Fundraising Analytics

Using Data to Guide Strategy

Joshua M. Birkholz

• Publisher: Wiley
• Year: 2020
• Print ISBN: 9780470165577
• eBook ISBN: 9781119782353
• Edition: 1
• Topic: Business
• Subtopic: Nonprofit Organizations & Charities
• Index: Business

Fundraising Analytics: Using Data to Guide Strategy Fundraising Analytics shows you how to turn your nonprofit's organizational data—with an appropriate focus on donors—into actionable knowledge. The result—A vibrant, donor-centered nonprofit organization that makes maximum use of data to reveal the unique diversity of its donors. It provides step-by-step instructions for understanding your constituents, developing metrics to gauge and guide your success, and much more.

Chapter 1
Overview of Fundraising Analytics

There is a fundamental change emerging in 21st-century fundraising. This change is not driven by increasingly sophisticated nonprofit organizations; nor is it propelled primarily by the integration of MBAs or other smart people into the sector. Not even the compelling need for support of worthy causes produced this evolutionary leap. In fact, it is the most important component of the philanthropic partnership that is moving us forward: We are changing because of our donors.

Donors approach philanthropy in a completely different way. They make decisions more thoughtfully. Their gifts follow their own intended purposes. Donors seek a return on their philanthropic investments. And they desire an increased level of personalization.

Organizations embracing this change are climbing a mountain of success without zenith, while others, forcing their own models onto their donors, are fighting in the foothills. In this time of great wealth and generosity, I wish the mountain of success for you. But more importantly, I wish to see the worthy causes you represent feed our world, educate our children, wipe out horrible diseases, and accomplish many good things. That is why I wrote this book.

As a fundraising strategist and student of philanthropy, I see tools and techniques consistently produce wonderful results. I have seen these same tools and techniques fail. When we put our hope in devices and skills without an appropriate focus on donors, we rarely succeed. As I present an approach to using data to guide strategy, I hope you realize that I really intend for you to use people to guide strategy. But these are not just any people. These are the people who care passionately for what you do. Likely, you share this passion, or you would not be where you are.

Every person has a thing, a person, or an idea they cherish. For most people, there are many things, people, and ideas. Things might include home, community, or an heirloom. People might include significant other, children, all children, or the poor. Ideas might include peace, education, religion, artistic expression, and health. This treasured grouping of things, people, and ideas might be called a person’s value portfolio.

Everything a person does in life in guided by his or her value portfolio. It is the reason people work. It is the object of their leisure. It is what they pass on to their children. And, most importantly for this book, it is the beneficiary of their philanthropy. Donors do not give to you; they give to their value portfolios.

Every nonprofit organization also has a value portfolio. This is the purpose of what they do. This is the reason they exist. This is where they spend the gifts they are given. When the value portfolios of a donor and an organization align, an interchange of investment and impact is the result. This is the key to successful, major gift fundraising.

Obviously, using this key requires something of us. First of all, we need to understand the value portfolios of our donors. Next, we need to understand the value portfolio of our organization. Then, we need to bring them together.

Analytics is a suite of metrical tools and techniques for understanding the past and projecting the future. For analytics to be effective, we must use it in the context of our work. Most importantly, it must be used in the context of our donors.

We can use analytics to understand the value portfolios of our donors. Why do they give to us? Who is most aligned to our value portfolio and warranting attention? Which field officers most effectively position which portions of the value portfolio? Which prospects have other or even contrasting values? Are we spending the right time with the right prospects using the right field officers?

We can use analytics to understand our fundraising programs. How do we spend our time? Which of these tasks contribute to increased giving? Which tasks detract from fundraising success? Are we subject to the right metrics? Are we using metrics at all?

We can use analytics to pave our road for the future. Which prospects will be our top donors 10 years from now? How many new field officers should we hire? Will our infrastructure support this growth? What should our campaign goal be? Is it attainable? What are the factors for our success?

Using data to guide strategy is more than just database technology. It is the core of what we do. Essentially, we are using people to guide strategy. We are using our values to guide strategy. We are using the values of our people to guide strategy. And, most importantly, our strategy is grounded in facts, not assumptions.

Defining Analytics

In the fundraising industry, many diverse definitions of analytics can be found. Largely, this is because many vendors doing business in the nonprofit market describe their product offerings as analytics. Often, each vendor offers a portion of the broader services generally associated with the term. Some vendors provide predictive modeling in combination with database screening services. Others may conduct market research studies to determine attitudes and giving motivations. And still others provide metrical assessments of annual giving or membership programs. All of these services are analytics, but rarely do you see all of these services in one place.

I would describe analytics as a suite of statistical tools and techniques used to:

• Analyze constituencies
• Build models to predict constituent behaviors
• Make organizational decisions by:
  • Evaluating program performance
  • Projecting future program performance

It is likely you have heard many accounts of corporations analyzing their customer base. Corporations such as Best Buy, J.P. Morgan, and Volkswagen have conducted thorough analyses of who purchases their products and services and why they make these purchases. These descriptive analytics studies generally focus on demographics, geography, behaviors, transactional history, and interests. The goals of these projects are to produce a taxonomy of key groups. Then they market and engineer their products and services with these taxonomies in mind.

When a higher education development office develops custom strategies for alumni, young alumni, community members, and faculty, they are using similar customization. When a health care organization approaches patients differently than community donors, they also use similar customization. However, these are natural organizational segments. There may be many other derived segments informed by demographic characteristics, entry points, giving motivations, occupation, industry, and affinities. These characteristics may warrant revised segmentation strategies.

Many of us most often interact with a predictive model when we seek financial services. The credit score or FICO score, named for the Fair Isaac Corporation, is an everyday example of a predictive model. These scores predict a statistical likelihood of loan repayment. As a result of analysis of the characteristics of those who do and do not pay back loans, the credit score statistically predicts how likely you are to pay back your loan. When a lender chooses to give you a loan, they made the decision based on your financial ability and likelihood to pay.

In fundraising, major gift officers assess their portfolios according to financial ability, or capacity, and the likelihood of making a major gift. This likelihood is informed by the prospect’s connection to the organization and/or an alignment of interests between the organization and prospect (matching value portfolios). This assessment is made through interpersonal interaction. Prospect researchers will also seek out capacity and likelihood information in their data gathering and interpretation. Also...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright Page
• The AFP Fund Development Series
• Acknowledgments
• Foreword
• Chapter 1: Overview of Fundraising Analytics
• Chapter 2: Understanding Your Constituents
• Chapter 3: Analytics and Prospecting
• Chapter 4: Analytics and Campaign Planning
• Chapter 5: Data-Driven Prospect Management
• Chapter 6: Annual Giving Analytics
• Chapter 7: Selecting Data for Mining
• Chapter 8: Descriptive Analysis: Basic Statistics and Scoring Models
• Chapter 9: Regression Analysis
• Final Thoughts on Fundraising Analytics
• Glossary of Common Analytics Terminology
• AFP Code of Ethical Principles and Standards of Professional Practice
• Index
• End User License Agreement