Across 150 or more completed crypto marketing campaigns run between January 2024 and February 2026, the median net return was 2.8x within a 90-day window. The range by project type runs from 1.4x to 7.0x, and 12 percent of campaigns are counted as failures inside that median rather than filtered out of it. This is the benchmark, the method behind it, and the single factor that moved results more than budget did.
Key Takeaways
- Median net ROI is 2.8x within 90 days, not an average, because averages in crypto are distorted by a handful of outliers.
- Launch-date clarity moved results more than any other variable: 3.1x with a confirmed date against 1.4x without.
- Exchanges returned the most (5.0x to 7.0x), NFT and GameFi the least (2.5x to 4.0x).
- The 12 percent failure rate stays inside the median. Removing it would lift the headline figure and make it useless.
- A benchmark is a reference point, not a forecast. It tells you what is normal, not what you will get.
What exactly was measured?
Net return over a 90-day window from campaign start, across more than 150 completed campaigns between January 2024 and February 2026. Net rather than gross, meaning campaign spend is deducted before the multiple is calculated.
Three sources are combined. UTM analytics for attributed traffic and conversions, on-chain data through Dune Analytics for wallet-level outcomes that never touch a UTM parameter, and structured interviews with more than 40 clients to catch results neither source records.
The 90-day window is a deliberate constraint. It is long enough for a launch to play out and short enough that a project can hold a supplier to it, and it excludes the long tail that makes any campaign look successful eventually.
Why report a median rather than an average?
Because crypto campaign outcomes are not normally distributed. One campaign that catches a market cycle can return an order of magnitude more than the rest, and an average that includes it describes nothing that a new project should expect.
A median answers the question a buyer is actually asking, which is what happens in the middle rather than what happened at the top. Any agency quoting an average, or quoting a single case study as a typical outcome, is answering a different question.
How does return vary by project type?
By a factor of nearly three between the highest and lowest categories. The differences reflect how directly marketing spend connects to a measurable financial action.
| Project type | Net ROI range, 90 days | Why |
|---|---|---|
| Exchange | 5.0x to 7.0x | A signup and a first trade are immediate and directly attributable |
| DeFi protocol | 4.0x to 6.0x | Deposits are on-chain and measurable, though retention varies widely |
| Token launch (TGE) | 3.0x to 5.0x | Concentrated around a date, with little room to correct a bad start |
| NFT and GameFi | 2.5x to 4.0x | Purchase intent is discretionary and the audience is the least predictable |
Read these as ranges rather than targets. A project at the bottom of its category range has not necessarily been failed by its agency, and one at the top has not necessarily been served better.
What moved results more than budget?
A confirmed launch date. Campaigns with a specific date within a two-week tolerance returned a median 3.1x. Campaigns without one returned 1.4x, a difference of roughly 140 percent, and it held across project types and budget levels.
The mechanism is not mysterious. A date lets every channel converge: PR can be embargoed to it, creators can be scheduled against it, community activity can build toward it. Without a date, each channel fires independently and the audience never assembles in one place at one time.
The practical implication is uncomfortable for most projects. Postponing a campaign until the date is real produces better returns than starting on schedule without one, and almost nobody does it.
Why publish a failure rate at all?
Because a median calculated only on successful campaigns is not a median. Twelve percent of the campaigns in this set did not reach their target, and those campaigns sit inside the 2.8x figure rather than beside it.
Removing them would produce a higher headline number and a less useful one. It would also be undetectable from outside, which is precisely why benchmarks published without a failure rate should be read with suspicion. The question to ask any agency quoting a return multiple is simple: how many campaigns are in the denominator, and did the ones that failed stay in it.
What this benchmark does not tell you
It is not a forecast, and it is not a guarantee. It describes what happened to a set of campaigns run by one agency in a specific market period, and market conditions between January 2024 and February 2026 will not repeat.
It is also first-party data rather than an independently audited industry study. We compiled it from our own campaign records, which means it is verifiable against our methodology and not against anyone else’s. Treat it as a reference point for what normal looks like, and use it to interrogate the numbers other suppliers quote rather than as a promise about your own project.
The ROI calculator models the same variables against a specific project, and our methodology page sets out how the underlying data is collected and reviewed.
Frequently asked questions
Is 2.8x a good return for crypto marketing?
It is the middle of this data set rather than a standard. Whether it is good depends on margin: a protocol whose depositors stay for a year values a multiple differently from a collection selling once. Compare it against what the same spend would return elsewhere in the business.
Why 90 days rather than a longer window?
Because a longer window makes almost any campaign look successful and removes the supplier’s accountability. Ninety days is long enough for a launch to resolve and short enough to be checked.
Does a bigger budget produce a higher multiple?
Not proportionally. Larger budgets buy more absolute reach, but the multiple flattens as the most efficient channels saturate. Launch-date clarity moved the multiple further than budget size did across this data set.
How can a buyer verify a benchmark like this?
Ask for the denominator, the window and the failure rate. An agency that can state how many campaigns are counted, over what period, and what share missed target is describing a real data set. One that cannot is describing a selection of good outcomes.
Where to go next
For the placement side of the same data, see our study of 195 crypto PR outlets. For how the channels that produce these returns fit together, see how the direction gets chosen.
Want the benchmark applied to your project rather than to a median? Send the brief on Telegram at https://t.me/flexe_io_agency and we will come back with a channel mix and a target range within 24 hours.
Nothing here is financial advice. Figures are first-party data from our own campaign records for January 2024 to February 2026 and are not an independently audited industry benchmark. Past campaign results do not predict future ones.