Devin Hartcastle
Start with the search term, not the offer
In nearly every Google Ads setup for a prop firm, the same mismatch appears: a broad keyword sends several kinds of searchers to one challenge page. Someone comparing rules, someone looking for a discount and someone ready to buy may all search for a prop firm. Their intent, and the economics of acquiring them, are different.
That matters because a challenge purchase is not the same as a funded trader, and a click is not evidence of either. The account needs to show which queries bring buyers who pass verification, begin trading and remain active. If reporting stops at checkout, a low-cost campaign can look healthy while buying mostly one-off, low-value orders.
Google Ads can capture existing demand for evaluation programmes. It is less suited to creating demand among people who have not heard of the model. Treat search as a channel for matching an offer to a need already expressed in a query, then test whether the resulting cohort creates enough contribution to justify the cost.
Separate intent before setting bids
Build keyword groups around what the searcher is trying to do. Keep these groups separate in campaigns or tightly organised ad groups so that budgets, ads and landing pages can reflect the intent.
- Ready to buy: queries such as "buy prop firm challenge" or "instant funding challenge". These can support a direct offer page with price, rules, drawdown limits and payment details visible.
- Comparing providers: searches for "best prop firm" or a specific rule, platform or account type. Give these visitors clear comparison criteria and evidence about the programme, not a generic discount message.
- Looking for a promotion: "prop firm discount code" or a brand plus "coupon". State the offer terms, expiry and eligible account types on the landing page. Measure whether discounts attract incremental orders or simply reduce margin on purchases that would have happened anyway.
- Learning the model: searches about how funded trading works or what an evaluation is. These users may need an educational page and a later follow-up, rather than an immediate checkout push.
Use search-term reports to find queries that do not fit the group. Add negatives for irrelevant meanings of "funded", job searches, unrelated investment products and free downloads where they are not commercially relevant. Review negatives carefully. Excluding a broad term such as "free" could also block a valuable search for a free trial or rule explanation.
Model typical costs by intent
The ranges below are planning benchmarks for search campaigns, not guaranteed prices or audited results. Actual costs vary with country, language, competition, season, account history and landing-page quality. The implied first-purchase cost is a simple CPC divided by click-to-purchase conversion calculation, so it is deliberately broad.
| Search intent | Typical CPC range | Typical click-to-purchase range | Implied first-purchase cost range |
|---|---|---|---|
| Brand terms | US$0.50-US$2 | 10%-25% | US$2-US$20 |
| High-intent challenge terms | US$2-US$8 | 2%-6% | US$33-US$400 |
| Competitor and comparison terms | US$2-US$10 | 1%-4% | US$50-US$1,000 |
| General information terms | US$0.75-US$4 | 0.2%-1% | US$75-US$2,000 |
These are broad directional ranges, not targets to copy. Brand traffic is often cheaper and converts more readily, but it may capture demand created by affiliates, organic search or existing awareness.
Do not treat every branded order as incremental revenue. Competitor and general information terms can have a high purchase cost, so set a capped test budget and assess them against downstream quality, not click-through rate.
Count the value after checkout
A challenge buyer may purchase another evaluation, reach a funded stage, generate a payout or stop trading. The first order is only one part of the cohort economics.
Calculate the amount available to acquire a customer after payment fees, refunds, discounts, support costs and expected payout obligations. Use a consistent time window, such as 60 or 90 days, and compare like-for-like countries and offer types.
Useful measures to keep alongside first-purchase CAC include:
- Refund and chargeback rate by campaign and offer.
- Share of challenge buyers who start the evaluation and reach each stage.
- Repeat purchase rate, with discounted rebuys separated from full-price orders.
- Cost per trader who becomes funded, and the cohort's net contribution after payouts and operating costs.
- Time from first click to purchase, plus the share of sales assisted by a later visit or another channel.
Attribution can over-credit paid search when a buyer first discovers the firm elsewhere, then searches its name before buying. Compare platform-reported conversions with analytics and order records. Use a holdout or a careful brand-spend test where volume permits. Avoid switching off brand campaigns on the basis of a few days of data: daily conversion counts can be small, and auction conditions change.
For a rough planning check, if a challenge order contributes US$40 before acquisition costs, a US$100 first-purchase CAC cannot pay back on that order alone. Repeat purchases or later funded-trader contribution might change the cohort value, but include them only when observed data supports the estimate. Keep assumptions visible rather than hiding them in a blended return figure.
Set up the campaign and measurement
Before increasing spend, make sure the campaign can distinguish a paid challenge from a registration, a payment failure and a refund. Import offline events from the trading or customer system where possible. Keep personal data handling and consent aligned with the markets being targeted. Check Google Ads policies and local requirements for financial promotions before launch; an eligible-looking ad or landing page is not a substitute for that review.
- Choose a narrow test: start with one or two countries, one language and a clearly defined challenge offer. Check local currency, payment methods and page localisation.
- Map keywords to pages: send purchase-intent queries to the relevant challenge page. Give rule-comparison queries enough information to assess drawdown, targets, time limits, resets and restrictions.
- Write specific ads: describe the actual programme and state material conditions. Avoid implying guaranteed profits, guaranteed funding or a typical outcome that cannot be substantiated.
- Install the full event chain: record landing-page visits, checkout starts, paid orders, refunds and later programme milestones. Deduplicate purchase events and check that order values and currencies pass correctly.
- Set a test budget: choose a maximum spend based on the CAC the business can tolerate, not on an arbitrary daily budget. Allow enough time for the normal purchase lag, but pause quickly for policy issues or irrelevant queries.
- Review search terms and cohorts: check query quality frequently at launch. After enough time has passed for the chosen evaluation window, compare purchase cost and downstream behaviour by intent, country and offer.
- Scale in controlled steps: raise budgets on groups with stable conversion tracking and acceptable cohort economics. Retest after material changes to price, rules, landing pages or promotions.
Campaign management also means keeping offer details consistent across ads, landing pages, checkout and support. If a promotion applies only to a particular account size or excludes a reset, say so where the buyer makes the decision. A lower CPC will not compensate for confusion that increases refunds or support load.
Decide what deserves more budget
Scale the intent group that brings the strongest contribution after the costs that matter to the firm. That may not be the group with the highest purchase conversion rate. A branded campaign can convert well but add little incremental demand; an education campaign can convert slowly yet introduce valuable traders; a promotion campaign can lift orders while cutting margin. Compare each against its own purpose.
Keep the operational cadence simple: review search terms and tracking regularly, assess conversion and refund patterns weekly, and make cohort-level budget decisions only after enough time has passed for the relevant outcomes. Document changes to bids, exclusions, landing pages and offers. That makes it easier to tell whether a change improved results or merely coincided with a shift in demand.
For teams with limited capacity, an AI marketing agent for prop firms can help organise recurring marketing work, but it does not replace clean conversion data or commercial judgement. The campaign still needs a clear offer, accurate terms and a person accountable for the economics.
This advice does not apply where the firm cannot legally advertise the offer in its target market, or where its checkout and trader systems cannot reliably record purchases and downstream outcomes; resolve those constraints before buying traffic.
Frequently Asked Questions
What is a typical Google Ads cost per challenge purchase?
It varies by country, keyword intent, offer and landing-page conversion. Broad planning ranges run from a few dozen US dollars for some high-intent searches to several hundred dollars or more for competitive and general queries. Calculate your own range from CPC and verified paid-order conversion, then compare it with cohort contribution.
Should a prop firm bid on competitor keywords?
Competitor searches can reach people comparing providers, but CPCs and purchase costs may be high. Test them in a separate, capped campaign with accurate ads and a page that clearly explains your own rules. Judge the test by refunds and downstream trader quality as well as initial orders.
How should prop firms measure Google Ads conversions?
Track paid challenge orders as the primary purchase event, and exclude failed payments, duplicate events and refunded orders from revenue reporting where possible. Connect order data to evaluation and funded-stage milestones so acquisition cost can be compared with later cohort value. Check platform attribution against analytics and transaction records.
