The Fundraising Insider I Episode 2 I How Data Makes Fundraising Easier
(Check out the previous episode of The Fundraising Insider series on YouTube.)
Daryl Chan, Head Data Strategist at SG Support, joined Seema Nair, the host of Fundraising Insider, to discuss what he means by “How Data Makes Fundraising Easier” and how charity leaders can use data to shape better donor experiences and fundraising strategies.
In this conversation, we learned about:
- Why having access to data alone is not enough
- The evolution of how charities use data to make decisions
- Datashare as a platform for charities to learn from one another
- How a charity would use learnings from data analysis to improve their retention program
A transcript of the interview is also available, as below.
SEEMA NAIR: Hi everyone, welcome to another episode of Fundraising Insider. Today, we’re speaking with Daryl Chan, head data strategist at SG Support. Daryl transforms large data sets into stories that shape actionable fundraising strategies. Thanks for speaking with us, DC.
DARYL CHAN: Pleasure to be here.
SEEMA: Let’s get right into it. You’ve got a communications background. What got you into working with data?
DARYL: So, coming from a communications background, it was a natural segue to join SG, into the communications team back then, which I was leading. And the approach back then, in at least where I came from, it was almost the antithesis to data, because I was a creative guy, and when the client service people came down with the brief, “Here’s the client insight.” And we go like, this client insight is not aligned with the creative direction. And then we threw it out the window, and kind of do what we like creatively, anyway.
Then joining SG, I then realised that there had to be some sort of a method to the way that we communicate. We can’t just communicate and do what feels right, there had to be actual evidence backing the communication that was going out to donors. Not just based on assumptions.
Just to give an example of what went on back then, there used to be this assumption that, we’ve got an attrition spike in month X, therefore we must precede that with a communication in the month before. And the questions in my mind were, have we first established that this attrition spike exists? And does it really solve anything by putting a communication in the month before? Have we established that the problem to this attrition spike is communication, if the spike did exist at all. So that kind of got me looking into the data, trying to find evidence and one thing led to another. So today here I am, in the data team called SG INSGHT.
SEEMA: That’s a good story. So, going into the aspect of data itself, we hear that data is the ‘new oil’, but having data alone is not enough, is it? We have to put it to work. So, what does data-driven decision-making mean to you?
DARYL: It pretty much means exactly what it says on the can. It is making your decisions not based on assumptions, not in a ‘finger in the wind’ kind of manner. It is looking at evidence and using that evidence to draw a conclusion, which sometimes has to be a fairly brave conclusion. Being human, we tend to sometimes want to do what feels right. It may not be what exactly it is that the data is indicating. It may take some will or some courage to actually make a certain jump.
SEEMA: A counterintuitive decision, is that what you are saying?
DARYL: Yes, sometimes it is counterintuitive. Sometimes it is just a jump that may take a lot of effort, or take us out of a comfort zone, out of the way we are used to operating. But it is what the data indicates that we should do. Therefore, this is the decision that we should make.
SEEMA: And how have you seen the evolution of the use of data to support decision-making in the charity space, typically with regular giving, which is what we deal with most of the time.
DARYL: For sure, charities are asking for more and more data, and deeper and deeper data. So, I think that’s a very positive indicator for the way that the industry is moving. In the past, I think they, our clients used to ask us for some fairly top line numbers, or sometimes, we will be asked for a number, and we will give it in the form of a report or something and a certain decision would be made, which in retrospect, when I look at it, did the client ask for the right thing? Because there are times when just getting into the early stages of transitioning into data-driven decision-making, the clients are trying to be data-driven in the way they make decisions, but perhaps they have not asked for the right data in order to make the right decision.
So, part of our process now in our team is that when someone asks us for data, we usually ask, “What’s the outcome that you want?”, “What is the decision that you’re trying to make?” Or “What problem is that you’re trying to solve?” And sometimes we realise that there’s a mismatch between what is being asked for and what that problem is. We can then try to propose a better solution or a better way, or give the right numbers that you need, which were not what was asked in the first place.
SEEMA: Yeah, that makes a lot of sense. These insights and trends and learnings that come out of your analysis. These are shared back to clients in the form of scheduled reports, Power BI dashboards, and off and on even in special events, is that correct?
DARYL: So yeah, we do reporting in various forms, from the classic static reports in Excel, to Power BI, which is the trend these days. People love Power BI, it has its place. It still has its limitations though. So, Excel is still very much a thing. There’s an important place for it in the work we do.
Let’s talk about Datashare since you’ve already alluded to it. Datashare is a product started four, maybe five years ago. It is kind of experimental. At first, we thought, what if we kind of got all our clients together from one country and tried to cross-share their data. Because typically every client’s data is siloed, the client owns their own data. And we’re not at liberty to share that information with other people. But then we realised there are valuable insights to be learned when we aggregate all the data from one country into one bigger data set. And there are things that charities can learn from each other as well. By looking at each other’s data, and see what they do well, what they’re not doing well compared to others. So, we got all our charities from one country into one room, and we aggregated the data. We presented it back to them, in a small way at first, but it’s kind of grown over the years into one of SG’s flagship products.
SEEMA: And you do that for multiple countries now? I think that first one was a small one for Malaysia, and now we do them across all markets?
DARYL: Yeah, of course, we do it for more markets now, so more countries and every country has more clients. So, the data sets that we’re working with get bigger and bigger. Not only do we do country (specific) Datashares. So, one way of looking at it is taking one country with all your clients, the other way is looking at one client, but for all their countries. So, for a multinational (organisation), we take one charity brand and do that same Datashare across different countries as well, which has a different complexity to it because every country is different. Whereas if you’re taking all your charities from one country, they have more or less the same commonality, being in one country. When you start looking at multiple countries, you then get into things like currency conversion, you’ve got to use a common currency. Metrics need to be normalised across different countries as well. So, it does bring different challenges. But it’s been, I’d say, a valuable product for SG to establish a thought leadership and market leadership position for ourselves.
SEEMA: And where do you see the difference in learnings that come out of the country-centered Datashares, versus a Datashare which is one charity, but multiple countries. So, for that one-charity multiple-countries model, what would be the kind of metrics we look at that would make sense across different countries, across different markets?
DARYL: They would look at, in every market, how much are we asking for. The average donation. For example, every market kind of has different levels of average income, and GDP (gross domestic product) and so on. So, are we over asking? Are we under asking? We typically see different donor behaviours in different markets. For example, the attrition rate is naturally different for every market. And this is driven by both the donor’s financial capacity as well as the banking infrastructure in that country. So, you can’t compare apple to apple, attrition rates for every market, but for charities, they find it valuable to benchmark between their markets. Ultimately, for a charity, I think, if you’re one charity looking at multiple markets, you’re trying to see like where’s my revenue coming from? Which is my strongest revenue stream, and how do I support that? Which is my weakest revenue stream, how do I build this up? Are there roadblocks that I can get past so that this country performs to better potential, for example.
SEEMA: So, we are really using the data to support pretty high-level strategic decision-making and strategic planning.
DARYL: So, we typically see the top brass of the charities attend these Datashare sessions as well. Whereas if you compare it to a country-level Datashare, it is a different dynamic because every client in that room is kind of for themself, and they are there trying to learn from each other, rather than, having high-level decision-making, on the other hand.
SEEMA: That’s a very interesting point. So, staying with this line of thinking on data-driven decision-making, let’s go into the practicalities a little bit. Give us an example of how a charity would use the data model or use the learnings from a data analysis to either inform or improve their donor retention programme.
DARYL: So, when it comes to donor retention, I’m going to reword this as ‘communication’. Retention is very broad. Retention is kind of everything that SG does to help to retain a donor, from processing, how we process the payment, to call centre work. Saving and recovery and all that. In that case, the data tells you, where you can find the gains. Some exercises may be high-effort but low-gain. If you look at the data and see that the revenue output coming out of this exercise is really very minimal compared to how much effort it took to implement it, and should we continue doing this, for example.
From a communication perspective, because a lot of charities refer to communication as donor retention, although it is broader than that, my usual advice is that you first want to evaluate your content. From a content perspective, is this content performing or is it not? Is it having the desired effect? The content can often be a double-edged sword. And this is completely unintentional, but it is just the nature of the message and how people react to it. If you’re a charity dealing with human rights issues, for example, people sit on two sides of the fence when it comes to human rights, it steps into the realm of the political. Some people are more conservative, some people are more liberal. Different food for thought. Therefore, your content can resonate both ways, within groups of people.
So, you first need to evaluate, is my content having the desired effect on the desired group of people? If your content is working, do more of it. Communicate as much as you can with as many people as you can, and that helps you to amplify and maximise the effect of your communication. And we hope this, therefore, leads to longer term donor retention, a more positive donor experience, and so on. If your content is not resonating as you intend, your best move is really to stop communicating until you figure out your next move. What is the right message to communicate to this group of people that will kind of push them in the direction that I want them to go, rather than the other way. And once you have established that, you can then get back into planning, I guess, logistical part of your communications plan which is, how much do I communicate, when do I communicate and how do I communicate. And really look at data to drive this. So, of the people I’m sending communication A to, how are they performing? Are they dropping off faster than the control group B who is not receiving these communications, and so on. And how do I benchmark this performance against other charities?
We do, of course, not share the data. Like I mentioned, we’re not at liberty to share the data, unless it is in the Datashare environment. But we are in a position to consult for our clients and tell them where they stand, in terms of, is it good or is it bad. And what’s the kind of the envelope that you’re looking at in those percentages.
SEEMA: And you can always share learnings, good practices that you see from one charity can always be taught to another charity without sharing the data.
DARYL: Yeah, we can certainly share best practices, or consult our client, if they bring an idea and we think that’s not the best idea, we generally do that for them.
SEEMA: This has been very insightful, DC. Thanks so much for this and hope to speak to you again soon.
DARYL: Yeah, it was a pleasure. Thank you. Bye.