The Donation Maximization Blog

Intro to Predictive Analytics: Improving Nonprofit Fundraising

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Predictive Analytics for Dummies

Marketing agencies are always looking for new ways to differentiate their clients from the competition. Especially in the nonprofit sector, clients are competing for limited attention, time, and money from donors. One way to make this distinction is by implementing the use of data and employing predictive analytics.

If an agency has never dabbled in predictive analytics, even the term can seem a little overwhelming. There’s no reason to be intimidated, though, as this predictive analytics for dummies post will demonstrate.

Really, predictive analytics is pretty straightforward. It’s the concept of using existing data to predict future performance. Businesses normally study data to learn what has worked for their company in the past or just to group their clients for demographic purposes.

Predictive analytics takes this information and applies it to future scenarios or choices. This can apply to many different aspects of business. For nonprofit clients, it’s mostly going to affect fundraising analytics, like learning how donors respond to certain ask amounts or how to most effectively bring donors back in after a first donation.

Collecting Good Data

If you’re interested in getting into predictive analytics, the first thing to determine is the quality of your existing data. While predictive analytics can still be utilized with limited data, it’s far more effective with complete data.

It’s a good idea to review your nonprofit clients’ data with an eye on these things. If a client’s data is consistent, uniform, from a proper time frame as determined by the agency, and doesn’t have a lot of missing pieces, you should incorporate that data. Otherwise, you’ll want to make it a priority to start obtaining some of that missing data as possible.

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There are several ways to ensure the data your agency collects on a nonprofit’s behalf is complete and consistent. Achieve consistency by using drop-down menus rather than allowing users to type in answers. Anytime users can type in whatever they want, you’ll run into inconsistencies. Another way to ensure you’re gathering complete data is by using required fields. Any field that isn’t marked required will likely be skipped by the majority of people, so you won’t have complete information.

Correlation and Statistics

Once you have a good data set, analytics of the plain, non-prescriptive kind would have you comb through it to try to make sense of it. For your nonprofit clients, you’ll learn things like the average amount of money a single donor will give over the course of their relationship with the nonprofit or the percentage of donors who are in certain demographic groups.

These facts are certainly good to keep in mind, but just knowing where you are doesn’t necessarily give you anything to go on. This is especially important to keep in mind because of the old adage that correlation does not equal causation. Observers untrained in statistics and data analysis may view a data set and make false assumptions based on something that seems to be obviously related, but actually isn’t.

Making Predictions

This is where advanced analytics and data scientists come in. When you’re combing through data, you’re just looking at the data. Prescriptive analytics goes in with a question. Data scientists can combine the data your client has with other large data sets to figure out personalized ask amounts and substantially improve overall fundraising efforts on an individualized basis.

Nonprofit analytics are especially difficult because nonprofits generally don’t have the resources to hire data scientists to work internally. Hiring an outside data firm like ExactAsk can be a great way to get your nonprofit clients into predictive analytics without restructuring your agency’s payroll. Predictive analytics could be the key to securing higher donations and greater return donation rates for your nonprofit clients.

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