Maximizing Campaign ROI: Crash Course for Fundraisers

Maximising your nonprofit’s fundraising returns requires raising more while spending less. Learn these modern tips for boosting campaign ROI to get started.

Guest blog post from Tim Paris, CEO and Co-Founder of Dataro.

There’s a lot that can be learned from disappointing fundraising results with the help of hindsight and data. These invaluable lessons can be used to strengthen your nonprofit’s next appeal or crowdfunding campaign, but where do you start? How do you know what to take away from your performance?

If you’re looking to learn from past mistakes and improve future fundraising, go straight to the source by digging into the ROI of your campaigns and working from there.

Return on investment is a fundamental metric that defines your fundraising success, essentially measuring whether your strategy generated or lost revenue. The objective is to maximise donations generated by each appeal while also minimizing costs, thus raising more money for your cause.

To start securing as much support as you can from your fundraising appeals, you’ll rely on your data in order to better target your audience. This idea of “segmentation,” or splitting your audience into discrete groups based on shared characteristics and assumptions about how they’ll behave, is nothing new and is a standard part of fundraising today. But we’ve found that traditional segmentation techniques don’t go far enough to help nonprofits truly make the most of their fundraising budgets (and therefore maximize campaign ROI). Let’s walk through 3 advanced strategies we recommend to nonprofits to maximize appeal returns.

Supporter segmentation is useful as a foundational concept for fundraisers. But the trouble comes when nonprofits allow their segmentation strategies to dictate rather than guide how they plan campaigns.

For example, nonprofits often rely on a form of segmentation called RFM analysis, standing for recency, frequency, and monetary value of donations. Donors are sorted according to these metrics, which then shape the mailing lists developed for direct mail appeals. But this approach has its limits and can be inaccurate.

Think of donation recency. If your appeal is only sent to those you consider “active donors,” or those who have given within a certain timeframe, exactly how you define recency could have big implications. By neglecting those who fall outside that timeframe, you miss the chance to put your mission back on their minds and secure more donations from those who actually would be likely to give but just haven’t been contacted in a while.

Rather than allowing the RFM metrics to flatly determine who gets contacted for appeals, you should instead take a more selective approach.

By analysing your donors’ propensity to give, you can determine down to the individual level who should be contacted and when. Artificial intelligence generates propensity metrics for donors based on their entire histories of engagement with your organisation, plus all the deep patterns in your engagement with other donors over time. This approach gives you a much more nuanced and individualised understanding of when donors are and aren’t receptive to appeals.

From there, tailor your mailing lists using propensity metrics rather than vague RFM groupings. Target only those who are likely to give, save on mailing and marketing costs, and see higher returns on your investment — raising more while spending less.

Donation page design is well known to have significant impacts on fundraising performance. A long, difficult form will naturally raise less than one that’s streamlined and loads perfectly on mobile devices.

One element of donation page design that can directly impact the ROI of your campaigns is suggested donation amounts, also called ask strings. By prompting donors to give a specific amount, perhaps just above what they would have otherwise given, you can increase returns with little extra effort. For even more effective suggestions, nonprofits today use technology to dynamically adapt their ask strings based on how individual donors have given in the past.

But go further — if you’re carefully tracking the results of your appeals anyway, pay extra attention to the specific ask strategies to learn from them over time.

Better yet, directly test different ask amounts. Track the results from an A/B test of different ask strings and use your findings in future campaigns. Remember, AI can take the guesswork out of maximizing returns by identifying the donors most likely to give and by finding donors likely to make a mid-size or major gift in the future based on their giving habits.

Direct mail is still a highly effective fundraising strategy for nonprofits when used smartly, but high-quality printed materials can get expensive fast. Just as vague assumptions can be costly for other forms of advertising, you’ll need to be selective about which donors receive your best campaign brochures and postcards.

Start by developing a targeted mailing list using propensity metrics, as detailed in Strategy 1. This will look beyond surface-level RFM metrics to give you a tighter list of the donors who are actually the most likely to give right now.

Then, drill deeper to further improve your ROI. Which donors on this list are likely to give above a certain threshold that would recoup the costs of the high-quality mailers?

You can roughly accomplish this task by studying your own data. Isolate the donors on your list in your CRM or database, and then sort them by their average gift sizes. For a lower-tech option, try simply sorting them by the size of their last donation. This isn’t a terribly accurate or efficient approach, but it does still give your mailing list an added level of precision.

For the best results and less manual analysis needed from your team, however, predictive modelling through AI will again be the better choice. By studying all of your past fundraising analytics, it can completely streamline the process of developing and refining highly targeted mailing lists that are more likely to generate a strong positive ROI. For instance, it can tell you which donors are the most likely to give above $500 in the current appeal. AI technology that integrates with your CRM will bring even more efficiency to the process, making it easy to create high performing lists right in your familiar database.

Most modern fundraisers already understand that targeting their appeals to specific sets of donors is a best practice, but remember that there’s always room to improve.

These strategies, backed up with the right technology and data, can help you achieve real results. Start working to maximize your appeal returns within your existing network of donors. Once you’ve mastered these tips and built a solid foundation for continuous improvement, you’ll be able to more efficiently expand your reach and grow your mission.

Key takeaway: Improving your fundraising ROI requires securing as much support as possible while reducing costs. By targeting and optimizing your campaigns in smarter ways, you can accomplish both at once.

About Tim

Tim is the co-founder and CEO of Dataro. He holds a PhD in Cognitive Neuroscience and a Bachelor’s degree in Psychology. Following roles in academia and startups, he co-founded Dataro in 2018 alongside schoolmate David Lyndon. The company’s mission is to help charities improve fundraising using the latest machine learning and predictive modelling techniques.

Source: https://blog.pozible.com/maximizing-campaign-roi-crash-course-for-fundraisers-1275244ceb8b?source=rss—-ae0d23b22248—4