Data with AI

The Future of Advancement is Here

As AI penetrates all industries, Advancement will be no exception. At its heart, Advancement possesses similar challenges to any business. For this reason, Advancement departments can benefit from many of the new and exciting technologies used by other businesses. Learn more

The top performing nonprofits and Advancement departments are already using AI to improve the returns on their fundraising efforts–and an increasing number of organizations are joining them (Learn more). For good reason. AI has consistently succeeded in providing dramatic improvements in results across all industries.

Today, a university has to compete with over 1.2 million equally worthy causes that its alumni can give their limited charitable dollars to. Moreover, megatrends such as declining participation rates, generational changes in attitudes towards giving, and tax code changes that are also having a materially negative impact on fundraising. In an increasingly competitive and challenging fundraising environment, maintaining the status quo is not an option.

CueBack is at the forefront of applying AI to Advancement to increase predictability, minimize cost, and maximize giving.

Machine Learning Resources

Learn more about machine learning and its benefits to your needs.


NSL in TensorFlow

A new framework by TensorFlow to train neural networks with structured signals


A System for Large Scale Machine Learning

An open source machine learning system focused on training deep neural networks


Predictive Analytics using Extreme Learning Machine

Extreme Learning Machine, a neural network with a faster learning rate


Solving Recommendation Problems Daily

Creating recommendations as a service and lessons learned for future ML services

How AI and Predictive Analytics Enhance Advancement

Advancement Challenges:
  • Grow the number of donors
  • Increase average gift size
  • Increase gift velocity
  • Optimize resources
  • Reduce costs
  • Improve efficiency

AI technology enables Advancement to scale its operations by optimizing resources to maximize return. Predictive analytics can identify and qualify low value transactional donors that only require low touch outreach, such as phonathons. Meanwhile, Advancement can focus its most precious resource-its Major, Principal, and Planned Gift Officers-on high value donors who require a high touch strategy. AI can provide the insights needed to help cultivate the deeply personal relationships with these donors that generate the largest financial returns for the university.

CueBack’s predictive analytics helps universities unlock hidden insights within their data so they can do the following:

  • Identify prospective donors
  • Segment prospects
  • Predict who is most likely to give, how much, and when
  • Optimize Advancement resources
Predictive analytics helps colleges gain a 360 degree view of the donor experience that they can use to attract ideal donors, increase contribution amounts, and retain current donors. Learn more

CueBack’s AI process can be summarized into 4 steps: Integrate Data, Predict Behavior, Convert to Donors, and Re-optimize Models.


1. Integrate Data

The first step of the process is to align your existing donor information. Donor data from all sources across your organization, including databases, spreadsheets, and social media, can be integrated into a single solution.

Then, we marry your existing data with quantitative and qualitative data generated by your alumni through CueBack’s engagement and event management applications. This enables you to have a complete view of each alumni and your alumni base as a whole.

  • Demographic Information (In-house and CueBack)
  • Giving History (In-house)
  • Alumni Identity: alumni identity strength and how it was shaped (CueBack) Learn more
  • Alumni Connections: who is connected to who, how they are connected, and the strength of those connections (CueBack)
  • Alumni Affinities: affinity groups they were involved in and the impact they had on their life (CueBack) and
  • Many more...

2. Predict Behavior

CueBack’s predictive model uses the consolidated data to segment and score donors in order to determine the likelihood, size and timing of donations.

3. Convert Prospects to Donors

Now that you know which prospective donors to cultivate and how to allocate your resources, you need to define your cultivation strategies. For your high value alumni, there is no substitute for person-to-person interactions. The personal touch is the foundation for building the deep, long-term relationships that underpin all fundraising activities. CueBack’s AI engine provides you with deep insights about your alumni to help you build that rapport and trust. Learn more

4. Re-optimize Models

The AI process is not linear. CueBack’s AI Engine continuously uses real-time feedback from ongoing advancement and engagement activities to learn and improve its predictions and insights.


Predictive Analysis Resources

Learn about the benefits you can gain from implementing predictive AI.


Deep Neural Networks for YouTube Recommendations

Practical insights from creating a deep candidate generation model & a separate deep ranking model


Email Category Prediction Using MLP & LSTM

Effects of varying configurations and hyper-parameters on two different types of neural networks


Predictive Analytics in Information Systems Research

Why predictive analysis should be incorporated into information systems research and how to accomplish it


Predicting Bounce rates in Sponsored Search Ads

Using predictive analysis to improve the effectiveness and quality of ads

Built for AI from the Ground Up

CueBack’s unique alumni engagement and advancement strategy has been built from the ground up to fully employ the power of AI, machine learning and predictive analytics. During the design phase, we made fundamental decisions to ensure we did not just capture more data than other platforms, but that we uniquely tracked how alumni relate to each other, their affinities, and your university. The quantitative and qualitative alumni data that we capture provides the most detailed and comprehensive dataset to fully harness the power and benefits of AI.

To learn more about how CueBack’s AI engine and predictive analytics can help your university achieve its fundraising goals, contact us for a demo.

Additional Resources


Predictive Analytics Using Big Data with Data Mining

How predictive analytics can be applied to big data and make recommendations called prescriptive analytics


Predicting Audience Retention

An example of how predictive analytics is used to determine a retention score


Machine Learning Algorithms

A review of machine learning algorithm foundations and implementations with R and Python.


Large-Scale Machine Learning on Heterogeneous Distributed Systems

TensorFlow is flexible and computations expressed on it can be used on a plethora of devices