Typical Predictors for Predictive Analytics in Non-Profit Organization

• Donor giving history: Previous giving, cadence of giving, amount donated
• Donor demographics: Age, income, zip code, gender
• Appended data: Donor psychographics, lifestyles, attitudes
• Marketing data: Product behavior, historical campaign data
• Additional: Donation, donor, or household-level data

All of this data is used to understand the relationship between the two variables. Finding the model and determining its accuracy is what the predictive modeling process is all about. This process often requires several iterations and the quality of the model requires an understanding of the organization problem and the data, modeling algorithms and parameters, software, and implementation practices.

Best Practices 

Before getting started with predictive analytics it’s important to understand the pieces that must all come together to ensure success. We’ve defined these below:

Define a Clear Objective: Decide what your objective is. Do you want to activate prospects? Increase donor value? Be clear on this before diving into any data.

Validate Existing Data Sets: It may sound obvious but make sure you are working with data sets that are reliable and offer meaningful results. Inaccurate data leads to inaccurate outcomes.

Upskill Your Team: Ensure your team is trained on how predictive analytics works and fundamental concepts such as A/B testing, data cleaning methods, etc. Or, work with outsourced experts who know.

Invest in Technology and Expertise: Onboarding a full-service advanced analytics solution, like Lityx, Salesforce Einstein  empowers every level of your team with the capacity for predictive analytics, optimization, and automation.

Add the Right Tools: Ensure that your database, CRM, marketing automation systems, and email systems are optimized and integrate with the predictive analytics platform.

Get Team Buy-In: Make sure your entire team, especially senior management, is committed to a data-driven culture and marketing approach.

Benchmark & Measure: Define key metrics, such as donor lifetime value, conversion rates, etc. Measure these regularly to begin to understand the actionable data that comes from predictive analytics.

With prescriptive analytics, you are combining and analyzing data to make a prediction, then creating options to take advantage of that prediction. Prescriptives use the prediction to prescribe what should be done.

Imagine using your donor data not to just report what has happened, but to predict what is likely to happen. That is the opportunity offered by predictive analytics—revealing the likelihood of a major gift or a planned gift. Predictive analytics can light the way. What does the journey down that path look like?

Let’s begin with your data. Is it good?

The common concern is that the data may not be good enough to be used for an analytic effort. Whether you are using an analytic partner or using analytic software, the hygiene of your data is critical. The statistical models work from your data, and technology in use merely enables that process. The accuracy of the data determines the accuracy of the result. Cleaning that data does not have to be painful, and a vendor may be a viable option for both cleaning the data and helping you build good data practices.

What questions do I want to ask?

Regardless of whether regression analysis, classic statistical models you saw in college, or highly proprietary statistical models are used by your scientists using software or your analytics partner, you do not have to understand the entirety of the math involved. You do need to understand what question is being answered. You are no longer reporting who gave a gift over $1k or $100k, but rather who can give a major gift. That means that access to some data sources outside of your database may be necessary to determine both affinity and capacity. The model needs to know: to what other organizations did the donor give, and how much wealth does she possess?

What will you do with the information? Can your organization react to and act upon the predictions?

If you use a partner experienced in nonprofits, and you understood the questions you were asking, the predictions will make sense. The process of using predictive analytics is only a benefit if you use the results. Using the predictions requires a cohesive organization willing to change. As with any major initiative you must consider and plan for successful adoption. The culture of your organization trumps everything else!