Identifying Campaign Prospects – Donor Analytics from Simple to Sophisticated - Above Goal

Identifying Campaign Prospects – Donor Analytics from Simple to Sophisticated

Our latest blog post is from Amanda Jarman from www.fundraisingnerd.com. As a consultant, you are probably inundated with data at the beginning of a capital campaign. Amanda offers some suggestions for how you might manage through this data using common donor analytics.

Identifying sufficient prospective donors is critical to capital campaign success. It can be overwhelming to decide where to start, with so many analytical techniques you can use to identify major donors. Let’s look at several methods, from simple to sophisticated, to identify and prioritize donors in preparation for your capital campaign.

In general, as you know, the best capital campaign prospects for your clients are going to be donors with a long history of giving to the organization, who have been giving at significant annual fund levels in recent years. These donors also must have the financial means to give a significant gift, a.k.a. major gift capacity.

There are several analytical techniques you can use to identify campaign prospects. Let’s review some, from basic reporting you can do using your client’s data to more complex methods which may require help from a third party or the person charged with analytics on your team.

The initial capital campaign prospects should be fairly easy to find, since they will be your client’s best current donors. Once you receive data from your client, it should be easy to report on a few basic giving stats, like lifetime giving, giving in recent fiscal years, or largest gift amount. Sort by the top donors in each category to find the  campaign prospects. Depending on the size of your campaign, this might be sufficient to yield the prospects you need. Otherwise, these methods may be a useful starting point, to be followed by more sophisticated methods as you expand your prospect list.

The first giving statistic to look at is lifetime giving, or the total amount that a donor has given to the organization. When using lifetime giving to identify donors, be sure to include both hard and soft credit giving. (Soft credit is recognition for gifts given by other legal entities, e.g. donor advised funds, small businesses, and family foundations.) Because the best future donors are often the best previous donors, lifetime giving is a great way to identify those who are likely to continue supporting the organization.

However, lifetime giving does have a couple of drawbacks. It doesn’t necessarily identify major gift capacity. Someone may have a high lifetime giving total, all from modestly-sized gifts. That makes them a great planned gift prospect, but does not guarantee they will be a current capital campaign prospect. As well, a high lifetime giving total is exciting, but if the last gift was ten years ago, the person may not be such a great prospect after all.

To be sure you are identifying active donors, you might decide to look at cumulative giving in recent years. This will give a good sense of both the scale of a donor’s giving and how recently they have given. However, it will not take overall longevity into account. And we still don’t necessarily know that the donor has major gift capacity, unless the giving total is very large.

You may also decide to look at a donor’s largest single gift or pledge payment amount, which can give a better indication of capacity. In general, gifts of $1,000 or more (made in one single payment) are often indicative of larger major gift capacity.

Each of these giving factors is a useful indicator on its own, but none of them give you a complete picture. Enter RFM analysis. RFM stands for Recency, Frequency, and Monetary. RFM is a technique borrowed from customer analysis. It’s used to identify a company’s best customers: those who shop often, spend a lot, and have done so recently. Adapted for the nonprofit sector, RFM is a fantastic technique for donor identification because it balances three factors: how recently a donor gave to you, how often they do it, and how much they’ve given. Basic RFM scoring is pretty easy to do yourself.

These are all helpful techniques for understanding likelihood, but are not necessarily helpful in uncovering major giving capacity. This is where wealth screening comes into play. Wealth screening, sometimes also called asset screening, helps you to identify individuals who may have major gift capacity. This will be most helpful for organizations with ambitious capital campaign goals and large ask amounts at the top of their gift table.

The way an asset screening works is that you send a list of prospective donors and their home addresses to a wealth screening vendor. The vendor matches the prospect list against proprietary databases, which contain personal information about many people who live in the United States. You will receive specific asset information about each person the vendor is able to match to, e.g. a list of properties owned, along with the assessed and/or fair market value; information about stock held by insiders at public corporations; and more.

Based on this information, the vendor will provide an estimate of each prospect’s major gift capacity, expressed as the total amount they could give to nonprofit organizations over a five-year period. This will allow you to home in on specific ask amounts for each prospect. Because you will receive a lot of data about each prospective donor, it is crucial to have a plan in place to review and verify screening results, which require human review due to the messy nature of the data returned.

The downside to asset screening is that while it will help identify prospective donors with the capacity to give, it won’t tell you who is likely to give to you. This is why most wealth screening vendors pair their asset screening with a likelihood scoring or predictive modeling project.

Likelihood scoring uses a variety of factors, which could include your client’s internal data (e.g. prior giving to the organization) and external data (e.g. known giving to other organizations) to identify donors who appear to have a propensity to give. (RFM analysis is a simple version of likelihood scoring.) Likelihood scoring is not necessarily based on statistical analysis, though it could be.

Predictive modeling is a common type of statistical analysis used in fundraising. A predictive model assesses a variety of internal and/or external data against a desired outcome, e.g. making a major gift, to identify which factors are significantly correlated with that outcome. Predictive models allow you to identify people who behave like major gift donors, but have not yet given a major gift to the organization.

For example, a predictive model may find that donors who have given to the organization for five or more years, have given at least five gifts of $1,000 or more, and have participated in your client’s membership program, are those who are most likely to become major gift donors. Every prospective donor you include in the model can be scored against this factor. The more each person matches the profile of a major donor, the higher their score.

Each of these techniques is a useful prospect identification method, and may be used in combination with one another. When identifying your initial campaign prospects, you could use simple giving statistics like lifetime giving, and then introduce more sophisticated methods as your campaign progresses. Perhaps you might use an RFM project to identify the best donors, followed by an asset screening of the top prospective donors identified by the RFM project.

The most important thing is to have enough prospects to fill your campaign gift table. Use as many prospect identification methods as it takes to get there! Start simple, and build from there.

As the president of Fundraising Nerd, Amanda Jarman teaches nonprofit organizations how to manage their donor data.

March 19, 2018