mcpbeat

Ecom Promo Roi

asgard-ai-platform/ecom-promo-roi

Calculate and analyze promotional ROI including incremental sales lift, margin impact, and promo type comparison. Use this skill when the user needs to evaluate whether a promotion was profitable, compare promotion types, simulate promo scenarios, or optimize promotional spending — even if they say 'was this sale worth it', 'which promo type works best', 'are we giving away too many discounts', or 'plan our next promotion'.

5k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
223
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/asgard-ai-platform/skills --skill ecom-promo-roi

What comes with it

14 679 bytes besides the instruction
examples/sample_scenario.md
references/promo-testing.md

The instruction itself

8 sections, as written by the author

Promotion ROI Analysis

Framework

IRON LAW: Measure INCREMENTAL Sales, Not Total Sales During Promo

A promotion that generates $100K in revenue during a sale week looks great —
until you realize $80K would have happened anyway (baseline). The incremental
lift is only $20K. If the discount cost $25K in margin, the promo LOST money.

Promo ROI = (Incremental Revenue × Margin - Promo Cost) / Promo Cost

Key Metrics

| Metric | Formula |

|--------|---------|

| Incremental Revenue | Promo period revenue - Baseline revenue (what would have happened without promo) |

| Promo Cost | Discount amount + marketing spend + operational cost |

| Promo ROI | (Incremental Gross Profit - Promo Cost) / Promo Cost |

| Cannibalization Rate | % of promo sales that would have occurred at full price |

| Pull-Forward Rate | % of promo sales that are just earlier purchases (customers would have bought next week anyway) |

Promo Type Comparison

| Type | Mechanism | Pros | Cons |

|------|-----------|------|------|

| % Discount | 20% off everything | Simple, high traffic | Erodes brand, trains discount-waiting |

| $ Off | NT$200 off orders >NT$1000 | Drives AOV up | Less exciting for low-AOV customers |

| BOGO | Buy one get one free | Moves inventory fast | 50% margin hit, attracts deal-seekers |

| Gift with purchase | Free item with min spend | Protects price perception | Cost of gift, may not drive incremental |

| Flash sale | Time-limited deep discount | Urgency, high engagement | Operational stress, cannibalization |

| Loyalty points | Earn/redeem points | Builds retention, deferred cost | Complex to manage, redemption liability |

Analysis Steps

Phase 1: Establish Baseline

  • What would revenue have been without the promo? (prior period, prior year same period, or control group)
  • Account for seasonality, day-of-week effects, and trend

Phase 2: Calculate Incremental Impact

  • Total promo revenue - baseline = incremental
  • Subtract cannibalization and pull-forward estimates
  • Calculate gross profit on incremental (not revenue)

Phase 3: Calculate Full Cost

  • Discount dollars given up
  • Marketing spend (ads, email, creative production)
  • Operational cost (warehouse overtime, customer service spike)

Phase 4: Compute ROI and Decide

  • ROI > 0: Promo was profitable
  • ROI < 0 but strategic (new customer acquisition, inventory clearance): May still be justified
  • ROI < 0 with no strategic rationale: Don't repeat

Output Format

# Promo ROI Report: {Promotion Name}

## Promo Summary
- Type: {discount type}
- Period: {dates}
- Offer: {details}

## Results
| Metric | Value |
|--------|-------|
| Total Revenue (promo period) | ${X} |
| Baseline Revenue (estimated) | ${X} |
| **Incremental Revenue** | **${X}** |
| Incremental Gross Profit | ${X} |
| Promo Cost (discount + marketing + ops) | ${X} |
| **Promo ROI** | **{X%}** |

## Profitability Assessment
{Profitable / Unprofitable — with context}

## Recommendation
{Repeat / Modify / Discontinue — with rationale}

Gotchas

  • Baseline estimation is the hardest part: The accuracy of your ROI depends on how well you estimate what would have happened without the promo. Use prior year same period as a starting point, adjust for trend.
  • Post-promo dip is real: Sales often drop BELOW baseline after a promotion (customers pulled purchases forward). Include the dip period in your analysis.
  • Discounts are addictive: Customers learn to wait for sales. Track the % of revenue sold at full price over time — if it's declining, you have a discount dependency problem.
  • New vs existing customer mix matters: A promo that acquires 500 new customers at negative ROI may be worth it if their LTV justifies the acquisition cost. Track separately.

References

  • For A/B testing promotional offers, see references/promo-testing.md

How to use it

Copy the folder

Take asgard-ai-platform/ecom-promo-roi from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.