A shallow tray of neatly arranged gray pebbles, one of them teal

Creator selection

Same budget, which creator portfolio delivers more?

A review of hundreds of real campaign posts — why a creator list should be built as a portfolio, not a bet on stars.

Tutti ResearchPublished ≈ 5 min read

We pulled a quarter of campaigns for review — a dozen-plus campaigns, hundreds of original posts, all measured in effective exposure, the same basis the invoice uses.

We set out to validate something we believed and had been telling clients: creator results swing a lot, so spread the budget across more people. The data showed that's only half the story. Spreading is right — the same logic as a stock portfolio — but what actually separates outcomes is how the creators are picked.

01 · Within one creator

How much one creator swings

Start with the picture. Each row is one of the more active creators, drawn from their worst post to their best:

Each row is one creator, from their worst post to their best
Horizontal axis: multiples of that campaign's median post (log scale) · dot = the creator's middle level
0.1×1× = campaign median post10×50×Median row: best ≈ 14× worst

Creators anonymized. Source: real campaign data from the Tutti platform, measured in effective exposure.

The middle creator's best post is 14× their worst, and half the creators swing wider than that. A third of what one creator delivers in a quarter comes from their single biggest post.

The chart is not saying some creators are steady and some are not. Every one of them is unsteady — almost every creator's results spread across a very wide range.

So don't plan around which post will take off. Creators can't predict that about themselves either.

02 · The picking signal

But picking creators is not a raffle

The other half of the same data: group the posts by author, and half of the variance in outcomes is decided by who posted. Picking controls a roster's average level; what it can't control is any single post.

One number surprised us: follower count explains only 7% of the variance in post outcomes. Picking from public profiles is close to not picking at all. The signal that works is a creator's record across real campaigns — how much effective exposure each post actually earned, and where it ranked in its own campaign. That record doesn't live on any public profile; it lives in our settlement data. It's what we build brand rosters from.

How far can you trust the record? We split the campaigns into an earlier and a later half: of the strongest earlier cohort, half stayed in the top tier, and a third dropped below the middle. Far better than a coin flip — nowhere near "buy last season's number one and relax."

03 · Where hits come from

Same hits, different people every time

The top 10% of posts carry half of all effective exposure. Splitting the later half's hit posts — the top decile within their own campaign — by the author's track record:

Who produced the hit posts
By the author's prior campaign record
Veterans (top third by record)
45%
Middle third
3%
Lower third
9%
Newcomers with no record
42%

Source: real campaign data from the Tutti platform, measured in effective exposure.

Veterans are genuinely strong: roughly one hit in every four to five posts, and 45% of all hits are theirs.

But 42% of the hits came from creators with no record at all — people no data could have picked in advance.

And only a third of these hits came from someone who had ever produced a hit before. The other two thirds hadn't hit in the previous round.

Build the list from records alone, and that 42% is never yours.

04 · Building the portfolio

How we build the portfolio

We backtested it on real history: same budget, same number of posts, a roster picked on records earns about seventy percent more than one thrown together — sometimes double.

Same budget: what picking is worth
Indexed to the unscreened roster = 1
Creators picked with no track-record screening
Screened on Tutti's campaign history
1.7×
Screened, with a few slots given to newcomers
newcomer slots
1.7× (volume holds)

In bad-luck runs, the screened-vs-unscreened gap widens to 2×. Third bar: volume barely moves, while 42% of hits come from newcomers. Source: real campaign data from the Tutti platform, measured in effective exposure.

Now give a few of those slots to newcomers: overall exposure barely moves, but the newcomer hits — and the fresh distribution they open up — can be worth far more than recycling the same accounts.

As for pushing the whole budget onto two or three stars: the paper math looks best, but you can't buy it — one creator posts one or two originals per campaign, nowhere near a full campaign's volume. Even if you could, when one person has an off run, all their posts sink together; top-tier pricing runs higher too. It's also hard to scale — too much of it is non-standard and uncertain.

So this is how we build brand rosters now: most slots go to creators with proven records, so the campaign's volume has a floor you can rely on; a few slots go to newcomers, a bet on where the hits come from. Like a stock portfolio — every single position is uncertain, but a well-built portfolio balances the whole.

Next time someone hands you a creator list, ask one question: has this list been portfolio-optimized?

Want to see your last campaign rebuilt as a portfolio? Bring the list — start a campaign consult and we'll draft a record-based version to compare side by side.

Topics

Build the roster like a portfolio

Tutti connects vetted 𝕏 creators with brands; rosters are built from real campaign records and measured on effective exposure end to end.