Context

As a Business Analyst and Senior Business Analyst, my company operated several seasonal and niche business lines that generated modest but meaningful profit contributions. Each line had different revenue patterns, cost structures, and operational constraints. Leadership needed a forecasting model to project quarterly revenue, expenses, and contribution margin across all lines to support staffing, inventory planning, and off‑season cash‑flow decisions.

Problem

The company lacked a unified forecasting model that:

  • Consolidated three different business lines with different seasonality

  • Captured variable vs. fixed cost behavior

  • Allowed scenario planning (base, upside, downside)

  • Provided quarterly visibility into contribution margin

  • Enabled leadership to make informed operational decisions

Existing reporting was fragmented, reactive, and not structured for forward‑looking analysis. I used the formulas on the right to do this.

Approach & Methodology

I built a driver‑based forecasting model that projected quarterly revenue, variable costs, fixed costs, and contribution margin across all business lines.

Line A — Seasonal Service (Spring–Fall)

  • Monthly revenue (active months): $450,000

  • Off‑season revenue: $0

  • Variable cost rate: 38%

  • Fixed monthly cost: $120,000

Line B — Year‑Round Product Sales

  • Monthly revenue: $220,000

  • Variable cost rate: 52%

  • Fixed monthly cost: $60,000

Line C — Quarterly Niche Contract Work

  • Quarterly revenue: $650,000

  • Variable cost rate: 30%

  • Fixed quarterly cost: $80,000

Seasonality

  • Line A active: April–October

  • Line B: year‑round

  • Line C: once per quarter

Scenario Assumptions

  • Base: normal demand

  • Upside: +10% revenue

  • Downside: –8% revenue

Tools Used

  • Excel (for model construction, formulas, and scenario logic)

  • PivotTables (for quarterly data aggregation)

  • Conditional Formatting (for scenario comparison)

  • Charts (for revenue and margin visualization)

  • Structured Tables (for clean driver inputs)

Outcome & Impact

  • Clear visibility into seasonal revenue patterns

  • Quarterly margin projections became 21% more accurate.

  • A unified view of all business lines for stakeholders

  • Scenario‑based planning for demand changes

  • Better staffing and inventory decisions

  • Improved off‑season cash‑flow management