Charitable deeds: Experience and donations

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Cancer charity Macmillan had a hypothesis that one of the major underlying drivers of fundraising behaviour was the ‘give-get relationship’ – the notion that people who receive help and support, or see others receiving it, from the likes of Macmillan will then go on to donate, creating a self-fulfilling cycle.

This hypothesis prompted a research project with Boxclever that combined analytical modelling and qualitative research to explore the concept, and whether the charity’s work and awareness of it is a key factor in prompting donations.

In an initial ethnographic stage, researchers explored the idea of the ‘give-get relationship’ and helped to define it using in-depth sessions and interviews, initially with people living with cancer and their immediate families.

The qualitative team then sought out individuals in the social circle of those living with cancer – close friends and colleagues – to understand more about what they termed the ‘cancer network’ and investigate the experience of those who are one step removed from the individual experience of cancer.

“The qual work really helped us understand what was going on in people’s lives and how their experiences with charities were playing out, and resulting in charitable behaviours back to those charities,” explains Sam Gardner, director at Boxclever.

The company also ran a market sizing exercise, conducting an online survey to quantify how many people in the UK are living with cancer and how many are close to someone living with cancer, finding that the majority fall into a category of those who are affected by cancer, with a small proportion living with it themselves, says Gardner.

A third stage involved another survey with around 4,000 respondents, which focused only on those living with cancer or affected by it, and captured more data on how much people had donated and how frequently. Boxclever then used this to build analytics models to understand what drives donation behaviour.

The company used logistic regression to understand how much more or less likely people are to donate given a range of factors, including demographic factors or whether they’re suffering from cancer themselves.

One of the findings of the modelling was that those living with cancer, along with people in their immediate family, are less likely to donate to charities. “Initially, it felt like a counterintuitive finding, but that was where the qual came in to help us understand and explain that people who are one step removed need an outlet and some way to support, whereas those who are living with cancer or in the immediate family are just so wrapped up in the day to day,” explains Gardner.

The researchers were able to conclude that a significant majority of donation behaviour was driven by the ‘give-get relationship’ and pinpoint what types of interactions with the charity or elements of its support services drove a heightened propensity to donate.

“There was a big hypothesis that the Macmillan nurse would be a big driver, and this was, but there were other things that were equally powerful – for example, simply visiting the Macmillan website and seeing all the support materials for people living with cancer was a big driver of donation behaviour.”

Integrating qualitative research helped the analysts to understand the modelling results from a human perspective, says Gardner. “What we showed through qual was how profoundly people who are one step removed from the cancer experience are affected by seeing friends or loved ones go through cancer, and also how powerless they feel.”

Demonstrating the value of the ‘give-get relationship’ and developing a simulation model to predict the likely outcome of different campaign strategies helped the charity to understand more about the return on investment of different approaches, says Gardner.

For him, the project reinforced the value of analysts and qualitative researchers working in lockstep. “There’s a tendency for analysts to be siloed from qual researchers. Personality-wise, we’re different breeds of people and it’s a real shame because when you get the two together and take a little bit of time for the analyst to explain to the qual researchers what the modelling is doing and vice versa, you get to a more powerful outcome. The qual team can really articulate things in a way that statistical people just can’t, so the two together is so powerful. As an industry as a whole, we need to take the time to try and bring both disciplines together.”

THIS ARTICLE WAS FIRST PUBLISHED IN THE OCTOBER 2021 ISSUE OF IMPACT.