out. You go in and explain what fundraising does. You mention that a key element of fundraising is Direct Mail. You’ve sussed the crowd, hunting for the 20-something year old person who is going to come out with it… And then they say it…

‘What about younger donors? What are you doing online?’

This particular day, this particular monkey, is not in a particularly good mood. I can’t help myself. So what do you mean when you say younger?

‘Oh, not super young. Maybe 35?’


As a data monkey, I answer questions with data. The problem is in most organisation’s I’ve ever worked in at best, there has been reliable age or date of birth data for about 1 in 4 donors. I answer in my usual style. ‘About 50% of donors are aged over [insert number here usually between 75 and 80]’. The 20 something looks at you unconvinced. I imagine them having a conversation tomorrow on the bus (because we all know that all irritating conversations occur on public transport)… “and this woman was like, saying this stuff like, young people aren’t the best donors, and like, she didn’t even say like every second word.” (Yes, I know this makes me look incredibly old but I assure you that I’ve just been exposed to an excess of teenagers on transport of late who have two words they use to excess. One is ‘like’. The other starts with f. I’ll let you figure out the latter).

If you are as tired as I of people looking at you as if you are a moron when you assure them that the majority of donors are over 65, then try this exercise. Go to your database and select the 20 most common names. Then go to a site such as “Behind the Name” and do a percentage based popularity search (I used United States). If the bulk of those top 20 donor names are born after 1970 or 1980, I’ll eat my data monkey hat!

So what names were popular before people started using the word like to excess? Here’s just a taste:

Ruby SlippersDorothy: If your donors are called Dorothy, then chances are they were born well and truly before 1950. Why have I chosen Dorothy as an example? It’s nothing to do with my employer’s database (although it wouldn’t surprise me if we did have more than our fair share of people called Dorothy). The answer is it was my grandmother’s name (born 1913), and also my aunt (born in the early 1940s). What Behind The Name did show me was that Dorothy actually reached it’s popularity height back in the 1920s and to my surprise doesn’t seem to have an over the rainbow ruby-slippered inspired resurgence in 1939. (The popularity it seems was reserved for the name of the star rather than the character, with Judy and Judith being very popular during the early 1940s. Similarly, the name Shirley surged in popularity in the early 1930s, I presume as a result of a chunderous little 3 year old movie star with ringlets).

The E’s: There are a bunch of E names which just scream early 20th century. Ethel ranks among the earliest and I suspect the days of Ethel being in your top 20 donor names went out a couple of decades ago. (Hmm… this has me wondering when Ethel Merman was most popular… I can see a film and theatre trend emerging here) Esther, Enid, Elsie, Edna and Ernest are all probably in your database but no longer the main names.

Beverley was popular through the 1930s and 40s, as was Donald, Joyce, June and Joan. Anyone called Helen is most likely pre-1940, as is Grace…. or they are still a minor as it appears to have made a comeback in the 21st century. Margaret was around for decades, steadily declining from about 1950 onwards. Marjorie, Marjory, Margery and any other variants thereof, were popular in the 1920s and 30s. Marilyn on the other hand, was a little later… I wonder what impact the movie star had on that one!

Of course, there will be names in your top 20 which are timeless. I expect you’ll find John,  William, Mary and Elizabeth (and all the variations thereof).

If you’re running a regular giving program, perhaps Karen, Sharon, Janet, Mark, Adam, David, Christopher, Daniel, Brett, Kirsten or Laura will be more common?

What are your top 20 donor names?

PS: For those interested you can access the most popular names of recent times at the NSW Births, Deaths and Marriages website. It seems that Ruby has made a resurgence!


Some premium attention

It seems that while I’ve been beavering away at work and neglecting my blog, there’s been a flurry of activity following Sean Triner’s response to my ‘premium cynicism’.

If I am absolutely honest with myself, I’ll admit that I expected my blog post to get some attention.

I mean, really… what were my chances of publishing a post rubbing it in that feline owners are far superior to dog owners when it comes to charitable donations and not expect some raised eyebrows??

I’m sure you can imagine my surprise when the cat reference went entirely unnoticed and it was the questioning of the similarity of renewal rates of premium to non-premium acquired donors which attracted the attention. Unbelievable. Where are all those passionate cat haters?

A premium cynic

Cynicism is a good quality for a data analyst to sport. Lately, I’m sporting it in spades.

The focus of my current cynicism is an article in the Feb/Mar edition of Fundraising and Philanthropy magazine by Sean Triner of Pareto. It claims that the difference in renewal rates between premium acquired and non-premium acquired donors is minimal.

To my mind this seemed contrary to what I would expect and what I’ve read on the subject. Take this snippet from an article on the Agitator by Tom Belford:

Now, I’m not a fan of premiums, period. Yes, I know they can lift response (not always), but they’re prone to generating low value, low loyalty tippers. So analysis of lifetime value usually indicates a rather poor return compared to non-premium responders.

The stock standard response to complaints about poor conversion appears to be to continue to incorporate premium / freemium elements in the follow-up packs.

This is certainly the view of David Hazeltine who says: ‘so what?’ Send them another premium if that’s what it requires.’ Similarly, a convio study shows some increased renewal figures when a link back to the premium acquisition pack was included. That said, the convio slidedeck also features some data which supports the view that premium acquired donors are harder to renew and retain, with a smaller window of opportunity to boot! (You can see the convio slidedeck at: A New Donor is Like a Blind Date).

My experience to date (with more than one charity) is that the difference in renewal rates is anything but minimal. However, perhaps what the Pareto article is suggesting is that when it is all said and done, the difference in renewal rates is not sufficient to dismiss the use of the premium acquisition packs. If response rates are sufficiently higher, even with a low renewal, the ROI may come out markedly better. My only question is for how long?

Lastly, I have to throw in this one. Not because it has anything to do with premium acquired donors. It’s because it proves cats rule. Or at least, cat owners rule when it comes to donations. If I ever work for an animal charity, that knowledge may come in handy. Until then, I can just smile knowing feline owners are superior.

The nuclear fundraiser: 3 figures every data analyst should know

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.

Data Mining for Fundraisers

I know, I know… it sounds like you’re in for a boring post. ‘Feeding your brand manager to the lions’… now that would be a good title for a post! But alas, ‘data mining for fundraisers’ it is. Why? It’s the name of a book I just finished reading. Well, actually it’s the short name. The full title is: Data Mining for Fund Raisers: How to Use Simple Statistics to Find Gold in Your Donor Database-Even if You Hate Statistics‘ written by Peter B. Wylie.

It is a fantastic book and here’s why.

  1. It’s short. Who wants to read a tome on data mining in the hope that it will enhance your Organisation’s fundraising. This book is under 100 pages and in my opinion, teaches far more than many books do which are four times the size.
  2. There’s not an RFM, LTV, LYBUNT in sight. Well, there may be the occasional reference to such things… but for the most part this book is about data mining. This book is about finding donors in your database who will end up with a brilliant RFM score long before they have one.
  3. It’s practical. This isn’t a text book. It’s more like a tutorial. The chapters are even called ‘step 2, step 3…’. It is a book designed to be put into practice.
  4. The author is a pragmatist. When you get to the part of ‘significance’, he tells you that you can run a chi-square test, or run the ‘wow, look at that difference style test’. I cannot tell you how wonderful it was to read a book written by someone with over 35 years of statistical experience telling me that it was ok to skip the significance testing and go with the ‘gasp, isn’t that a brilliant difference’ style approach.
  5. There’s nothing new here. This may sound like a strange ‘plus’. Let me try to explain. This book is consistent with so much of what I have read and done over the years in my roles which was very comforting. I didn’t go to ‘data monkey training school’. Sometimes I think I should have more of a background in statistics or IT to do the work that I do. Yet the idea of either bores me to death and I have clung to experiences where I’ve come across someone with those qualifications who, at the end of the day, was quite ineffective. Reading this book assured me that a qualification in stats is not essential. This showed me I’ve been on the right track, filled in a few of the missing pieces and gave me a structure for building a predictive model.

So I say to Mr Peter B Wylie – thank you! A handy little book that I will revisit time and time again.

Miss Milly sells spats to snakes

I have a colleague who could sell spats to snakes. I once had a boss who was similar. A mate said of her ‘she could say you had two months to live and somehow you’d think that was good news’. Indeed when I think of former boss and current colleague – who I’ll call Miss Milly – they both had a similar way of smiling at the very moment you wanted to scream at them. They repeatedly have that ‘surely that can’t be too hard’ expression?

It’s Miss Milly who convinced me that it was ok to implement a new fundraising database (aka big bird) AND within a few weeks kick off an acquisition campaign, a supporter survey, a few random eDMs and a step up in the community fundraising activity. I have no idea how she managed to hoodwink me into this one but as I say, Miss Milly could convince you to put a screen door on a submarine.

To be fair, Big Bird is weathering the storm quite well. He’s only 3 weeks old and we are certainly making him run. I can tell I’m in for a chaotic couple of weeks. I already am telling myself ‘when I get through these few weeks I’ll have time to sit-down and learn how to use our new database better.’ I am of course delusional because when I get to the end of these few weeks, there will be Miss Milly and her smile and two words… Christmas Campaign.

Data and creativity

Over at Beth’s Blog she’s discussing the value of quantitative and qualitative data in not for profits (translated – numbers and stories). Beth says that she likes to start with numbers and then use qualitative data to explain what those numbers actually mean. She quotes one of her twitterer followers (@orgnet) who says:

‘turning data into stories is the real trick.’

I’ve made a note of this for my resume. I have such trouble explaining to people what I actually do. I’ve had job titles like ‘research and reporting analyst’, ‘data analyst’ and ‘database manager’. Yet these seem misleading. People meet me and immediately I’m that ‘uncreative black and white numbers geek’. (I’m not disputing the ‘geek’ part, athough I prefer the term ‘quirky!)’. What I am disputing is the idea that data and creativity DON’T go together. When people find out that I am an artist in my spare time, I get some very strange looks. A data person cannot of created these.

And so it is that I’m delighted to read Beth’s post emphasising that data (quant and qual) should be used to ‘paint pictures’ in fundraising.

Perhaps all data analysts should have their job title changed to storytellers.

At this point I cannot do Beth’s post justice. The discussion of appropriate collection measures for qualitative data was a little too much for my brain on a Sunday morning. So instead I’m going to suggest the simplest of qualitative data exercises.

Open the mail.

Yes, you read right. Open the mail.

If I had my way, every member of the fundraising department would open the mail at least once a month. Here’s a few things to look for:

  • Names – are your donors called Dorothy? Chances are she’s not 35. Or are you seeing names like Kylie – she’s not 80. Age is a data field often missing. So any sense you may get from this exercise is a bonus.
  • Handwriting – an obvious application is once again hinting at donor age. However it doesn’t end there. Are supporters sending messages that are all squished? Perhaps you need to give them more room for comment.
  • Language – your Organisation may mandate the use of politically correct, or clinically appropriate terms, but what language are you donor’s using to describe your services and work? I don’t discount the need for using appropriate language but sometimes checking this against your donor’s language is a useful way of spotting a disconnect.
  • Addressing – do your supporters use your reply paid envelope or address their own and affix a stamp? Do they address it to the CEO? Do they use ‘address labels?’ (I can tell you the names of charities who’ve been communicating with our supporters simply by the stickers our supporters are putting on mail to us!)
I’ll add one word of caution. If you think you spot a trend from this exercise e.g. your supporters don’t like the premium / freemium you sent, then its time to get out your quantitative tools and check your hypothesis before making any sweeping changes toy our fundraising program.
That caveat aside, get out your letter opener and start slashing. I’d be interested to hear what you find!

The chocolate mail preference

OK, you’ve spent months getting everything right for your new database. You have asked everyone in the office once, twice and some three times over what the ‘lookup’ values should be for all the drop down boxes and tick boxes for your shiny new database. In fact, you are just plain over it. You have asked so many times that surely you have to have covered everything.

Then some resourceful 30 something trendy loft living marketer comes up with a new concept and you have to change your database to adapt. This time it’s to cope with a new alternate format. Braille, large print, standard print – all old hat. You’re going to need to add a lookup value of chocolate. Yes you heard right. Chocolate.

At this point I apologise to my readers who are blind and using a screen reader as the video below uses printed text to tell the story. I’ve done my best to ‘audio describe’ it below the embedded link. The irony of this is that it is a video talking about how to get Direct Mail to appeal to all your senses!

Audio description of video:
Text on video reads: “Brief: Convince 6,000 top marketers and ad agencies that Direct Mail can have more profitable relationships because it can engage all five senses. Sight Sound Touch Taste Smell. Solution: send them a letter made entirely of chocolate”

Video then shows factory machinery piping chocolate into a large plastic chocolate mould. It looks to be about A4 paper size. The chocolate slab is then shown with the ‘text’ of the letter embossed into the chocolate slab. A worker uses a piping bag of chocolate to hand-write the recipient’s first name.

More text on screen reads: “The letter explains the principle of engaging the senses to create more emotive connections with customers. It asks the reader to admire it, smell it, touch it, snap it and, of course, taste it. And it asks them to call Royal Mail to find out more about sensory marketing. The full results of the campaign will be measured over 12 months. But, since November 19th, 51 leads have been generated from the mailing. 1.17 million pounds has been predicated from these leads. The mailing cost 210,000 pounds.”

How to stuff up your fundraising database without even trying

There’s some things that just come in pairs. Salt and Pepper. Kermit & Piggy. Bonnie and Clyde. Van Gogh and Ears…

So it’s not surprising that when you divorce database functionality from process that something feels amiss. While most data monkeys see those two as things as inseparable as Garfield and food, functionally speaking they are often quite separate. Processes naturally come from inside your Organisation while databases – generally – are purchased externally. So it is little wonder that people can have brilliant knowledge of what their database can do yet they remain frustrated with it and aren’t able to utilize it effectively.

If you’re thinking that great words of wisdom about how to avoid this perilous pathway are coming next, you’d be wrong. My analysis of blog stats tells me that serious advice is scorned, while database murder is applauded. So instead I present my top 5 tips for stuffing up your fundraising database without even trying. Here goes.

1. Call it the ‘donor database’

Go on. I dare you.

Refer to the fundraising database in your everyday business as the ‘donor database’ each and everyday.

It won’t be long before your colleagues go feral.

Those really, really, important people who are leaders in your field of choice; who pull the strings in government departments and head up the corporate partnerships division you’ve been hankering to find… they won’t make it into your database because it’s for DONORS. Only people who have gifted funds belong in a donor database – everyone knows that!

Put a person in that database and god forbid that horrible Direct Mail person who eats toenails for breakfast and inbred slugs for morning tea will go and mail your really really important person and ask them for money. So, just don’t put them in the database and you’ll solve the problem.

Alternatively, put them in the database and follow my next tip.

2. Don’t bother defining ‘no mail’

I bet you didn’t know that there’s a famous person who has set up the data definitions in most fundraising databases. That’s right, super famous! Even your 3 year old knows who he is.

It’s Humpty Dumpty.

Want proof? No problem. See Chapter 6; Through the Looking Glass and What Alice Found There:

When I use a word’, Humpty Dumpty said, in a rather scornful tone, it means just what I choose it to mean – neither more nor less.’

And such it is with ‘no mail’. When the major gifts person uses it, Humpty meant: ‘mail them invitations and maybe a newsletter without an ask but don’t ask them to donate’. When the Direct Mail person uses it, it means ‘someone meant to reduce the person’s mail and put them on no mail instead’ and when a data monkey uses it, Humpty meant – do not mail this person. That’s it – neither more nor less.

3. Process your membership payments as donations

What harm can a few ill-defined payments do? You can always exclude them based on campaign code.

This is a sure fire way to stuff up your database. These seemingly innocent miscellaneous payments that you forced through the ‘fundraising’ database because the finance department couldn’t cope will distort your active donor count; gifts per donor AND average donation value. What a brilliant strategy – you get to stuff up 3 core measures in one foul swoop!

4. Make groups. Lots of them.

It appears people love to categorise. As chief database stuffer-upperer you need to exploit this! Next time your colleagues get a burning desire to ‘group’ their contacts, donors, prospects etc it is your duty to encourage them. After all, why have something in the database in one way, if you can have two? Instead of settling for a recency, frequency and gift value criteria for identifying people for your next bequest campaign, why not round them all up and put a nice label on it?

Maintaining a group you don’t need isn’t an albatross. I promise.

(To be fair, people often make ‘groups’, ‘lists’, ‘codes’ for things which already exist in another form because the tools to find them are lacking. I have to admit to creating a few ‘groups’ myself recently. I tell myself that I’ve only gone to the darkside as a temporary measure. When I get rid of Barry – the bloody useless database – and get a decent query tool, my database will be cleansed of such duplication. Or so I tell myself daily.)

5. Don’t monitor your data.

If you look at your data and question who put certain information in the database and why, trust me, you’ll be cast as the ‘data police’.People who actually look at the percentage of cases where information has been completed; patterns of entry by data entry person; or where those strange campaign codes came from are people who are just pernickety. They aren’t doing it out of any interest in the quality of your data; or the efficiency of your business: they do it because they love to point the finger.

So spare yourself the grief. NEVER – no, NOT EVER – monitor data entry. You will be rewarded with junk you’ve not even had the pleasure of setting eyes on. Who could resist?

7 tips for finding duplicates in your database

Ok, you’ve been somehow landed yourself the job of being the data monkey. Someone has given you a list of clients, donors, customers, three-headed atlantic goats and you’ve been asked to do something with it. It could be really simple like: how many clients do we have?

Hmm… let me rewind. That might be simple if your non-profit Organisation has invested in a good database. Or go back another step, even has just ONE database. Chances are if you are a small not for profit Organisation you probably don’t have a dedicated data monkey and your [insert one type of clinican here] have one lot of data and your [insert second type here] their own little list. (Counsellors are the usual culprits for harbouring separate lists)

For the sake of simplicity, I’m going to presume that you’ve managed to round up all the lists and throw them together into one spreadsheet.

How hard can it be to find duplicates? Just look for people with the same name and address right? (I’m presuming that given your Organisation doesn’t have a data monkey it’s unlikely to have software that will de-dupe either).

Well, it is just about looking for doubles but here’s a few tips and tricks.

  1. Know who you are dealing with: if you’re working with a list containing children you’ll probably have siblings. What’s more, with low birth weight associated with some disabilities, you’re likely to have a few more twins than a normal database (assuming you’re a disability Org). With this in mind be careful of automatically ‘deleting’ duplicates based on surname, address and date of birth.
  2. Know how the data will be used: why are you de-duping this list? Is it to count the number of clients? To mail donors for a fundraising campaign? To phone customers? Mail clients about a new service? What you’re going to do with it will help you decide how aggressive to be with your de-duping.
  3. Be greedy: sometimes it’s best to ask for more data than you think you need. Commonly if you need a mailing list, you’ll pull out Title, Name, Address and Postcode. I recommend that you also pull out phone numbers and email addresses even if you don’t plan on calling anyone. These fields can be handy for detecting duplicates.
  4. Change it up: It can be tempting to use the same criteria each time you de-dupe a list (gasp, yes, you’re likely to have to do this more than once!) The trick of using just the first character of the person’s first name so R Goulburn and Robert Goulburn so that both will show in your output is a good one. But every now and then, ditch the initial. I recommend this all the more if your clientele are older. This is because names where the nickname and full name start with different letters seem to be more prevalent in older generations (I’m sure some social scientist would be able to tell me whether this is just my perception or not). Common pairings to be on the lookout for: Edward and Ted, Alfred and Fred, Robert and Bob, William and Bill, Margaret and Peg, Elizabeth and Betty… and so on.
  5. Swap it around: Another ‘every now and then’ trick is to search for duplicates where the first and last name fields have been entered backwards. This could be where people have a last name which can also be a first name, or for cultural reasons the name appears the otherway and it has been shuffled in your database. This is when your phone numbers come in handy. Exclude name fields altogether from your match criteria and throw in phone, email or DOB with the address component. You may surprised what you find.
  6. Know when to quit: if you are doing a ‘manual’ style de-dupe (i.e. not specialised software), it can be time consuming. If my search combinations are showing up only a handful of duplicates, I know that it’s time to call it a day. If I’m still getting a fat list of dupes every time I tweak the criteria, I’ll persist a little longer.
  7. Address duplicates where they breed: I know, I know, it seems really obvious doesn’t it? However I’m sure that many people have been through a process, saved what they thought they needed to update the database and then later found they needed something else. I’ve often been sent emails alerting me to a pair of duplicates, many people kindly providing the reference numbers and I will still need to email back and ask – which one is the primary record? Which ones has the correctly spelt first name etc. So start with the end in mind and you’ll save yourself some grief.