What I learned from attempting a dozen data challenges

Back at the start of last month I embarked on an ambitious project: to attempt every single Workout Wednesday in chronological order. What is Workout Wednesday? Put simply, it’s a set of weekly challenges designed to improve one’s skills using Tableau, led in 2017 by Andy Kriebel and Emma Whyte from The Information Lab. They post a challenge, a few pointers to get you started, and their solution for those who get stuck (as you’ll read below, I got stuck a lot).

I’ve completed twelve of these now, and I wanted to describe here what I’ve taken away from this, both in terms of what I’ve learned about Tableau as well as what I’ve learned about myself.

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Charting my progress

Lee's Tableau Public

For about two years now, and possibly for longer, I’ve been suffering from depression. It’s been a struggle admitting this to myself and making positive changes in my life, and although I still have some way to go I do feel hopeful about the future.

I’ve recently quit my job to start freelancing, and it’s given me some time to reflect on things. One of the things I’ve been reflecting on is the progress I’ve made in my professional life, and given that my efforts in Tableau form a part of that progress I wanted to look back on how far I’ve come using the tool over the last two years (oddly enough, that’s the same length of time that I’ve been conscious of suffering from depression – though I don’t think the two are connected!). The most important reason for doing so is because depression can make us think we’re not good enough at something, but in tracing my progress over time I might be able to convince myself otherwise.

To do this, I’m going to look at each of my Tableau Public submissions since my first in early 2016. There have been ten in total, and for each one I’m going to describe how and why I did what I did, and why I think each one is an improvement on the last (you can click on the images to go to the interactive visualisations on Tableau Public). Here’s the first, from April 2016…

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Making it back into Makeover Monday



One of my resolutions for 2018 is to improve my Tableau skills, which stalled somewhat in 2017. That isn’t to say I learned nothing, but most of that learning came about because of work I had to do as part of my day job – and not because I was pushing myself outside of that. With this in mind, I downloaded the first Makeover Monday dataset of the new year and got cracking.

Now, I’m not entirely new to Makeover Monday – I participated on three previous occasions (here, here, and here), but if there’s one thing I’ve come to realise with Tableau it’s that learning in my case has to be a continuous process for it to stick, and an odd Makeover Monday submission here and there doesn’t give me the sense that I’ve achieved much. It’s like any skill in that regard – if you only practice it sporadically, then your improvement isn’t going to be anything other than incremental.

Another difficulty I’ve experienced is the sense that my own makeovers pale in comparison to the works of art that other Tableau enthusiasts seem to turn out on a weekly basis, and that does make it hard to motivate myself to even get started on these things. Makeover Monday is so public, and even though we’re reminded it’s not a competition that doesn’t stop my own inner critic from treating it like one, and of course comparing what little I can achieve against the masterpieces I see all the time on Twitter is also going to feel like defeat.

So, it’s clear: I need to get involved with this initiative regularly, and I need to keep reminding myself that I’m doing it entirely for myself. It’s perfectly fine to admit that, even after using Tableau for over two years, I still know very little – but it’s through engaging with (and documenting) this process that I’m going to do better. Here we go then…

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What’s a Direct Debit donor worth?

Box plots for blog

Donors who give on a regular basis via Direct Debit are an extremely valuable group for a charitable organisation, but when trying to evaluate their financial worth things become slightly complicated. In this blog post I’ll describe how to create the above visualisation in Tableau (using dummy data) as a way of demonstrating the value of Direct Debit donors – and, conversely, the cost of Direct Debit donor attrition.

Now I’ve written before about how you really need to take the long-term view when assessing the value of a given acquisition channel, but I didn’t refer very much to Direct Debit donors in that post. The main reason for this is because we’re not talking about a single payment in this case, and because we can’t know for sure how much in total these donors will contribute we sometimes project how much we can expect from them over a given period of time – in UK higher education we take 5 years as the default, in accordance with the Ross-CASE benchmarking guidelines. But is 5 years a realistic length of time over which to expect a donor to give on a regular basis? And if it isn’t, then for how long on average does a Direct Debit donor give before cancelling their recurring payment?

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How to measure the success of your fundraising appeals over the long term

Numbers And Finance

Image courtesy of Flickr user teegardin, CC-licensed

In my previous post I quoted liberally from Ken Burnett in my attempt to define “relationship fundraising”. I’m going to quote him once more here as a means of introducing the current topic:

[F]undraising is unquestionably a long-term activity and relationship fundraising demands that at intervals throughout that long relationship the fundraising organisation will be required to invest in the relationship with no immediate prospect of financial return. In the future fundraisers will require the vision to see the long term and the courage to resist the clamouring demands for short-term signs of gain. Perhaps in the future the people who surround fundraisers will also come to see that the organisation’s interests are best served by those prepared to wait.

(Burnett, 311)

After two posts that included very little on data, I’m going to focus more on metrics this time. The aim is to answer the following question: was my fundraising appeal successful or not? To answer this, it’s important to first appreciate that there are two very different approaches that may be taken here, but the most obvious approach is in fact the one that provides the least valuable information.

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What is relationship fundraising?


The scope of my blog seems to change every time I write a new post and the truth is the following has little to say about data, but it does concern fundraising and it was actually a series of data-led discoveries that motivated me to read up on the following subject. It’s a tenuous link admittedly, but then the only one holding me to account is myself and I’m prepared to give myself a pass on this one.

In 1992 Ken Burnett first published his now-seminal book Relationship Fundraising: A Donor-based Approach to the Business of Raising Money. It’s actually the 25th anniversary of the book’s publication this month, and Ken has written a piece here reflecting on the ways in which the ideas he set out in the book have influenced the industry – as well as the ways in which they haven’t. It’s a book that has profoundly changed my perspective in a number of ways that I won’t delve into right now, though what I will say is that the idea of relationship fundraising has occupied my thoughts a great deal in recent months and I wanted to explore those thoughts a little further here.

A lot of my current thinking on the matter has focused on the meaning of the term “relationship fundraising”, partly because I’m in the midst of creating the first Wikipedia entry on relationship fundraising. Ken himself has stated a preference for his book’s subtitle, but there’s no doubt that what precedes that subtitle has stuck in the minds of many fundraisers. Ken defined it succinctly in his book:

Relationship fundraising is an approach to the marketing of a cause which centres not around raising money but on developing to its full potential the unique and special relationship that exists between a charity and its supporter. Whatever strategies and techniques are employed to boost funds, the overriding consideration in relationship fundraising is to care for and develop that special bond and not to do anything that might damage or jeopardise it. In relationship fundraising every activity of the organisation is therefore geared towards making donors feel important, valued and considered. In this way relationship fundraising will ensure more funds per donor in the long term.

In spite of Ken’s clear explanation, in their recent academic study of relationship fundraising, Adrian Sargeant et al. nevertheless posit that there are “different approaches to relationship fundraising on either side of the Atlantic”, and that in fact no real consensus exists on its meaning. With that in mind, I’ve tried here to arrive at my own understanding of the term based on reading Ken’s book and others (you can check out my sources at the end of this piece).

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Re-imagining Aquatic Analogies in Fundraising


Anyone who works in fundraising will have heard of the leaky bucket analogy before, or some variation of it: donors pour in, donors leak out. It’s an effective way of describing donor attrition, or the rate at which donors do not choose to renew their giving from one year to the next. Roger Craver’s excellent little book Retention Fundraising: the new art and science of keeping your donors for life uses this very image on its cover to get the point across, as you’ll see above.

And yet the leaking bucket analogy has never seemed entirely satisfactory for me. It seems a bit of an oversimplification of donor behaviour from the fundraiser’s perspective, and so I wanted to an offer alternative. It’s called the leaky barrel.

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An Unequal SET

So before I begin, it’s worth noting that this post has nothing to do with fundraising, although it does concern UK universities. As such, I’ve tweaked the subheading of this blog to reflect the fact that I’m writing as much about data analysis outside my own job as I am within it: “Data discoveries in UK university fundraising & beyond”.

And with that out of the way, I wanted to talk a bit about the process behind the latest visualisation I’ve created. Here it is (click the image to open an interactive version):


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Getting Started with Audience Segmentation in Your Direct Mail Appeals


Image courtesy of Flickr user smackfu, CC-licensed

Imagine, if you will, a typical audience for a university’s flagship direct mail appeal. It might consists of tens of thousands of recipients, possibly living in dozens of countries, and spanning an age range that extends from the most recent graduate to the nostalgic retiree. Most of these people won’t respond to the appeal at all, but in order to understand the appeal’s success (or otherwise) we need to group together those who did in a way that makes sense.


Think of this post as an introduction to audience segmentation. I’m going to describe a basic form of splitting your appeal’s audience that will enable you to put its performance into context, but I’d appreciate any comments around how you might go about this in your office. I’ll endeavour in a future post to explain how you might go about setting this up in Raiser’s Edge, how you might extract the results in Alteryx, and how you might visualise those results in Tableau.

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Letting it all hang out there: #MakeoverMonday


The Rise and Fall of the Soviet Union: my #MakeoverMonday submission (click the image to go to the interactive visualisation)

Now that I’ve introduced Tableau from the perspective of my working life, I wanted to go off-topic a bit by looking at it from the perspective of a learner. That means for this post I won’t be talking about the main theme of this blog, namely data analysis in the context of UK higher education fundraising – so I’d advise you to bail now if that’s what you’re here for.

In this post I’m going to talk about my experience of submitting an entry to #MakeoverMonday. This is a weekly initiative led by Andy Kriebel (Head Coach at The Information Lab) and Andy Cotgrave (Technical Evangelist at Tableau), the latter of whom says the aim of it is to improve people’s data literacy (he elaborates on that here). It’s been running for several months now but I only got involved for the first time a couple of weeks ago, and the reasons for this have been captured well by both Chris Love and Adam Medcalf. I won’t repeat what they’ve said, but I’m pleased that these two data visualisation experts have said it publicly, and whereas before I felt like my efforts weren’t up to scratch I now believe that it doesn’t matter as long as I’m learning as I go along.

But I digress. Let’s get back to the evening of 11 August, in which I set myself a 1-hour time limit to create a visualisation in response to that week’s #MakeoverMonday data on Olympic gold medals. You can see my final submission above, and the original visualisation is here.

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