Why charities should embrace predictive analytics

Charities can use data techniques to retain donors

Predictive modelling helps prevent donors from leaving by anticipating what turns them off. Virtuous, a US-based CRM platform, offers insight on what analytics can do. They say at a basic level, charities use SPSS to answer questions like:

• What interactions convert web visitors to donors?
• How can charities identify long term donors?
• How many ‘touch points’ does a person need to increase their donation from the previous one?

The software works by knitting together donation and online traffic data. The outputs are powerful because they let charities know the best course of action.

At Charity Digital, we’ve pointed out how predictive data analytics can point decision-makers in the right direction.

The data can enhance outreach programmes by prioritising channels to focus on. For example, data analysis may reveal that donations increase when controversial tweets are published. For donation teams, the conclusions are invaluable because they show what works, and what doesn’t.

Data analytics improve charity operations

Making operations more efficient is another reason to tuck into data analytics. Analytics helps charities identify beneficiaries, what services work best, and, how to increase efficiency.

DataKind specialises in helping other charities use data. Recently DataKind partnered with Christians Against Poverty (CAP) to get a better understanding of how to help beneficiaries struggling with debt. The team wanted to predict which debt reduction routes would work best and the level of support needed to achieve success.

The statistical regression models showed the best course of action up to 90% of the time. The analysis also revealed how little information was needed to predict meaningful results. For CAP, this means that staff no longer need to collect as much data in order to suggest the best solution.

DataKind and CAP also worked out where to focus resources. Since many debt reduction routes require sustained support from charity staff, it was important to understand where and when to expect heavy workloads.

Predictive data analytics proved useful again.

The results showed CAP that beneficiaries living in the South needed more time and support than those in the Midlands. Interestingly, beneficiaries based in Northern Ireland needed less than expected.

Martin Cowles, Senior Project Manager at CAP, summed up how important data analytics are to operations: “CAP’s got a journey ahead of us in the next few years, and we want to help more people. And if we do want to help more people, there’s absolutely no question that data can be such a big part of that.”