Use Case: Donor Lead Scoring

Category: Development

GPT Model Used

Custom-built AI model for identifying high-potential donors based on engagement, giving history, and behavioral data.

Data Structure

To effectively score donor leads, the model requires the following data:

  • Donor Profile Data: Name, Email, Customer ID, past giving history

  • Giving Behavior: Donation frequency, recency, average gift amount, lifetime giving total

  • Engagement Metrics: Event attendance, volunteer involvement, email open/click rates, personal meetings/interactions

  • Affinity & Wealth Indicators: Wealth screening data, prospect research scores, philanthropic interests

  • Demographic & Behavioral Data: Age, location, past interactions with fundraising campaigns

  • USE AOi CORE DONOR MODEL

PDF Strategic Knowledgebase

  • Donor segmentation and engagement strategies

  • Major gifts fundraising framework

  • Wealth screening and donor research best practices

  • Case studies on donor cultivation success

Model Description

The AI model assigns a lead score to each donor prospect based on their likelihood to increase giving. It evaluates donation patterns, engagement with development efforts, and affinity indicators. The model outputs:

  • High-Potential Donors (consistent giving, high engagement, wealth indicators)

  • Medium-Potential Donors (active donors but inconsistent or lower giving amounts)

  • Low-Potential Donors (infrequent or small gifts, low engagement)

This allows development teams to prioritize cultivation efforts and tailor outreach strategies.

Model Parameters

  • Temperature: 0.3 (keeps responses deterministic but allows slight flexibility)

  • Scoring Algorithm: Weighted ranking based on recency, frequency, monetary (RFM) analysis, and wealth indicators

  • Prompting Strategy: Uses structured prompts to analyze donor behavior and generate recommended engagement actions

  • Thresholds:

    • 80+ Score → High-Priority Major Gift Prospect

    • 50-79 Score → Mid-Level or Recurring Giving Prospect

    • Below 50 → General Annual Fund Donor

Example Output

"This donor (Jane Smith) has a 90% likelihood of upgrading to a major donor level. She has given consistently for the past three years, recently attended a VIP donor event, and has a wealth screening score indicating capacity for larger gifts. A personalized meeting and tailored cultivation strategy could result in a major gift pledge."

Real-World Example: [Placeholder]

[Insert a real-world example of how an arts organization used Donor Lead Scoring to identify major gift prospects and increase fundraising revenue.]

Recommended Action Plan

To implement Donor Lead Scoring, follow these steps:

1️⃣ Prepare Your Data

  • Compile donor history, wealth screening results, and engagement tracking.

  • Ensure CRM data is structured and consistent across fundraising records.

2️⃣ Train Your AI Model

  • Use historical donor data to identify patterns in major gift upgrades.

  • Fine-tune the model’s scoring weights based on your donor base.

3️⃣ Integrate with Fundraising Strategy

  • Set up automated workflows:

    • High-potential donors receive personalized outreach from development officers.

    • Medium-potential donors get mid-level or monthly giving offers.

    • Low-potential donors stay engaged through general appeals and annual campaigns.

4️⃣ Monitor & Optimize

  • Track conversion rates and refine the model based on giving trends.

  • A/B test engagement strategies to improve donor retention and upgrades.