How Many Creators Connect Their Accounts When Asked?

Nobody can tell you, and anyone who quotes a single figure without a denominator is describing their own funnel rather than an industry benchmark. Creator OAuth connect rates are held by consented data vendors and the platforms built on them, and none of them publish, so there is no dataset to average.

That is an unsatisfying answer, so here is a more useful one. The closest heavily measured analogue is App Tracking Transparency, where four major measurement companies have tracked billions of prompt responses for five years. Their published rates for the same thing in 2026 range from roughly 13.85 percent to 39 percent. A 2.8x spread, on the most instrumented consent prompt in the industry.

Understanding why that spread exists is worth more than any number, because it is the same reason your consent rate will be whatever your definition makes it. Below: what the ATT data actually shows, the four denominators that produce four different answers, what genuinely moves the rate, and how to instrument your own so the figure means something.

Why is there no published creator consent benchmark?

Three structural reasons, and none of them will resolve soon.

• The data sits in few hands. Only consented API providers and the products built on them see connect rates at scale. That is a short list, and each has a commercial reason to publish only when the number flatters them.
• Funnels are not comparable. A creator payments product that requires a connection to function has a completely different rate from an influencer marketing tool where connection is optional. Averaging them produces a number that describes neither.
• There is no standard definition. Consent rate of what: everyone who saw the screen, everyone who started the flow, everyone who signed up, or everyone the platform could theoretically reach? Each is defensible and they differ by multiples.

The third one is the real problem, and the ATT data proves it at industrial scale.

What does the most measured consent rate in tech actually show?

That the same phenomenon, measured by serious companies with enormous samples, produces wildly different published numbers. Here is what the major sources reported for iOS ATT opt-in around 2025 and 2026.

Look at rows two and three. AppsFlyer published 39 percent and 26 percent from the same underlying data. The only difference is weighting: one counts every response equally, so large apps dominate, the other gives every app one vote regardless of size. Both are honest. They are 1.5x apart.

Flurry goes further and documents the ambiguity explicitly, defining opt-in rate across apps that have displayed the prompt as one metric and separately tracking users who never had a choice at all. If the company measuring it has to publish two definitions to be accurate, a single industry number cannot exist.

One more figure worth carrying, from an FTC-published academic analysis of billions of ad impressions across 19 countries: ATT cut the share of trackable Apple traffic in the United States from 73 percent to 18 percent. That is a 55 point drop in *outcome*, on a prompt whose *opt-in rate* is variously reported between 14 and 39 percent. Rate and outcome are not the same measurement and they get quoted interchangeably.

Which denominator are you using?

Four are defensible, they answer different questions, and they produce different numbers from identical data. Choose one deliberately, publish it alongside every figure you quote, and refuse to compare against any number whose denominator you cannot see.

The account-level figure is the one that matters commercially, because your authenticated coverage is a function of it, and it is the one vendors are least likely to quote. If someone tells you their consent rate is high without saying which of these four it is, assume prompt-level.

A worked illustration of why the choice matters. Take 10,000 registered creators, of whom 4,000 reach the connect screen, 2,400 start the OAuth flow, and 1,800 complete at least one connection, holding an average of 3.2 relevant accounts each of which 1.4 get connected.

REGISTERED       = 10_000
SAW_SCREEN       =  4_000
STARTED_FLOW     =  2_400
COMPLETED        =  1_800
ACCOUNTS_HELD    = 1_800 * 3.2   # relevant accounts across supported platforms
ACCOUNTS_LINKED  = 1_800 * 1.4

prompt_level   = COMPLETED / SAW_SCREEN          # 45.0%
flow_level     = COMPLETED / STARTED_FLOW        # 75.0%
account_level  = COMPLETED / REGISTERED          # 18.0%
platform_level = ACCOUNTS_LINKED / ACCOUNTS_HELD # 43.8%

# One product. One month. Four honest numbers:
#   75.0%  flow-level      <- the one that goes in the deck
#   45.0%  prompt-level
#   43.8%  platform-level
#   18.0%  account-level   <- the one that drives your coverage
#
# Spread: 4.2x. Nobody lied.

Four point two times, from one month of one product, with no dishonesty anywhere. That is the same mechanism that produces the 2.8x spread in the ATT literature, and it is why a benchmark without a denominator is not a benchmark.

Do consent rates go up or down over time?

Up, on the evidence available, which is the opposite of what almost everyone predicted. When ATT launched in April 2021 the forecasts were close to apocalyptic, with estimates as low as 2 percent and most clustered in the low teens.

What actually happened was a slow, near-monotonic rise: roughly 34 percent in Q2 2023, 34.5 percent in Q2 2024, 35 percent in Q2 2025 and about 38 percent in Q1 2026. Five years of people getting more familiar with a consent prompt, not less willing.

I would be careful about over-reading this. ATT is a permission to track for advertising, which is a harder ask than connecting an account to a product you already chose to use. But the direction is worth knowing, because the assumption baked into most creator product roadmaps is that consent gets harder every year, and the best long-run dataset available says the opposite.

What actually moves the rate?

Five things, and the first one has the strongest evidence behind it by a distance.

1. The pre-prompt

The mechanism behind most of the ATT improvement was not attitudes changing. It was the priming screen: apps that explain, in their own words and their own design, what the user gets in exchange, before the platform modal appears. Apps that fire the platform modal cold on launch still get floor-scraping rates.

This transfers directly. A creator who hits a raw Instagram OAuth screen with no context is being asked to grant permissions by a company they have not yet decided to trust. A creator who has just read one screen explaining that connecting fills their media kit automatically is making a different decision. Same modal, different question.

2. Where in the flow you ask

The ATT guidance that consistently holds is to show the prompt after the user has experienced value, not on first launch. The creator equivalent: ask at the moment the connection unlocks something visible, not during signup. Signup is when trust is lowest and motivation is about creating an account, not about permissions.

3. The value exchange, stated concretely

Not "connect your accounts to get started." Something the creator can picture: your media kit fills itself, your real reach appears instead of your follower count, you stop typing numbers into a spreadsheet. Vague benefit statements are the most common failure and the cheapest to fix.

4. How many scopes you request

Every additional permission on the consent screen is another reason to hesitate, and over-requesting is also a common app review rejection. Request the minimum your core feature needs, ship, and add scopes later with a second, contextual ask. Two small asks usually beat one large one.

5. Which platform you are asking for

Rates vary by platform and the variance is not small. The consent screens differ in wording, length and scariness, the account type requirements differ, and a creator may hold a Professional account on one network and a personal one on another. Instagram requires a Professional account before any API access is possible, which turns a one-click connect into a two-step conversion. Measure per platform or you will average away the thing you can fix. That constraint and others are in our Instagram API guide.

How do you instrument this properly?

Log the funnel, not the outcome. Most teams record only the completed connection, which makes it impossible to tell a bad screen from a bad platform handoff from a creator who never got there.

# Emit one event per stage, per creator, per platform.
# Without platform on every event you cannot diagnose anything.

STAGES = [
  "connect_screen_viewed",   # they saw your pre-prompt
  "connect_clicked",         # they chose a platform
  "oauth_redirect",          # handed off to the platform
  "oauth_returned",          # came back, approved or denied
  "connection_active",       # token valid, first read succeeded
  "connection_revoked",      # withdrawn later
]

def track(stage, creator_id, platform, **ctx):
    emit({
        "stage":      stage,
        "creator_id": creator_id,
        "platform":   platform,        # never aggregate without this
        "surface":    ctx.get("surface"),   # onboarding | media_kit | settings
        "scopes":     ctx.get("scopes"),    # count and names
        "prompt_variant": ctx.get("variant"),
        "ts": now(),
    })

# The three drop-offs worth separating, because the fixes differ:
#   viewed -> clicked      = your copy and value exchange
#   clicked -> returned    = platform friction, account type, scope count
#   returned -> active     = YOUR BUG. Token exchange or scope handling.
#
# That third one is usually assumed to be creator refusal.
# It frequently is not. Instrument it separately before
# you rewrite any copy.

That last comment is the practical payoff of this whole section. Teams that log only completions attribute every loss to creators declining, then spend a quarter rewriting onboarding copy while a token exchange failure quietly eats a slice of every cohort.

What does a consent rate mean for your coverage?

It is the multiplier on your authenticated coverage, which is the point of measuring it. Authenticated data covers exactly the creators who connected, so your coverage curve is your connect curve, as covered in authenticated versus public social data.

def authenticated_coverage(registered, account_level_rate,
                           accounts_per_creator, platform_level_rate):
    """How many connected accounts you actually hold."""
    creators = registered * account_level_rate
    return creators * accounts_per_creator * platform_level_rate

# Same product, one year apart, same funnel:
print(authenticated_coverage(10_000,  0.18, 3.2, 0.438))   #  2,522
print(authenticated_coverage(40_000,  0.18, 3.2, 0.438))   # 10,091

# Now move ONLY the account-level rate, 18% -> 24%:
print(authenticated_coverage(40_000,  0.24, 3.2, 0.438))   # 13,455

# A 6-point rate improvement is worth ~3,400 connected accounts
# at 40k users. Growth and consent compound multiplicatively,
# which is why the rate is a product metric, not a marketing one.

What can Phyllo say about its own numbers?

Only what we can define and defend, which is the standard this post argues for, so it applies to us first.

What we can say without any proprietary data at all: connect rates vary enough by product type, funnel position and platform that a single industry benchmark would mislead more than it helped. If you want to compare against something, compare against your own last quarter, per platform, with the denominator written down.

And on the mechanics, which are the same for everyone: Phyllo's connect SDK handles the OAuth flow across 25+ platforms, so the parts you control are the pre-prompt, the placement and the scopes, and the parts we handle are the handoff, the token lifecycle and the normalisation afterwards. Identity resolution and account linkage then tie a creator's connected accounts to one record, which is what makes the platform-level denominator computable in the first place.

Where none of this applies. If your product does not require creators to sign in, consent rate is not your metric and a public data source is your architecture. We say the same thing at more length in the universal API guide and consent-based versus public social APIs.