AI Associate AI Capabilities in CRM Practice Question
A nonprofit organization uses Salesforce Nonprofit Cloud with Einstein Discovery to analyze donation patterns. They have activated a story that predicts which donors are most likely to churn (stop donating) in the next three months. The story shows a top influence called 'DonationFrequency' with a negative correlation: donors who donate less than once per quarter are 40% more likely to churn. The director of development wants to use this insight to create a retention campaign. However, the story also includes a field called 'LastDonationAmount' which has a small positive influence. The development team wants to ensure the predictions are actionable. What should the administrator do to maximize the effectiveness of the Einstein Discovery story for this retention campaign?
⚠ Common exam trap
Salesforce often tests the misconception that administrators can directly edit or retrain Einstein Discovery models to suit specific needs, when in fact the platform is designed to be used as-is, with actionable insights derived from segmenting the data rather than altering the model.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a segment of donors with low donation frequency and use that as the target for the retention campaign.
The most actionable insight from the Einstein Discovery story is the strong negative correlation of 'DonationFrequency' with churn. By creating a segment of donors with low donation frequency, the administrator can directly target the highest-risk group for a retention campaign, making the prediction actionable without altering the model or its output. This approach leverages the story's findings as-is, which is the intended use of Einstein Discovery insights.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the prediction model using only 'DonationFrequency' and 'LastDonationAmount' as predictors.
Why it's wrong here
Discarding other predictors reduces model accuracy and may miss important factors.
- ✗
Delete the 'LastDonationAmount' influence from the story to simplify the output.
Why it's wrong here
Removing an influence does not make the story more actionable; it only hides information.
- ✗
Adjust the influence weight of 'DonationFrequency' to be higher in the story settings.
Why it's wrong here
Influence weights are derived from the data and cannot be manually adjusted.
- ✓
Create a segment of donors with low donation frequency and use that as the target for the retention campaign.
Why this is correct
Focusing on the strongest actionable influence maximizes campaign impact.
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