Lawrence Jackson and Ujwal Kayande explore some opportunities a data-rich world has brought to decisions of how to invest in fundraising.
Some fundraising methods are more effective than others. But to what extent is this effectiveness contingent on the situation and context of each organisation, including the size of charity, nature of the cause, strength of the brand, and attractiveness of the “case”?
In other words, which approaches are likely to be best suited to your organisation’s unique mission, resources, and market opportunity? And how can you assess this, to guide your fundraising spend?
Critical questions to answer
In the data-free age that has now fortunately passed, fundraising investment decisions were often made largely, if not solely, on a combination of gut instinct and prior experience rather than on analysis and benchmarking of campaign performance.
Today metrics and big data are the new buzz words. However, many charities and not-for-profits are not sufficiently adept at tracking or testing their performance at a program level. This stops them determining which methods work best for their particular organisation, brand and situation.
As more donors expect feedback on their impact – and the ACNC registers more new charities that will compete for available funding – the questions all charities will be compelled to answer will be:
- How do we go about determining which fundraising methods are more effective than others?
- What is our definition of effectiveness, short-term and long-term?
- What tools and techniques are there to help measure and determine effectiveness?
- How equipped is the sector in general, and our individual organisation in particular, to embrace this new data-rich world?
Determining fundraising effectiveness
To determine effectiveness requires data to be collected consistently, and structured and stored in a consistent manner across programs. Once this foundation of data is laid, the organisation can start examining the effectiveness of a variety of tactical options such as lotteries, direct marketing campaigns, community fundraising activities, digital fundraising, events, corporate sponsorships, and major gift and bequest programs.
A common foundation of data, with embedded consistency, allows the effectiveness of different programs to be compared using the same currency of return on fundraising effort – rather than on the ‘current flavour of the month’ type assessment.
This effectiveness analysis will need to determine clear and precise measures within each of these programs – such as donor conversion/retention rates, donor lifetime value by channel (including digital), major donor prospect conversion rates, and comparative program ROIs over the short- and long-term.
Effectiveness analysis must also consider the critical interrelationships and dependencies across programs. This requires a sound database design and a co-ordinated approach to programs. This will be far more successful than a siloed approach, and will allow tracking of all donor movements – e.g. donor conversion from base programs into higher value programs such as regular giving, recurring mass participation event fundraisers, major donor upgrading, and confirmed gifts in wills.
After all this, the Holy Grail is then to decide which level of investment in fundraising programs will maximise future income.
Case study: How scoring guided a more cost-effective direct mail campaign
A simple example illustrates how analytics can be used to sensibly guide fundraising.
A large fundraising organisation solicited donations from 300,000 donors with a direct mail campaign (see Table A), of whom 15,000 gave donations, which implies a response rate of 5%. Total funds raised were $990,000, with mailing costs of $180,000. The fundraising ratio was 18% meaning it cost 18 cents to raise each dollar.
Table A: Key results from a large fundraising organisation’s direct mail campaign
Quantity mailed: 300,000
Number of donations: 15,000
Response rate: 5%
Total gross funds raised: $900,000
Average donation: $60
Mailing cost: $180,000
Net margin: $720,000
Return on investment: 400%
Fundraising ratio: 18%
The organisation then developed a model of the drivers of a) the likelihood of donation and b) the amount of donation, of 200,000 randomly selected donors in the database. These included donation recency, frequency over the past two years, recent engagement activity, and their demographics.
By building a model of likelihood and amount of donation, for each donor the organisation could generate a score for the predicted likelihood of donation, multiplied by the predicted amount of donation.
The organisation then tested this model by applying it to the remaining 100,000 donors, to predict who would give when solicited. Because it was already known whether they had donated, the predictions could be compared to their actual donation behaviour.
In the 100,000 test sample, the model predicted that 94,020 would not donate if solicited. Of those, a remarkable 98.22% did not donate. It also predicted 5,980 donors would donate, of whom a good majority of 68.5% did actually donate.
The organisation could use this information to be more effective and efficient by not wasting resources contacting donors who were unlikely to donate.
In the next campaign, contacts were reduced to only 38,000 donors – a fraction of the full database of 300,000 individuals. The organisation did not raise dramatically more, but spent a fraction (about 5%) of the cost, to raise about the same amount. These cost savings were then freed for other types of fundraising and/or serving the cause.
The bottom line
Fundraising staff will increasingly be called upon – by internal and external stakeholders – to justify their expenditures, budgets, campaigns and results, using the new tools and abilities in analytics that are at their disposal.
And organisations owe it to the causes they represent, as well as to society in general, to be more measured, analytical and evidence-based in selecting fundraising methods. The lives and welfare of those being served are depending on it.
Lawrence Jackson and Ujwal Kayande
Lawrence Jackson ([email protected]) is a philanthropy, fundraising and cause marketing consultant at Catalyst Management. Ujwal Kayande is Professor of Marketing and Director of the Centre of Business Analytics at the Melbourne Business School.
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