7 answers to data’s role in advancement

The advancement industry is quickly changing. In recent years, we’ve witnessed shifts in donor behaviors and motivating factors, largely influenced by emerging technologies, economic fluctuations, and evolving perspectives on philanthropy in the post-pandemic era. To effectively engage donors and meet increasing goals, leaders need to prioritize a robust and clean data strategy.

To learn what that looks like in practice, I’ve listed some of the questions I hear from institutions below, exploring the relationship between data and advancement.

1. Why is now an important time for advancement leaders to evaluate their data strategy?

The world is going increasingly digital at a rapid pace, leading to a massive surge in available consumer data that’s only expected to grow. Simultaneously, donors are increasingly aware of the value of their data and expect organizations to use it thoughtfully for better customized advertisements while ensuring privacy and security. So it is a critically important time to prioritize a data strategy, because failing to do so won’t just lead to missing out on valuable insights, but also falling short of meeting the expectations of your donors. More data-backed personalization means higher donor engagement, higher conversion rates, and a better donor experience.

2. What are the key elements that advancement leaders should prioritize when developing or refining their data strategy?

I’d start with analyzing specific donor engagement activity. This includes the donor’s engagement both with the institution in general and with individual marketing efforts. Think of specific actions or interests, such as attending homecoming, being a part of a scholarship program, or even engaging with specific content like articles in alumni newsletters. This level of granular data provides valuable information on how to personalize your communications and prioritize which individuals to engage with, how to approach them, and when.

3. What are some common missteps and challenges faced by advancement teams in managing and utilizing data effectively?

One of the biggest missteps I see is advancement teams placing too much emphasis on short-term goals and not thinking about the big picture and long-term opportunity. With all the pressures facing teams right now, it’s understandable that they’re focused on hitting their current fiscal year goals. But while short-term goals are important, getting too near-sighted and overlooking the bigger impact can hinder the real potential of your program. I’ve talked with some advancement teams that struggle with basic data practices like creating new appeal codes because their CRM systems are too complicated. Avoiding processes will cause you to miss out on so much valuable donor information that can help inform your next year’s strategy. Keeping your nose to the ground and a bird’s eye view of your program is key for future-proofing your institution.

4. How can a stronger data strategy impact the overall donor experience and engagement levels?

Ultimately, it all comes back to personalization and a more sophisticated, custom donor experience. You can use donor data points to tailor communication and experiences to better meet their expectations and preferences. A more personalized approach will increase your institution’s relevance in their minds and lead to higher conversion rates.

5. What role do data and analytics play in identifying and prioritizing donor prospects for major gifts and campaigns?

For the major gift prospects you already know about, having detailed data on their interests, engagement, and giving history is important for more personalized engagement and can help your major gift officers tailor their interactions accordingly.

When it comes to finding new major gift prospects, you can use analytics to analyze characteristics, demographics, engagement levels, and giving history of past major gift donors in order to identify patterns and behaviors. This can help you source non-major givers who may have the potential for a major gift in the future based on their similarities to other major givers.

6. How can advancements in technology, such as artificial intelligence and machine learning, impact decision-making in advancement strategies?

Data science and machine learning focus on using models and statistics to analyze data, automatically identifying the data points that matter most, and creating outputs like donor scores or giving likelihoods. Machine learning can also self-learn and improve efficiency over time.

While data science helps you find your key data points, AI creates and simplifies those data points into easily understandable outputs and insights. This means that with AI, an advancement leader doesn’t need to just stare at a large table of data, but can actually directly ask a question they need an answer to and receive an answer fast, without needing deep technical expertise to interpret the data.

A shift towards conversational analytics makes it easier for non-data scientists to navigate complex data environments and extract meaningful information quickly. This allows advancement teams to make more informed decisions based on data insights, ultimately improving strategies for donor engagement, campaign planning, and resource allocation.

7. How do you envision the advancement industry evolving in the next 5-10 years?

We’re going to see some exciting changes in the next 5-10 years. Think about it this way: when it comes to K-12 or undergraduate recruitment, we don’t have as much data due to privacy concerns. But with advancement, especially with known alumni and donors, we have a goldmine of information. From how they engaged with the institution to their post-engagement behavior, we have a lot of insights at our fingertips. And as we continue to use and analyze this data, our campaigns are going to get even more personalized.

In terms of tech, data science and AI are going to continue being huge factors in advancement. Data science will help us determine what’s important, while AI helps communicate those findings in a way that’s easy to understand. We’re going to continue getting better at understanding data and technology, and it’s going to get better at telling us what we need to know.