If you’re like most peer-to-peer fundraising (P2P) staff, you’re drowning in data. You ascertain averages, browse benchmarks, and create charts. You detail donations, edit estimates, and fetch files. You hoard history, gather graphs, and index indicators. We’re very serious about our data in P2P!
Sometimes we all get so caught up in reporting that it’s easy to forget about the people behind those numbers. Ironically if we consider the use of MORE data – just different data than we’re used to – we can bring our focus back to our constituents. How? First, let’s take a look at how we’ve been using data in P2P and the direction we’re heading.
Much of the data we have historically focused on in P2P is in the form of descriptive and diagnostic analytics. Descriptive analytics tells us what happened and typically include reports that tells you things like age, address, registration date, and fundraising statistics. Diagnostic analytics attempts to explain why something happened and helps to identify cause and effect. You are using diagnostic analytics if you have dashboards or established benchmarks for your campaigns.
This type of analysis has served us fairly well to date. The thing with these analytics though is they focus predominantly on what’s happened in the past. They tell us nothing about what might happen in the future.
For the P2P industry to get to the next level of analytics, we need to focus on predictive analytics, which tells us what will likely happen next.
We have been so focused on data that looks into the past that we are risking our future! We see future-looking analytics in action every time we receive a coupon for items we are likely to buy based on our past buying habits. When Amazon suggests items to purchase, Netflix recommends a new show, or Facebook determines what posts to show in your feed.
Now, I know what you’re thinking. “My nonprofit is certainly not Amazon, Netflix, or Facebook. We can’t do what they do!” We tend to shy away from the predictive data strategies that are now widely available because we think only big companies can use them. This simply isn’t true! In order to stay relevant and continue to increase revenue, it’s time we stopped being so shy!
Did you know your peers in other types of fundraising have been using advanced analytics for years? Predictive models help identify, for example, those in your database who are best suited for specific types of giving, such as annual, planned, and major giving.
But what about P2P fundraising? We all know that a good fundraiser can raise just as much, if not more, than some donor prospects. Plus, P2P has the added benefit of raising awareness and bringing more people into the organizational fold. So it’s about time we joined our peers in using similar data strategies to increase P2P participation and revenue.
We’re already aware of some predictive actions fundraisers take. We can assume with some certainty that P2P participants who make a gift during registration, update their personal page, or connect to Facebook will likely raise more money than those who do not take these actions.
To undertake a strategic initiative using predictive analytics though, we have to go even deeper. Predictive analytics uses big data, algorithms, and machine learning to predict outcomes. Here’s how it could work if you wanted to determine, for example, how likely each person in your database is to participate in a P2P campaign:
- Take the information you have – name, contact information, giving history, etc.
- Pair that with information that is not in your database – data on constituent consumer behavior, social media activity, education, wealth, other philanthropy, etc.
- Based on what is known about P2P participants using all the data available – usually thousands of data points – assign a score to each constituent that indicates likelihood to participate.
- Use those scores to target the right people and make better decisions on where to invest time and money.
Many organizations don’t have the luxury of having data scientists on staff and instead use technology solutions to do the heavy lifting for them, making it accessible to organizations both big and small.
So how can the predictive modeling project described above help us focus on people instead of numbers? By assigning every person in the database with a score between 0-1000, we could create segments based on likelihood:
- 701-1000: Likely to participate in a P2P campaign
- 501-700: Somewhat likely to participate in a P2P campaign
- 0-500: Not likely to participate in a P2P campaign
We can then make decisions on how much time, budget, and effort to commit to each group. Those likely to participate might receive a personal phone call to invite them to register, along with a glossy mailer, multiple emails, and exposure to Facebook ads. Those somewhat likely to participate might receive a simple flyer and a several emails. Those not likely to participate might only receive a few emails closer to the campaign date.
What’s not to love about this newfound data? With it you can:
- Reallocate resources by spending more time and money on those who are most likely to participate.
- Decrease or eliminate investment in those who are not likely to participate.
- Drastically reduce the need for “spray and pray” recruitment methods.
- Justify why certain constituents should be added to a P2P communications track when some people in the organization are wary of “over-messaging.”
- Overlay data with geography to determine the best new area to launch a campaign based on concentration of high scoring constituents.
The use of predictive analytics isn’t just for the likes of Facebook, Amazon, and Netflix anymore! Advanced analytics will help us be better stewards of our donor and fundraiser dollars, allowing us to use data-driven predictions to lower costs, increase participation, and ultimately grow revenue.
Are you ready to get serious about data in your P2P program? You don’t need a crystal ball to explore Advanced P2P Insights!