roas-forecast-attribution-modeler

What This Skill Does

The roas-forecast-attribution-modeler is a high-precision analytical engine designed for performance marketers and growth teams. It functions as a simulation environment for ad spend, allowing you to model complex relationships between budget, conversion rates (CVR), cost-per-click (CPC), and average order value (AOV) across the most influential advertising ecosystems. By integrating platform-specific nuances for Meta, Google, TikTok, Amazon, and DSPs, the skill bridges the gap between raw data and strategic decision-making. It calculates base, upside, and downside scenarios, performing sensitivity analyses that expose how minor fluctuations in funnel efficiency ripple out into major revenue outcomes. It focuses on attribution maturity, helping you understand how different window definitions (e.g., 7-day click vs. 1-day view) materially change the perceived profitability of a campaign.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/danyangliu-sandwichlab/roas-forecast-attribution-modeler Ensure your OpenClaw runtime is updated to the latest version to support the forecasting dependency modules.

Use Cases

• Budget Reallocation: Determining how to shift spend from top-of-funnel TikTok prospecting to bottom-of-funnel Google search demand without sacrificing total ROAS.
• Risk Mitigation: Running a 'worst-case' scenario simulation before a major product launch to establish stop-loss triggers based on initial spend efficiency.
• Attribution Audits: Comparing the impact of platform-native attribution vs. a 30-day lookback to identify ghost-revenue or platform overlap.

Example Prompts

1. "I have a $50,000 monthly budget for Meta and Google. Based on a $1.20 CPC, 3.5% CVR, and $68 AOV, create three scenarios (conservative, base, aggressive) and tell me the required ROAS to maintain a break-even CPA."
2. "Run a sensitivity analysis on my TikTok campaign: how does a 0.5% drop in CVR impact my net profit at our current $10k weekly spend, and at what point should I kill the campaign?"
3. "Compare our Shopify Ads performance using a 7-day click window vs. a 24-hour post-view window. Which budget distribution is safer given our 20% volatility in weekly AOV?"

Tips & Limitations

• Garbage In, Garbage Out: The accuracy of your forecast is strictly tied to the quality of your input assumptions. Always ground your base assumptions in historical channel data rather than aspirational targets.
• Platform Context: Always specify the channel. Treating Amazon Ads intent-based traffic identically to Meta's interruptive creative model will lead to inaccurate sensitivity outputs.
• Safety Guardrails: This skill is an analytical tool, not a budget manager. It will output recommendations but cannot directly trigger platform API changes for spend. Always review the stop-loss conditions generated in the 'Budget Recommendation' section before implementing changes in Ads Manager.