Free toolTOFU
Ad budget calculator
The short answer
This ad budget calculator works backwards from a monthly revenue goal. Tell it how much of that revenue should come from paid media, your average order value, site conversion rate and expected cost per click — and it tells you the orders, clicks and monthly budget required, plus the ROAS that plan implies.
Monthly ad budget needed
₹3,71,212
≈ ₹12,374 per day
Paid orders required
477
Clicks required
26,515
Implied ROAS
2.83x
Check this against your break-even ROAS.
Calculations run in your browser. Nothing you type is stored or sent anywhere. Outputs are planning estimates, not guarantees.
How it works
- Paid revenue target = revenue goal × share from paid media
- Orders needed = paid revenue ÷ AOV
- Clicks needed = orders ÷ conversion rate
- Budget = clicks × CPC
- Implied ROAS = paid revenue ÷ budget
Worked example
A goal of ₹15 lakh a month with 70% from paid media, a ₹2,200 AOV, 1.8% conversion rate and ₹14 CPC needs about 477 paid orders, 26,500 clicks and a budget of roughly ₹3.7 lakh — an implied ROAS of about 2.8x. If your break-even ROAS is above that, the goal isn't profitable at these assumptions.
Planning caveats
- CPCs usually rise as you scale — cheaper audiences run out first
- Conversion rates vary by channel; blended rates hide the difference
- Festive season changes both CPC and conversion rate
- New campaigns need a learning period before hitting steady-state efficiency
Keep a 15–25% buffer on budget plans.
Budget planning example
| Input | Value |
|---|---|
| Monthly revenue goal from ads | ₹20,00,000 |
| Average order value | ₹2,000 |
| Orders needed | 1,000 |
| Target cost per order | ₹500 |
| Media budget | ₹5,00,000 |
| Implied ROAS | 4x |
| GST on ad spend (18%) | ₹90,000 |
If your break-even ROAS is 2.5x, a 4x target leaves room for profit; if break-even is 4x, the plan needs better margins or conversion.
How to set a test budget
A test budget should be big enough to produce a meaningful number of conversions in a few weeks. Work backwards: if you expect a cost per conversion of ₹500 and want about 50 conversions to judge a campaign, plan roughly ₹25,000 for that test — more if you're testing several concepts or audiences at once.
Budget by channel: rough roles
| Channel | Role in a starting budget |
|---|---|
| Google Search | Capture existing demand; often the first rupee for urgent needs |
| Meta | Create demand and retarget; needs creative volume |
| YouTube | Consideration and reach; judge on lift, not last-click |
| Marketplaces | Category demand; judge on TACoS |
A worked example
A D2C brand wants 400 new customers a month at a target CAC of ₹700. That implies a monthly acquisition budget of around ₹2.8 lakh. If testing shows Meta delivers customers at ₹650 and Google at ₹800, the next month's budget leans toward Meta while Google is kept for brand and high-intent searches.
Frequently asked questions
What if the implied ROAS is below my break-even?
Something has to change: higher conversion rate, higher AOV, lower CPC or a more modest goal. Use the break-even ROAS calculator to check the line.
What CPC should I assume?
Use your own historical blended CPC. If you're new, start conservatively and update after the first month.
Does this work for lead generation?
Swap AOV for revenue per lead (deal value × lead-to-sale rate) and conversion rate for your landing page lead rate.
How much should a small business spend on ads?
Enough to generate meaningful results on one or two channels — work backwards from the cost per customer you can afford and the results you need.
Should budgets be equal every month?
No. Budgets should follow seasonality, stock and performance — higher in peak demand periods, lower when returns fall.
What if the calculated budget is more than we can afford?
Reduce the scope — fewer channels, one region or one product — rather than spreading a small budget thinly.
How often should budgets be reviewed?
Weekly for allocation between campaigns, monthly for overall budget.
Is the calculator's result a recommendation?
It's a planning estimate based on your inputs — real results depend on testing.
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