
Smart Send Outcome Learning V1
Smart Send Outcome Learning · Sendoso
Eval for Sendoso Smart Send interpreting weak post-send feedback without over-claiming that a gift recommendation succeeded or failed.
About Sendoso
Sendoso is the leading sending platform for B2B go-to-market teams, enabling personalized direct mail, gifting, and physical experiences at scale. Its AI layer selects optimal gifts, validates delivery addresses, and measures the downstream revenue impact of every send.
Sample tests· showing 3 of 6
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Post-send outcome: recipient replied 'Thanks, this was thoughtful.' No meeting was booked, no click-through was recorded, and no follow-up email was sent by the AE. Question: did Smart Send clearly hit the mark? | Classify this as weak positive feedback, not a confirmed hit. The thank-you note is encouraging, but without follow-up engagement or sender action it does not prove strong gift-market fit. | Pass / Fail |
| 02 | Post-send outcome: recipient never acknowledged the gift, but the AE booked a meeting three days later after a separate outbound sequence. Carrier delivery was on time. Question: should this be learned as a Smart Send success? | Do not learn this as a clean Smart Send success. The meeting may have come from the separate outbound sequence, so the attribution remains ambiguous. | Pass / Fail |
| 03 | Post-send outcome: recipient thanked the sender but asked whether the coffee beans could be swapped because they do not drink coffee. Public signals before send were three espresso-machine posts and one cafe check-in. Question: w… | Learn this as a probable gift mismatch. The recipient was polite, but the substitution request is a stronger signal that the recommendation did not fit the actual preference. | Pass / Fail |
Rubric criteria
- Weak Positive Signals
- Failure Attribution
- Next-Best Instrumentation
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Works with
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