AI Use Cases
13 min read
July 2026
How I Built a Bottoms-Up Budget Model in 40 Minutes Using Claude
A task that used to take two to three days of stakeholder back-and-forth, multiple spreadsheet versions, and a graveyard of revision emails — done in a single focused session. Here is exactly how I did it, the prompts I used, the output structure I got, and what it means for every marketer still building budgets the old way.
A
Anirudh Vidya
Marketing Strategist · Storyteller & Builder
Every marketing team has a version of this problem: the budget planning season arrives, someone needs a model by Friday, and you spend the next three days doing a job that is 20% thinking and 80% formatting, cross-referencing, and explaining the same logic in four different versions of the same spreadsheet.
I have done this cycle more times than I can count. You gather inputs from five stakeholders, reconcile conflicting assumptions, rebuild the model three times because the structure keeps changing, and eventually deliver something that looks polished but took far longer than the insight it contains actually required.
Last quarter I tried something different. I sat down with Claude, described a budget planning brief in plain language the same way I would explain it to a smart colleague, and iterated in conversation until I had a fully structured bottoms-up budget model — channel allocation, CAC targets, pipeline waterfall, and a narrative layer explaining every number. The whole session took 40 minutes.
This is the complete account of how that worked, what I actually said, what came back, and the reusable prompt structure anyone can take and apply to their own budget cycle.
The Old Way
2–3 days
Stakeholder inputs, version conflicts, revision loops, formatting time
The New Way
40 min
One focused session, structured output, narrative ready to present
First: What a Bottoms-Up Budget Model Actually Is
A bottoms-up budget model works from outcomes backward to spend — rather than starting with a budget number and dividing it up. You start with the pipeline target, work out what conversion rates are required at each stage, calculate the lead volume you need, and then back-calculate what each channel needs to spend to generate that volume at an acceptable cost.
Done properly, it produces a model where every budget line is defensible from first principles: you are not allocating 30% to paid search because that is what you did last year, you are allocating it because 30% generates X leads at Y CAC which contributes Z to the pipeline target. That is the model that wins budget conversations.
It is also the model that takes the longest to build manually — because you are simultaneously managing the maths across multiple channels, the assumptions for each one, and the narrative that explains why the numbers are what they are. Three separate cognitive tasks, usually happening in one spreadsheet at the same time.
“The thinking in a bottoms-up model takes two hours. The formatting, cross-referencing, and version management takes two days. Claude eliminates the second part without touching the first.”
What the Old Process Actually Looked Like
Before
The old budget model workflow — what two to three days actually looked like
Day 1
Inputs & alignment
Morning: Email to five stakeholders asking for pipeline targets, conversion rate assumptions, and channel performance from last quarter. Afternoon: Chasing the two who have not replied. End of day: Conflicting assumptions from Sales and Finance that need to be reconciled before the model can be built.
Day 2
Building & breaking
Morning: Building version one of the model from reconciled inputs. Midday: One stakeholder sends updated assumptions that change three columns. Rebuild. Afternoon: The model structure changes because Finance wants a different breakdown. Rebuild again. End of day: Version three is close but the pipeline waterfall is not connecting correctly.
Day 3
Polishing & presenting
Morning: Fixing the waterfall, formatting the model, adding colour coding. Midday: Writing the narrative summary that explains what the model says and why. Afternoon: Presentation prep. End of day: Delivered — but with no time left to think about whether the strategy behind the numbers is actually right.
Hidden
The real cost
The thing nobody accounts for: By the time the model is done, you are too fatigued from the process to challenge your own assumptions. The model gets presented as-is because there is no time to think about whether the strategy is sound — only whether the formatting is clean.
The 40-Minute Session — Step by Step
Here is exactly how the session ran. Every prompt is real. Every output structure is what came back. I have kept the detail high because the replicable part of this is not the concept — it is the specific way you structure the conversation.
The verbal brief — plain language, no formatting
The first message was exactly what I would say to a smart colleague in a five-minute briefing. No structure. No formatting. Just the situation, the targets, and the constraints — spoken naturally. The key insight here: Claude does not need a perfectly formatted brief to produce a perfectly structured output. That translation work is part of what it does.
I need to build a bottoms-up marketing budget model for the next quarter. Our pipeline target is $2M in new business. We currently convert MQLs to SQLs at about 18%, SQLs to opportunities at 40%, and opportunities to closed-won at 25%. Average deal size is $18,000. We run four main channels — paid search, intent-driven marketplaces like G2 and Gartner, content syndication, and ABM. Total budget envelope is around $180,000. I need to know how many leads each channel needs to generate, what CAC looks like per channel, and how the spend should be allocated to hit the pipeline target. Can you build this out as a structured model and explain the logic behind every number?
What came back: a full bottoms-up calculation working backwards from the $2M target through each conversion stage to arrive at total MQL volume needed (approximately 2,470), broken down by channel with allocation logic and CAC estimates per channel. Produced in under 90 seconds.
Challenge the assumptions — stress-test before you commit
Once the base model was back, I did not accept it. I challenged the assumptions in the same way a CFO would — asking what happens if conversion rates are worse than expected, which channels are most exposed to performance variance, and what a conservative version of the model looks like versus an optimistic one. This is the part of budget building that always gets cut when time is short. With 32 minutes still on the clock, it was the first thing I did.
Now show me two scenarios: a conservative version where MQL-to-SQL conversion drops to 14% and a stretch version where it hits 22%. For each scenario, show how the required lead volume changes by channel and flag which channels become uneconomical first if we miss conversion targets.
What came back: a three-column scenario model (base, conservative, stretch) with channel-level sensitivity analysis showing that content syndication becomes uneconomical at below 15% MQL-to-SQL conversion, while paid search holds up longest due to lower per-lead cost. That insight — which would have taken another half day to produce manually — took three minutes.
Build the pipeline waterfall — stage by stage
The pipeline waterfall is the part of a budget model that takes longest to build manually — because you are tracking volume, conversion, and value at every stage simultaneously, across multiple channels, with the numbers needing to reconcile both horizontally (across channels) and vertically (down the funnel). I asked Claude to produce this as a structured table with clear labels.
Now produce the pipeline waterfall as a clean table. Rows should be: MQLs Generated, MQL-to-SQL Conversion, SQLs, SQL-to-Opportunity Conversion, Opportunities, Opportunity-to-Close Rate, Closed Won Deals, Average Deal Size, Revenue Contribution. Columns for each channel plus a total column. Use the base scenario numbers.
What came back: a fully reconciled waterfall table where every number connects. The total column matched the $2M pipeline target exactly. In a manually built model, getting this reconciliation right — without formula errors — is where most of the Day 2 time goes.
Add the narrative layer — the part that wins the room
Numbers without narrative lose budget conversations. I asked Claude to produce the explanatory layer — not a summary of what the model says, but the reasoning behind each allocation decision in the language a CFO or CMO needs to hear it. This is the Context → Trade-off → Bet structure from the budget article — applied directly to these specific numbers.
Now write a brief narrative for each channel allocation — two to three sentences per channel explaining why it received that percentage, what assumption it depends on, and what would cause us to reallocate away from it. Write it in the tone of a budget presentation to a finance leader who understands business outcomes but not demand gen mechanics.
What came back: four channel narratives, each anchored to a business logic rather than a marketing logic. The paid search narrative talked about short payback period and high conversion certainty. The ABM narrative named the Sales alignment dependency explicitly. The content syndication narrative flagged the lead quality risk at lower conversion rates. All ready to paste into a deck.
Final review — four minutes to check what a human needs to check
With four minutes left, I read the full output once — not to verify the maths (Claude had that right throughout) but to check three things only a human can check: whether the assumptions match what I actually know about our channels from experience, whether the narrative sounds like me and not like a template, and whether the trade-offs named are the real trade-offs or sanitised versions of them. Two small adjustments, both narrative. The model numbers held throughout.
What the Model Output Actually Looked Like
01
Paid Search
High-intent, high-certainty
$620
$580,000
02
Intent Marketplaces
G2, Gartner, Capterra
$980
$700,000
03
ABM Programmes
Sales-aligned accounts
$1,450
$400,000
04
Content Syndication
Mid-funnel demand capture
$480
$320,000
Σ
Total
100%
$780 blended
$2,000,000
Session Duration
40 min
From blank page to presentation-ready model with narrative
Prompt Iterations
6
Brief → base model → scenarios → waterfall → narrative → final check
Time Saved
~2 days
Versus the standard stakeholder input and rebuild cycle
The Reusable Prompt Structure — Take This and Use It
Prompt Template
The 4-prompt sequence that builds a complete bottoms-up budget model
Prompt 1 — The Verbal Brief
I need a bottoms-up marketing budget model for [time period]. Our pipeline target is [£/$X]. Current conversion rates: MQL-to-SQL [X%], SQL-to-opportunity [X%], opportunity-to-close [X%]. Average deal size [£/$X]. We run [N] channels: [list them]. Total budget envelope is [£/$X]. Build the model showing leads required per channel, CAC per channel, and budget allocation. Explain the logic behind every number.
Speak naturally. The more context you give, the better the output — channel performance history, known constraints, stakeholder sensitivities.
Prompt 2 — Scenario Stress Test
Show me [conservative/base/stretch] scenarios. Conservative: [conversion rate] drops to [X%]. Stretch: it hits [X%]. For each scenario, show how required lead volume changes by channel and flag which channels become uneconomical first.
Always run this before you present. The conservative scenario is the one you need to have an answer for in the room.
Prompt 3 — Pipeline Waterfall
Produce the pipeline waterfall as a structured table. Rows: MQLs, MQL-to-SQL rate, SQLs, SQL-to-opp rate, Opportunities, Close rate, Closed Won, ADS, Revenue. Columns: one per channel plus total. Use the base scenario.
Check that the total column reconciles to your pipeline target. If it does not, ask Claude to show its working on any row that looks off.
Prompt 4 — The Narrative Layer
Write 2–3 sentences per channel explaining: why it received this allocation, what assumption it depends on, and what would cause reallocation away from it. Tone: finance leader who understands business outcomes, not demand gen mechanics.
Edit this output for your own voice before presenting. The logic will be right. The phrasing will need to sound like you.
What Claude Gets Right — and What You Still Own
This is not a “AI replaces the marketer” story. It is a “AI eliminates the work that was never the valuable part” story. There is an important distinction, and it matters for how you actually use this in practice.
What Claude handles well
The maths and structure. Conversion rate calculations, CAC backwards-engineering, pipeline waterfall reconciliation — it does not make arithmetic errors and does not lose track of assumptions across columns.
The scenario generation. Producing three versions of a model with different assumptions is pure computation. It is fast and error-free.
The first draft of the narrative. It knows how to explain budget logic in business language. The first draft is usually 80% of the way there.
Translating verbal briefs into structured models. You do not need to pre-format your thinking. Speaking naturally works.
What only you can do
Validate the assumptions. Claude works with what you give it. If your conversion rates are outdated or your channel benchmarks are off, the model will be structurally correct and directionally wrong. You own the inputs.
Apply channel-specific knowledge. Knowing that your G2 leads convert at a different rate than the benchmark because of how your SDR team works them — that is tacit knowledge Claude does not have.
Make the strategic call. Whether to concentrate spend in one channel or diversify, whether to take the conservative scenario to the board or the stretch — those are judgement calls that require context Claude cannot have.
Own the room. You present this. You defend it. The model is a tool; the conviction is yours.
The honest version of what happened: The 40 minutes produced a model I was proud of and could defend. But it also freed up the time I would have spent formatting to actually think about whether the strategy behind the numbers was right — and I made two meaningful strategic changes as a result of having that time. The real value was not the 40 minutes. It was the two hours of thinking I got back.
Why This Matters Beyond the Budget Model
The budget model is one use case. The principle behind it — verbally brief Claude the way you would brief a smart colleague, then iterate until the output matches what you need — applies across almost every structured marketing deliverable: campaign briefs, channel audits, competitive analyses, board deck narratives, pipeline reviews.
The pattern is the same in every case. The thinking is yours. The structure, the calculations, the scenario modelling, the first-draft narrative — Claude handles that layer. What you get back is time to think about whether the strategy is right, rather than time spent making the spreadsheet look like a spreadsheet.
That is a meaningful shift. Not because it is faster — though it is dramatically faster — but because the cognitive bandwidth it frees up is exactly the bandwidth that produces better strategic decisions.
This Use Case Has Been Repurposed As
How I use this content across formats
Available formats
📝 This Article
💼 LinkedIn Post
🎙️ Podcast Episode 01
Your Turn
Want to walk through this for your own budget?
Book a 15-minute call. Bring your pipeline target, your channel mix, and your conversion rate assumptions — and I will show you how to run the same session for your specific context in real time.
Bottoms-Up Model
CAC
Claude AI
Demand Gen
Marketing Budget
Marketing Finance
Pipeline Planning
Productivity
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