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Compare incentive options by synthetic cost, modeled lift, and net value.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "margin-aware-incentive-scenarios" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/aibast-agents-library/main/solutions/cart-abandonment-recovery/manual/skills/incentive-optimization/SKILL.md 2. Save it as ~/.claude/skills/incentive-optimization/SKILL.md 3. Reload skills and tell me it's ready
Model the following three acquisition incentive concepts as options for approval. Compare fixed synthetic cost impact, estimated conversion lift, and net value, then rank them from highest to lowest net value: Option A, Option B, Option C. Do not create any coupon or real benefit.
A scenario comparison table, key assumptions, and a ranked recommendation, clearly framed as options for approval.
For a repeat-purchase campaign, compare different incentive concepts by fixed cost impact, modeled lift, and net value. Provide conservative, base, and optimistic scenarios. Label every result as an option for approval and do not issue any actual reward.
A comparison across three scenarios showing which concepts are more promising for further approval.
Given multiple incentive concepts, screen for options for approval with lower fixed synthetic cost impact, reasonable modeled lift, and positive net value, and explain why others are eliminated. Do not generate discounts, gifts, or shipping benefits.
A shortlist and rejection list with brief evaluation reasons for each concept.
Before launching a campaign, a growth manager can compare incentive concepts by cost impact, modeled lift, and net value to decide which ones should move into approval. It is suited for upfront evaluation rather than issuing benefits directly.
When a team is considering several incentive ideas at once, it can treat each result as an option for approval and compare them side by side. This helps prioritize concepts with stronger net value.
The README describes a margin-aware skill for comparing incentive scenarios. Its core purpose is to evaluate incentive concepts using fixed synthetic cost impact, modeled lift, and net-value comparisons. It also stresses that every output should be framed as an option for approval rather than an executed offer. The document explicitly forbids creating coupons, discounts, gifts, shipping benefits, rewards, or purchases.
Compare fixed synthetic cost impact, modeled lift, and net-value scenarios. Call every result an option for approval. Do not create a coupon, discount, gift, shipping benefit, reward, or purchase.
It compares incentive concepts by fixed synthetic cost impact, modeled lift, and net-value scenarios to help a growth manager evaluate options. The outputs are meant to be treated as options for approval.
No. The documentation explicitly says not to create a coupon, discount, gift, shipping benefit, reward, or purchase.
The provided material does not include installation steps, runtime details, or key requirements; for specifics, see the source repository.
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