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Objective: Build an exhaustive five-year financial forecast model for Schmick, a privacy-first social media platform for niche communities, aligned with the business plan, market opportunity, and growth strategies outlined in the provided pitch decks and business plan documents.
General Requirements:
The model must be fully formula-driven and dynamic.
Use bottom-up forecasting methods wherever possible.
Ensure scalability for multiple scenarios: base case, upside case, and downside case.
Focus on both growth metrics and financial outcomes including Revenue, EBITDA, FCF, and Valuation.
Include comprehensive documentation (cell comments or separate tab) explaining assumptions and methodology.
1. π User Acquisition and Growth Modeling
Model monthly and annual user acquisition over 5 years.
Separate users into paid acquisition and organic acquisition channels.
Input Drivers to model:
Ad spend per channel (SEO, email, social, influencer marketing).
CAC per channel (already provided for 5 years β update/fine-tune).
Churn rate (monthly, forecasted to decrease yearly).
DAU/MAU ratio (currently targeted at 20%-25% range).
Build an acquisition funnel:
Impressions β Clicks β Installs β Activated Users β Retained Users (monthly).
Deliverables:
Paid vs. organic new users per month/year.
Total users, DAU, MAU by month/year.
Churned users and Net New Users.
2. πΈ Revenue Model
Map out all four revenue streams individually:
Subscription Revenue (40% of total)
Paid user conversion rate (Basic $5/mo, Platinum $10/mo).
Model churn rates separately for free vs. paying users.
Advertising Revenue (30% of total)
CPM assumptions for in-feed targeted ads.
Fill rate and impressions per MAU.
In-App Purchases (20% of total)
ARPU (average revenue per paying user) assumptions.
% of users making in-app purchases.
User Tipping (10% of total)
Tip participation rate.
Average tip size per user.
Deliverables:
Monthly and annual revenue breakdown by source.
ARPU trends and blended average across user cohorts.
3. π οΈ Cost Structure
Operating Expenses:
Marketing & Advertising (already planned at 40% of funding).
Product Development (30% of funding β detail hires, server costs, app upgrades).
General & Administrative (G&A) (include salaries, rent, insurance, legal).
Partnership Development (10% of funding).
Additional Expenses to Model:
Hosting and tech infrastructure (scaling with user base).
Content moderation (AI costs + manual reviewers over time).
Customer support.
Miscellaneous scaling costs (compliance, localization for global expansion).
Deliverables:
Fully built-out P&L with Gross Profit, EBITDA, and Net Income.
4. π° Cash Flow and CapEx Forecast
Build a Free Cash Flow (FCF) model tied to EBITDA, CapEx, Working Capital changes.
Include:
Changes in AR/AP (use working capital assumptions provided).
Taxes (assume NOL carryforwards at early stages).
Investment and financing activities if applicable.
5. π Valuation
Develop a Discounted Cash Flow (DCF) valuation.
Use provided WACC assumptions (~13%) and 5% long-term growth.
Terminal Value based on Year 5 EBITDA (use 8.5x to 12.5x multiples).
Perform sensitivity analyses:
WACC +/- 1-2%.
Terminal multiple scenarios.
DAU/MAU conversion sensitivity.
Build optional venture-style valuation triangulation:
Revenue multiple benchmarks from comparable companies.
6. π Scenario Planning and KPIs
Build three cases:
Base Case: As outlined in the deck (expected).
Upside Case: Faster adoption, higher premium conversion.
Downside Case: Slower adoption, lower ARPU.
For each case, track:
CAC trends
Payback periods per user
LTV/CAC ratio
FCF break-even point
Breakeven DAU/MAU needed to sustain operating cash flow
7. π Final Deliverables
Full dynamic Excel file.
Executive Summary (1-2 pages).
Charts: User Growth, Revenue Growth, EBITDA Trend, FCF Trend, Valuation Sensitivity.
Model audit checklist to ensure all formulas are correct.
Special Notes:
Align narrative with Schmickβs brand focus on privacy, wellness, and authentic connections.
Assume initial $1M Seed Funding as per the decks to fuel early stages.
Be mindful of the core user demographics: U.S., Canada, UK initially, expanding later.
Cross-validate all top-down projections (TAM, SAM, SOM) with bottom-up outputs to sanity check.
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