Scenario and Sensitivity Analysis for Cash Flow

Sensitivity: which lever moves cash the most

A sensitivity (or 'what-if') table holds the base forecast steady, then flexes one assumption — say, average collection days — across a range (40, 50, 60, 70 days). For each value, the model recomputes ending cash on a chosen date.

The output is a tornado chart or table that ranks assumptions by impact. For a typical service business, DSO, average deal size, and payroll headcount usually top the list. Marketing spend often ranks lower than owners assume.

Scenario: coherent futures, not isolated knobs

A scenario combines several assumption changes that would plausibly travel together. A worst case for an agency might pair a 20% drop in new bookings with a 20-day extension of DSO and zero new hires. A best case might pair 30% booking growth with on-time collections and one new hire.

Three scenarios is the floor. Banks and lenders increasingly ask for them when underwriting. Even without external pressure, owners who run scenarios make better hiring and pricing decisions because they've already pre-committed to triggers ('if Q3 bookings come in below \$X, we delay the new hire').

Making scenarios useful, not theatrical

The trap with scenario analysis is building three carefully labeled cases — base, upside, downside — that all assume more or less the same thing. To avoid that, define scenarios around specific events with real probability mass: 'top customer churns in Q3,' 'new hire ramps three months late,' 'recession compresses pipeline by 30%.' Each scenario should change two or three drivers simultaneously, the way real shocks actually arrive.

Sensitivity analysis is the complement: hold every driver fixed except one and chart the cash impact of moving it ±20%. The output is a tornado chart showing which inputs your runway is actually sensitive to. Often the answer is surprising — many founders learn that DSO and utilization swing cash more than headline revenue growth does, which redirects attention to operations rather than sales.

Document the assumptions for each scenario in the model itself, not in a separate deck. A year from now, when you re-run the analysis, you need to be able to see exactly what 'downside' meant the last time so you can compare like with like.

Pre-commit to actions tied to scenarios. 'If revenue falls 20% by end of Q2, we cut the contractor budget and freeze new hires' is much more useful than vague 'we'll respond if conditions deteriorate.' Pre-commitments turn the scenario analysis into an operational playbook and remove the emotional difficulty of cutting in the moment, when people will be motivated to wait one more quarter.