View presentation posterUnderstanding when opera audiences become donors.
A Rice D2K collaboration with Houston Grand Opera: connecting audience profiles, donation timing, and fundraising decisions through survival analysis.
Project Scale and Results
- Customer records · 2010–2024
- ~1,500
- Fields across 4 sources
- 46
- First-donation RSF · Evaluation C-index
- 0.733
Combined customer data from surveys, ticketing, donations, and marketing. Saved notebook experiments compare models for first and repeat donation timing; the results below distinguish training scores from evaluation scores.
The question
Which demographic and time-dependent factors are associated with a first donation—and a return donation?
My contribution
I worked across the full project with the team: merging and cleaning customer data, exploratory analysis, survival modeling, evaluation, and business recommendations. I also presented our work and answered questions at the poster session.
Approach & deliverables
Integrate Customer Data
Combined survey, ticketing, donation, and marketing data using customer IDs to connect audience characteristics with attendance and donation history.
Analyze Time to Donation
The team used Kaplan–Meier curves, Cox proportional hazards modeling, and random survival forests to study time-to-event outcomes, including first and repeat donations.
Translate Findings for Stakeholders
Explored how characteristics such as age, income, and education were associated with donation timing, and discussed targeted outreach and audience development recommendations.
Where Are the Customers?
Mapped customer ZIP codes to explore their geographic distribution across the United States. The heatmap helps describe audience reach; warmer areas indicate stronger concentrations in the displayed data, not higher donation rates.
When Might a Customer Donate?
Survival analysis studies how long it takes for an event to happen. Here, the event is a first or second donation. Cox regression gives an interpretable statistical model; Random Survival Forest (RSF) combines decision trees to capture more complex patterns.
| Task and Model | Training C-index | Evaluation C-index |
|---|---|---|
| First donation · Cox, full feature set | 0.731 | 0.717 |
| First donation · Random Survival Forest | 0.800 | 0.733 |
| Second donation · Cox, before feature selection | 0.603 | 0.581 |
Saved notebook outputs, rounded to three decimals, from 80/20 splits. These are exploratory results: evaluation data were also consulted during feature and parameter selection. They are not a fresh, untouched final test. The poster’s second-donation score of 0.61 is from a full-data fit; feature-removal experiments reached about 0.625 on the reused evaluation split.
Reading the Results
C-index · Which Customer Donates Earlier?
Measures how well the model orders donation times among customer pairs that can be compared. 0.5 is chance-level ordering; 1.0 is perfect ordering. A score of 0.733 is roughly 73 out of 100 comparable pairs ordered correctly, with ties receiving partial credit. It does not mean 73.3% of customers will donate.
Survival Probability · Not Yet Donated
In these plots, “survival” means the donation has not happened yet. A value of 0.7 at year 2 means an estimated 70% chance of no donation by then; 1 − 0.7 = 30% is the estimated chance of a donation by that time. A faster fall indicates earlier predicted giving.
These curves illustrate model predictions, rather than observed conversion rates.
From Customer Profile to Outreach Plan
A Profile to Explore
The team’s analysis highlighted Houston-based subscribers aged 55+, with a bachelor’s degree or higher, annual household income of $125,000+, and strong satisfaction and willingness to recommend HGO. This describes a candidate outreach segment, not a rule that every donor must match.
Recommended Actions
- Frequent Attendance
Give repeat visitors relevant follow-up and consider annual visit frequency when planning outreach.
- Subscription Relationships
Build on existing subscriber relationships and strengthen subscriber benefits.
- Strong Recommendation Intent
Support engaged customers with a good experience and opportunities to recommend HGO to others.
NPS (Net Promoter Score) summarizes how willing customers are to recommend an organization. Here, recommendation responses help describe engagement; they are distinct from overall satisfaction.
These are proposed actions informed by the analysis; resulting fundraising gains were not measured in this project.
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