Kuis Penilaian Prospek AI

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Apa perbedaan utama antara penilaian prospek AI dan penilaian berbasis aturan tradisional?
   • Ia mempelajari bobot dari data konversi historis, bukan menggunakan titik tetap
   • Ini hanya menghitung pembukaan email
   • Itu tidak memerlukan data sama sekali
   • Ini mengabaikan ukuran perusahaan dan jabatan
2. Tugas pembelajaran mesin manakah yang paling tepat menggambarkan prediksi apakah prospek akan berkonversi?
   • Segmentasi gambar
   • Klasifikasi biner
   • Pengenalan ucapan
   • Pembelajaran penguatan
3. Mengapa akurasi biasa merupakan metrik yang buruk untuk mengevaluasi model penskoran prospek?
   • Accuracy directly measures calibration
   • Accuracy tells sales representatives why a lead will convert
   • Converters may be rare, so a model can appear accurate by predicting “no conversion” for nearly every lead
   • Accuracy automatically adjusts decision thresholds
4. According to HubSpot’s lead-scoring documentation, which data can a custom score use?
   • Only a person’s name
   • Hanya waktu dalam sehari
   • Unlabeled audio waveforms
   • Record actions or properties selected for the scoring model
5. What can a lead-scoring model return for each prospect?
   • A score or estimated conversion probability, which still needs evaluation
   • A probability that is automatically calibrated by definition
   • A guaranteed sales outcome
   • A legal determination of creditworthiness
6. What does feature importance summarize in a tree-based lead-scoring model?
   • That a feature causes conversion
   • How influential a feature is in the model’s decisions under that importance method
   • That a feature is legally required
   • The future sales quota
7. What does calibration mean for a lead-conversion score?
   • Every lead receives the same score
   • The model’s accuracy is always 100 percent
   • Predicted probabilities match observed conversion frequencies over comparable cases
   • Scores are sorted alphabetically by company
8. Why should a lead-scoring model be monitored after deployment?
   • The original threshold can never become outdated
   • Monitoring makes labels unnecessary
   • A model score permanently captures market behavior
   • Lead behavior and the meaning of historical patterns can drift