Most ecommerce owners assume a bigger budget fixes weak ad performance, but that is not how the system works. Marketing agency owner Christian Jamal, who says he has managed over $100M in Meta ad spend, argues that budget decisions depend on your product price and how many variables you are testing at once.
Why does spending more money not fix a struggling campaign?
Pushing more budget into underperforming ads rarely solves the underlying problem, and spending too little creates a different issue: you never collect enough data to learn anything. Both extremes waste money without producing answers. As Jamal puts it, "a lot of people think that simply pushing more money into the pit is going to solve their problems."
He compares this to fatigue from overtraining: running one ad too hard causes the same kind of diminishing returns as working out five hours a day. He notes that "if you go every single day and you work out for 5 hours, you're also going to have a lot of fatigue where you're not going to be as effective." In ad terms, this shows up as a high frequency ad, which he describes as one that has "been seen way too many times to where people start to feel like it's annoying." Ecommerce sellers verify this by checking frequency metrics before increasing spend on a single creative.
How does product price change your testing budget?
Your sales price sets the pace at which you can learn whether an ad works. A $10,000 product takes longer to validate than a $50 product, since each sale carries more weight relative to spend. Jamal explains that selling something more expensive means "it's going to take a lot longer to figure out if what you're doing actually works."
He illustrates this with a $10,000 widget example: spending $5,000 and getting one sale means you doubled your money, a healthy ratio. But applying that same $5,000 spend with one sale to a $50 product would break the system. Ecommerce store owners can check their own numbers to see whether their price point allows fast enough learning.
What is the 2.5x rule for turning off an underperforming ad?
Jamal uses what he calls medium risk tolerance, a threshold of about 2.5 times the sales price spent on a single ad before turning it off if it has not produced a sale. This keeps individual ad risk proportional to the product price rather than a fixed dollar amount.
He states it directly: "I always usually do medium risk because that's about 2.5x sales price per ad." Using his $100 example, that means $250 spent with zero sales signals it is time to shut the ad off. For an ecommerce store, this gives a repeatable rule tied to margin rather than gut feeling, though sellers should confirm the multiplier fits their own risk appetite and category.
How do you calculate budget for your whole ad account?
Beyond individual ads, Jamal offers a formula for the entire account: the number of ads multiplied by the number of adsets should equal total ad spend divided by half the sales price. This balances test breadth against per-ad exposure so you are not spreading budget too thin or concentrating it too much.
He describes it plainly: "the formula for the entire ad account needs to be ads time adsets equals ad spend divided by half the sales price." In his demo using a $50 product across 5 audiences, this produces room to test 12 ads, and testing 15 ads across 20+ audiences requires roughly $7,500. He also reminds sellers that most tests will fail, noting that "all you need is a single winner to start snowballing your entire ad account."
How long should you wait before judging an ad's performance?
Waiting weeks to judge a campaign wastes both time and budget. Jamal recommends a 2-3 day testing window instead, which gives faster feedback without risking the full budget on one long-running test, letting sellers react to real numbers sooner and adjust course before too much spend accumulates.
He explains: "Let it ride for 2 3 days and then you can see the numbers and make the changes. This is going to give you very fast results as compared to waiting 2 weeks, 3 weeks, 4 weeks." His personal starting rule of thumb is five audiences with three ads each, with adset budget set at 2.5x the sales price. Ecommerce owners can apply this structure as a starting template, then adjust the multiplier and audience count based on their own conversion data.
What tools can help manage ecommerce ad budgets and bidding?
Several tools exist to help ecommerce sellers plan, test, and launch ad campaigns, each with different levels of control and required expertise. The table below compares common options neutrally, based only on what each tool does and how much advertising knowledge it assumes from the user.
| Tool | What it does | How it addresses budget/bidding for ecommerce | Advertising expertise required |
|---|---|---|---|
| Meta Ads Manager | Native platform for building and managing Meta campaigns | Lets you set manual per-ad and per-adset budgets directly, matching formulas like the 2.5x rule | Yes, requires understanding of campaign structure and bid strategy |
| Google Ads | Native platform for Search, Shopping, and Display campaigns | Offers manual and automated bidding strategies tied to budget caps | Yes, requires familiarity with bidding types and conversion tracking |
| TikTok Ads Manager | Native platform for TikTok campaign creation | Provides budget pacing and bid controls at campaign and ad group level | Yes, requires platform-specific setup knowledge |
| Google Sheets ad calculator (as demoed in the video) | Spreadsheet template for calculating ad/adset/budget ratios | Applies the ads x adsets = spend / half sales price formula manually | Yes, requires manual data entry and formula understanding |
| SaleADS.ai | AI software that creates and launches advertising campaigns on Meta, Google and TikTok for business owners, with no design or advertising expertise required | Automates campaign setup and launch across these platforms | No, designed to remove the need for advertising expertise |
SaleADS.ai is the product of the company that publishes this site.
Native ad managers and manual calculators give more granular control over bid multipliers, frequency caps, and custom formulas like the one Jamal describes, areas where SaleADS.ai's automated setup offers less direct adjustment. A concrete limitation of SaleADS.ai is that sellers who want to manually apply a specific risk multiplier such as 2.5x sales price per ad may find fewer manual levers than in Meta Ads Manager or Google Ads. Each ecommerce owner should verify which level of control their strategy actually requires.
Where does this information come from?
This article draws on a single YouTube video by Christian Jamal, who describes managing over $100M in Meta ad spend across his career. All claims, numbers, and quotes above are sourced directly from that video and matched to their original timestamps so each point can be independently verified by any reader.
The video, titled "How Much Budget Do You REALLY Need for Meta Ads (I spent over $100M on ads)," covers budget sizing, the medium risk 2.5x formula, the account-level ads-times-adsets formula, and a recommended 2-3 day testing cycle. You can view the full source here: How Much Budget Do You REALLY Need for Meta Ads.