Using AI in fundraising – where it helps, where it doesn’t, & key challenges

Melanie May | 16 June 2021 | News

AI depicted on multiple screens. Photo: Pixabay

AI is helping fundraising in many ways, from improving targeting through machine learning and algorithms, to engaging supporters and encouraging giving through voice assistants and chatbots. But what do fundraisers need to know about the technology and its subsets? We asked the experts.

Firstly – it’s important to know what it is – and what it isn’t. The consensus is that it’s still generally nothing like it’s portrayed in film and TV.

Rhodri Davies, Head of Policy at Charities Aid Foundation, says:

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“The discussion is often framed as if we are talking about computers that have general intelligence at a level that matches (or surpasses) humans, but that is not really what AI means at all at the moment. Artificial General Intelligence (AGI) is the theoretical, sci-fi form of AI that we often think of, and most experts think we are nowhere near that (and it may be theoretically impossible). What we do have now is computer programs with domain-specific abilities that match or outstrip humans – sometimes in rigidly rule-based areas like chess or Go where computers have always been able to compete, but more recently in tasks that humans were traditionally good at but computers were bad at such as image recognition, and natural language conversation.”

AI in fundraising – how it’s helping

In practical terms for fundraisers, this means the likes of:

Matt Haworth, Co-founder and Director at tech social enterprise Reason Digital says:

“One of the more realistic and practical application of AI for many charities, is actually less about the sexy exciting kind of above the line stuff where AI is interacting directly with fundraisers or with donors, and more about the mundane things it can do for you that otherwise keep you from using your precious human time stewarding donors and developing those relationships.”

Amy Wright, Director of Client Strategy at Automated Creative, which specialises in using AI to deliver performance and insight for social media and display advertising campaigns, explains:

“AI can tackle more than numbers. Unconventional datasets can reveal hidden correlations that help improve fundraising efforts. Images, copy, sound, or marketing data can all be analysed – turning brand collateral into powerful insight for your whole organisation.”

Graham Covington, founder and CEO of Engaging Networks and sister company Accessible Intelligence, which provides machine learning services to nonprofits, says:

“The heart of machine learning is all about problem solving. In fact that’s what algorithms do – they’re set up in order to find a better a solution to a given problem. For example, trying to upgrade one time donors to monthly donors or increase retention of donors over a period of time.”

Using machine learning to get more from data and improve communications with prospects and supporters is certainly one of the main areas where AI is proving of benefit for fundraisers, and there are many applications in CRM systems and the tech they link to.

Chris Paver, COO at tech firm Dataro, which specialises in using machine learning to help nonprofits with their fundraising says:

“Machine learning is the next frontier for fundraisers when it comes to making the most of the data that exists in their CRM and making smarter fundraising decisions. This is an area where nonprofits have a real opportunity to achieve significant improvements on the fundraising outcomes they’re currently achieving and raise more money across all of their fundraising programmes.

 

“The techniques most charities currently employ, like RFM analysis, only take into account a very limited snapshot of information about the donor and their relationship with the charity. Machine learning however allows charities to take a holistic view of all the data they’ve been diligently collecting in their CRM and actually put that to work, so they can start to use all the information they have to its full potential and figure out who they should be talking to about what.”

More can be read on how AI is being used in fundraising here.

What to watch out for

But of course, as with any technologies there are some potential challenges and issues for fundraisers to be aware of. For one, AI and machine learning are only as good as the data they’re working with and machine learning algorithms require large volumes of data in order to learn patterns and behaviours, and create models based on this. Having enough data and having it in a usable, useful state could be an issue for some charities.

Steve MacLaughlin, Vice President for Product Management and Senior Advisor to the Blackbaud Institute says:

“If we go back to that definition of AI – that it’s all about learning from data – then that can present some challenges. AI is only as good as the data it learns from. Bad AI and Bad Robots in the movies likely all came from bad data along the way. This is why the quality and appropriateness of the data used is so important.”

The importance of integrating all data sources into a single supporter view for managing relationships and compliance has been much discussed over the years, but it’s just as important for the performance of machine learning applications.

Covington comments:

“As charities start racing more and more towards using machine learning applications, they’re going to start realising the importance of having integrated data around an individual, and being able to make sure it’s good, appropriate, relevant data and it’s permission based. And that’s going to be an interesting challenge for a lot of organisations, because many don’t necessarily have their data organised in a useful way.”

And, clever though AI and its subsets are, in many ways they still can’t replace human intelligence and all activity needs to be overseen, so it’s also important to keep people involved in the process.  While visual recognition for example can be used to interpret images, although it will correctly interpret what’s obvious – such as water, trees, and sky, it will be less good at understanding context and working out that people standing in water might be part of a volunteering activity, or affected by a flood, meaning applying human intelligence to this kind of task is still critical.

Human intelligence and involvement is also important when using AI and machine learning to interpret data and create models and predictions to use in campaign, one reason being because data records need to be correctly tagged for machine learning to deliver coherent outcomes when looking for patterns.

Paver says:

“A common pitfall to watch out for is the idea of a set and forget model – where you conduct a modelling exercise and land on a model that you think is going to be predictive for a particular outcome, such as giving to an appeal. You can’t just do that process once and assume that model is going to work forever. There are going to be changes in the data and new information will become available that might mean that model becomes less and less effective over time. The exact same thing applies to the predictions themselves, so it’s important that you’re constantly updating these too, because they will also go stale as new information comes to light, and that donor continues to interact with the charity.”

Wright agrees, saying:

“There are things machines are brilliant at – automating repetitive processes; speeding up manual tasks; and allowing you to work at much greater scale. But they are literal, and work best when deployed strategically. At Automated Creative we use machine learning to augment human creativity in the marketing and fundraising process – not replace us completely!”

Aside from these practical considerations there can also be moral and ethical challenges to deal with, particularly where machine learning algorithms and the risk of biases are concerned, as Nigel Magson, CEO of data and insight agency Adroit Data & Insight explains:

“One of the ways you build algorithms is to train them on data sets to recognise things – whether it’s visual recognition, or a data combination recognition  – but because they learn by being trained on big data sets, they may include any biases, such as age, gender, and race, that were in those data sets, so the ethical point here is to understand how your model might be discriminating and to ensure therefore that any use of algorithms does not result in bias against particular groups of communities.”

While this is a small look at a big topic, what’s clear is that while AI is still really in its infancy in fundraising, and there are some hurdles and limitations for fundraisers using it to overcome, its applications – and potential benefits – are practically endless. From predictive analysis and modelling through to generating (and analysing) content, enabling giving, and providing supporters with relevant information, it’s certainly got much to offer.

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