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5 High-Level Steps to Predictive Analytics in Fundraising

Knowing where you’ve come from and where you are can help you prepare for the future. Understanding where you are going towards your end-goal, even if it's one that has a lifeline pursuit, is crucial for making future plans. Predicting how those plans go is something that requires some work, but can be done with the insight available at the present time. Predictive analytics can help an organization understand where it can reliably stand in the future based on past data that’s been gathered and assessed.

Here are the five steps to know and what to do when engaging in predictive analysis:

1. Define Requirements

The first thing is to know where you want to end up, what goal you plan to accomplish by a certain date. Data analytics can’t predict the whole future, but when it comes to reliable funding, returning donors, total assets and campaign outcomes, you can predict any one of them with enough data. Define what goal you will work toward predicting.

2. Explore Data

Once you have a goal, you need to assemble and explore the data available. Break down the data you have into what is important for the goal. If you’re looking into donor availability, you won’t need things like expenditures to find out more. Set aside all the necessary data to look through and prepare it for further analysis. A statistician or data analyst will know best what figures to pick and what data to comb through.

3. Develop and Test Model

After gathering data, you will need to build your model, a workable and viewable form of how the data changes over time and in various ways. This is the step where charts and graphs can be made to illustrate the rising or falling of data along the months or years that are being viewed. The more detailed, the more accurate a prediction will be. Data scientists can develop the most useful models for the given goal which will reliably create a predictive figure.

4. Deploy Model

Once the model has been constructed of all the past data up to the present, it’s time to implement the model and issue some predictions. This means you will be able to see how all the data has interacted thus far, and from there, you can make a plan as to where it might go next. If the data has a similar track along months or years, you can figure out where it will go next. Patterns tend to repeat, after all.

5. Validate

Once you have your predictive model, you can plan around it and validate the results as they come in. Set it as a standard for performance to look forward to. If you start meeting, or exceeding, expectations then you’ll know the predictive analysis was accurate and correct. If you underperform, you may want to look for corrections, or perhaps make adjustments that are a bit less ambitious but more realistic.


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