Quiz połączony z przewodnikiem · Średni Poziom

Sztuczna inteligencja w pozyskiwaniu funduszy dla organizacji non-profit i quiz dotyczący zarządzania darczyńcami

Sprawdź swoją wiedzę na temat punktacji skłonności darczyńców, RFM i kontroli majątku, testowania wycieków danych i wstrzymywania się oraz zagrożeń prywatności w zbieraniu funduszy na cele charytatywne.

Powiązane ścieżki prowadząceAI w pozyskiwaniu funduszy dla organizacji non-profit i zarządzaniu darczyńcami
Pytanie 1 z 8

What does RFM stand for in donor segmentation?

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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

  1. What does RFM stand for in donor segmentation?
    • Revenue, forecast, margin
    • Recency, frequency, monetary value
    • Reach, focus, messaging
    • Retention, fundraising, membership
  2. Why is wealth-screening capacity alone not enough to identify good major-gift prospects?
    • Wealth data is always illegal to use
    • Wealthy people never give to charity
    • Capacity data is too expensive for any charity
    • Capacity is not inclination; affinity for the cause also matters
  3. Why did the UK Information Commissioner's Office fine charities such as the RSPCA and British Heart Foundation?
    • Partly for wealth screening donors without telling them
    • For refusing online donations
    • For using RFM segmentation
    • For sending too few thank-you letters
  4. What did the 2020 Blackbaud incident demonstrate?
    • That AI models cannot predict lapse
    • That printed appeals outperform email
    • That concentrated donor data creates concentrated risk, after a ransomware attack exposed many clients' data
    • That wealth screening is always accurate
  5. Which is an example of data leakage in a propensity model?
    • Using gift history from before the mail date
    • Including a 'last gift date' field updated after the outcome
    • Using logistic regression
    • Testing on a holdout group
  6. What does a lift or gain chart show?
    • How fast the CRM loads
    • The number of emails sent per day
    • Staff hours saved by automation
    • How much of eventual income top-scored donors produced compared with random selection
  7. Why use a holdout group of high-scoring donors who do not receive the appeal?
    • Because a high score may identify people who would have given anyway
    • To save postage only
    • Because regulators require it
    • To train the language model
  8. According to the guide, what do well-used fundraising models often do?
    • Always increase the number of asks
    • Replace gift officers entirely
    • Identify donors to contact less often or through a different channel
    • Remove the need for clean data