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Why charities should embrace predictive analytics
31 Jan 2022by Christine Chiu
Empower your organisation with big data facts. Predictive analytics is here to stay
Gut feelings and impulses are the way of the past when it comes to charity decision-making. Digital management systems are building up a valuable cache of data. While most charity staff understand how to extract reports, there are gaps in knowing what to do with the outputs.
Making useful conclusions is the next step in the charity data journey. Predictive analytics is the statistical term used to describe how organisations can harness their own data. Knowledge of these strategies gives charities an edge in the decision-making process.
What are predictive analytics?
Predictive analytics is a broad term capturing statistical, data mining, and machine learning techniques. The term is closely associated with big data and data science. Organisations use predictive analytics to gauge decisions.
IBM puts forward a formal definition. They say: “Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modelling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.”
What’s powerful about predictive analytics is its ability to provide bespoke insight. Put simply, charities can leverage this technique by using their own data to understand underlying trends.
Charity software for data analytics
Software empowers charities to use their own data to look for solutions. The technique can determine how successful different strategies are.
Industry software like IBM’s SPSS Modeller and SPSS Statistics helps charities draw meaningful conclusions. SPSS’s speciality is in analysing the likelihood of success given certain parameters. The benefit here is the sensitivity. If you have the data to hand, the software determines whether a strategy, campaign or response is likely to happen.
For charities looking at less detailed platforms, Tableau’s data analysis suite includes data diving and reporting tools. Using the platform is easy. Charity staff drag-and-drop data sets into columns and rows. The visual tools then create a relationship-based graphic of two or more variables.
Tableau’s search bar works for charity staff who aren’t as familiar with the drag-and-drop features. Staff input a question and the platform generates results. For visual thinkers, the platform highlights what the underlying trends are.
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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.”
Christine Chiu
Christine Chiu
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