FP&A (Financial Planning & Analysis)

FP&A is forward-looking — budgeting, forecasting, variance analysis, and partnering with business leaders to model decisions before they happen. Accounting is backward-looking — recording and reporting what already occurred, in compliance with reporting standards. FP&A uses accounting's output as an input, but the job itself is about what happens next, not what already happened.

Start with the revenue plan since it drives most everything else, built bottom-up from sales/business unit input and sanity-checked top-down against market or historical growth trends. Then build cost budgets by department, tying variable costs to the revenue/volume assumptions and fixed costs to headcount and known commitments. Consolidate, review for consistency across departments, and iterate with leadership before finalizing.

A rolling forecast is continuously extended and updated — for example, always forecasting the next 12 months, refreshed monthly or quarterly — rather than being fixed once a year. Companies in fast-changing environments prefer it because a budget set 14 months before its final month often becomes stale, while a rolling forecast stays relevant and reduces the year-end scramble to explain a big variance to a plan that's long out of date.

I'd decompose the variance into its component drivers first — price, volume, timing, one-offs — attribute each piece to the department or driver that actually caused it, and present it that way rather than as one unexplained number. If departments disagree on attribution, I'd rely on the underlying data (which department's assumption changed) rather than opinion.

Bottom-up builds the forecast from granular inputs — individual sales reps' pipelines, unit-level cost drivers — and aggregates up; it's more accurate when good granular data exists but slower to produce. Top-down starts from a high-level assumption (market growth rate, historical trend) and allocates down; it's faster and useful for quick scenario work or when granular data isn't reliable. Many FP&A teams build bottom-up and sanity-check it top-down.

I'd model it with an explicit assumption about volume elasticity — price increases rarely leave volume unchanged — rather than just multiplying current volume by the new price. I'd build a base case with a modest volume decline assumption, plus upside/downside scenarios, and flag which assumption the conclusion is most sensitive to.

Driver-based forecasting ties each line item to the underlying operational metric that actually causes it to move, instead of just trending the dollar figure itself. For example, forecasting shipping expense off of units shipped and a per-unit shipping cost, rather than growing last year's shipping expense by a flat percentage — it's more accurate and makes the forecast easier to update when one driver changes.

I'd walk through the actual assumptions driving the difference rather than defending the number itself — often disagreement comes down to one specific input (their view on a deal closing, my view on historical conversion rates). If their information is better, I'd update the forecast; if it's a genuine judgment call, I'd present both views with the reasoning rather than picking one silently.

Incremental budgeting starts from last year's budget and adjusts it up or down — fast, but it can perpetuate spending that's no longer justified. Zero-based budgeting builds every line from zero, requiring justification for each expense regardless of what was spent before — more rigorous and better at catching unnecessary spend, but significantly more time-intensive to run.

Start with planned hires by role, start date, and fully-loaded cost (salary, benefits, employer taxes), and lay that against the org's growth or capacity needs. The plan flows into the P&L as compensation expense (timed to actual start dates, not year-start), and into cash flow planning, since hiring plans are often one of the biggest levers a company can pull to manage burn.

I lead with the headline takeaway and the 2-3 numbers that actually matter for the decision at hand, and keep supporting detail available but not front and center — leadership audiences want the 'so what,' not the full model. I'd rather be asked a clarifying question than bury the point in detail nobody asked for.

A sensitivity analysis shows how an output (like profit or valuation) changes as you vary one or more key assumptions, so you can see which inputs the conclusion is actually sensitive to. I'd use one whenever a forecast rests on an uncertain assumption — for example, showing how a revenue forecast changes across a range of conversion-rate assumptions, rather than presenting a single point estimate as if it were certain.

I'd use historical seasonal patterns (month-over-month or quarter-over-quarter shape) applied to a growth-adjusted base, rather than a flat run-rate extrapolation, and account for known anomalies in prior periods (a one-time promotion, a weather event) that shouldn't repeat. Comparing to the same period last year, not the prior month, is usually the more meaningful check.

A CFO typically cares most about company-wide profitability, cash flow, and capital efficiency — metrics tied to the overall health and runway of the business. A business unit leader typically cares more about metrics they can directly influence day-to-day — their unit's revenue growth, their team's cost efficiency, their specific KPIs — since that's what their decisions actually move.

A strong answer names the specific assumption that broke (a deal that didn't close on time, a cost that came in higher than modeled), how big the miss was, and — most importantly — what changed in your process afterward, like adding a sensitivity range around that assumption type going forward rather than presenting a single point estimate.

By showing up as someone who helps them make better decisions, not just someone who audits their spending after the fact — proactively flagging risks and opportunities in their numbers, being transparent about forecast assumptions, and giving them useful data rather than just enforcing budget compliance. Trust comes from being seen as a partner with real insight, not a gatekeeper.

Gross margin subtracts cost of goods sold from revenue. Contribution margin subtracts all variable costs (which may include some costs outside COGS, like variable selling costs) from revenue, leaving what's actually available to cover fixed costs and profit. Contribution margin is more useful for decisions like whether to accept an incremental order or discontinue a product, since it isolates the costs that actually change with that decision.