Strategy & Planning

How to run a scenario analysis for a strategic decision as DTC Brand Founders

Strategy & PlanningFor DTC Brand Founders2 apps10 steps~20 min to set up

You're running scenario analysis in a Google Sheet that has seventeen tabs, three conditional formatting rules you don't remember setting, and a CAC assumption that's six months stale. Every time you want to model 'what if we pull back Meta spend 30% and shift budget to Klaviyo flows,' you're manually updating numbers that should already live somewhere. Your Shopify revenue, your Plaid bank balance, your ad costs — none of it flows into the model automatically, so by the time the spreadsheet is updated, the inputs are already wrong. You end up presenting to your board or co-founder with a model you half-trust, built on a Saturday night.

Build this on Starch → See the apps used

Strategy & PlanningFor DTC Brand Founders2 apps10 steps~20 min to set up

Outcome

What you'll set up

A live baseline scenario that pulls your actual Stripe revenue and Plaid bank transactions on a schedule — so the starting point is always current, not last month's export

Multiple named scenarios side-by-side (e.g., 'cut paid 30%', 'new SKU launch', 'delay raise 6 months') each showing runway, burn rate, and break-even under their specific assumptions

A model you can update in plain English — adjust one assumption, re-run, and have numbers ready for a board call without rebuilding from scratch

The Starch recipe

Apps, data, and prompts

The combination of Starch apps, the data sources they pull from, and the prompts you use to drive them.

Apps used

Scenario Analysis Runway Analysis

Data sources & config

Starch connects directly to Stripe (synced on a schedule: charges, subscriptions, payouts) and Plaid (synced on a schedule: bank transactions categorized by type). Shopify can be connected from Starch's integration catalog — the agent queries it live when your scenario app needs order volume or refund data. No manual exports, no copy-paste from your bank portal.

Prompts to copy

Build me a scenario analysis comparing three futures: (1) current pace, (2) we cut Meta ad spend by 30% and hold headcount flat, (3) we launch the new fragrance SKU in June and it drives 20% revenue lift but adds $18k/month in COGS. Show runway, monthly burn, and break-even month for each. Pull the baseline from my Stripe and Plaid data.

Show me my current burn rate and 24-month cash projection broken down by fixed costs, ad spend, and COGS. Flag the month we drop below 6 months of runway at current pace.

Add a fourth scenario: we delay our Series A by one quarter and cut paid acquisition by 50% in that window. How does it change break-even versus scenario 2?

Run these in Starch → or paste them into your favorite agent

Walkthrough

Step-by-step

1 Connect Stripe in Starch — Starch syncs your charges, invoices, and subscription data on a schedule so your revenue baseline is always current.

2 Connect Plaid — Starch syncs your bank transactions on a schedule, categorized automatically, so burn is calculated from real outflows, not an accounting estimate.

3 Connect Shopify from Starch's integration catalog — the agent queries it live when your scenario model needs order volume, AOV, or refund rate as a revenue lever.

4 Open the Scenario Analysis app from the Starch App Store — your live Stripe and Plaid data pre-populate the baseline; you're not starting from a blank model.

5 Describe your first scenario in plain English: tell Starch what assumption you want to change (ad spend, headcount, COGS, revenue growth rate) and by how much.

6 Add your remaining scenarios the same way — each one is a natural-language adjustment on top of the same live baseline, not a new spreadsheet tab.

7 Open the Runway Analysis app alongside it — use it to validate that your baseline burn matches what you see in your bank feed before trusting the scenario outputs.

8 Review the side-by-side output: runway in months, monthly net burn, and break-even date for each scenario. Identify which assumption has the biggest swing on runway.

9 Iterate on any scenario by describing the change — 'update scenario 2 to assume Meta CPMs drop 15% in Q3' — without touching a formula.

10 Export or share the scenario summary ahead of your board call, investor check-in, or internal planning meeting.

See this running on Starch

Connect your tools, describe what you want, and the agent builds it. Closed beta is free.

Try it on Starch →

Worked example

Q2 2026 Planning: New SKU Launch vs. Paid Pullback

Sample numbers from a real run

Baseline monthly revenue (Stripe, trailing 90-day avg)	142,000
Baseline monthly burn (Plaid, trailing 90-day avg)	118,000
Baseline net burn	-24,000
Scenario A — cut Meta spend 30% ($14k/mo saved, assume 12% revenue impact)	-17,280
Scenario B — new SKU launch (add $18k COGS/mo, +20% revenue lift from month 3)	-34,400
Scenario C — delay raise 6 months, cut paid 50% (save $23k/mo, assume 18% revenue decline)	-19,240

Your current Stripe data shows $142k average monthly revenue over the trailing 90 days. Plaid shows $118k in outflows — split roughly $47k in paid acquisition, $38k in COGS, $21k in payroll, and $12k in ops. Net burn is $24k/month, giving you about 11 months of runway at current pace. Scenario A (pull Meta back 30%) saves $14k in spend but Starch models a 12% revenue decline based on your blended ROAS history, landing you at $17,280 net burn — better burn, but you need to validate that revenue assumption against your Klaviyo retention data. Scenario B (fragrance SKU launch in June) adds $18k in COGS per month starting month two, but if the 20% revenue lift hits by month three, you're at breakeven by October. Scenario C shows that delaying the raise and slashing paid in half actually extends runway by two months versus baseline — but only if you can hold revenue within 18% of current levels through organic and email. You go into the board call with three named, numbered scenarios instead of one 'base case' everyone knows is optimistic.

Measurement

How you'll know it's working

Months of runway at current burn (updated daily from Plaid + Stripe, not monthly from your bookkeeper)

Net burn rate by category: paid acquisition vs. COGS vs. fixed overhead — because not all burn is equal when you're deciding where to cut

Break-even month under each scenario — the specific date matters for raise timing decisions

CAC payback period as a scenario lever — how a shift in Meta or Klaviyo spend flows through to burn and break-even

Inventory COGS as % of revenue — especially sensitive if a new SKU or a stockout changes your unit economics mid-quarter

Comparison

What this replaces

The other ways teams handle this today, and how the Starch version compares.

Google Sheets (manual model)

Free and fully flexible, but your inputs are always stale — you're re-pulling Stripe exports and bank CSVs by hand every time something changes, which means the model is outdated before you finish building it.

Runway.com or Mosaic

Purpose-built financial modeling tools with cleaner UX than a spreadsheet, but they're priced for Series A+ companies, require a finance ops person to maintain, and won't pull in your Shopify order data or let you add a custom scenario in plain English.

Your bookkeeper's monthly close model

Accurate but arrives 3-4 weeks after the period ends — by the time you get it, you've already made the hiring or ad budget decision it was supposed to inform.

Notion or Coda formula databases

Good for lightweight tracking, but you'll still be copying numbers in manually, and the moment your model has three scenarios with different COGS assumptions, you're fighting the tool instead of making the decision.

On Starch RECOMMENDED

One platform — scenario planning, runway analysis all running on connected data. Setup in plain English; numbers stay current via scheduled syncs and live agent queries.

Try it on Starch →

FAQ

Frequently asked questions

Does Starch actually pull my Shopify revenue, or is this just bank transactions?

Both. Starch syncs your Stripe data on a schedule (charges, subscriptions, payouts) for revenue, and your Plaid bank transactions on a schedule for cash outflows. If you want Shopify order-level data — AOV by channel, refund volume, units per order — you connect Shopify from Starch's integration catalog and the agent queries it live when your scenario model needs it. You can build a scenario that uses Shopify order volume as the revenue driver instead of or alongside Stripe if that better reflects your business.

What if my biggest cost is Meta ad spend and it changes week to week? Will the baseline be accurate?

Plaid captures your actual bank outflows, including ad platform charges as they hit your account. So if Meta charged you $11k last week and $14k this week, that variation is in the data. For forward-looking scenarios, you tell Starch the assumption you want to test ('assume Meta spend drops 30% starting June 1') and the model adjusts from the live baseline — you're not locked into a static monthly average.

Can I model inventory and COGS changes, not just headcount and ad spend?

Yes. You describe the assumption in plain English — 'add $18k/month in COGS starting in month two because we're launching a new SKU' or 'assume reorder costs increase 15% due to freight rates' — and Starch builds it into that scenario. Starch won't automatically know your per-unit COGS from Shopify unless you tell it, but you can wire that in from your connected data or just input the delta directly.

Is this the same as the Runway Analysis app or different?

Different jobs. Runway Analysis is your always-on live dashboard — it shows you today's burn, current runway, and a 24-month projection based on what's actually happening. Scenario Analysis is for decisions — you use it when you're about to do something (launch a SKU, cut paid, delay a raise) and you want to see the financial consequences before you commit. Most DTC founders use both: Runway Analysis as the daily check, Scenario Analysis when something big is on the table.

Does Starch store my bank transaction data? Is it secure?

Starch syncs your Plaid transaction data and stores it in Starch's database to power the live dashboard and scenario baseline. Starch is not SOC 2 Type II certified today — that's worth knowing if your company has specific compliance requirements. For most early-stage DTC founders, the tradeoff is worth it: you get a live, accurate model instead of a stale spreadsheet. If SOC 2 is a hard requirement for your business, that's an honest reason to wait.

How long does it take to set up the first scenario?

If Stripe and Plaid are connected, the Scenario Analysis app pre-populates a baseline from your actual data. Describing your first scenario and getting output takes minutes, not hours. You're not configuring a model from scratch — you're adjusting assumptions on top of real numbers that are already there.