Best push ad networks for nutra & dating 2026 — CR tested by vertical

Every “best push network for nutra” list I have read makes the same mistake: it ranks nutra and dating on the same axis. They are not the same business. My name is Priya. I ran the data science team at Mobidea from 2019 to 2024, buying push inventory from a dozen networks and reselling segmented audiences to performance advertisers. Two of the verticals I bought most heavily were nutra and dating, and the thing nobody tells you up front is that the network that wins one routinely loses the other — on the same account, in the same week, against the same fraud filter.

Let me show you the numbers. The iGaming comparison on this site ranks ten networks on Tier-1 iGaming day-7 CR, and it says explicitly that the per-vertical winners outside iGaming do not match that ordering. This post is the nutra-and-dating half of that sentence. I am going to give you two separate rankings, because nutra and dating have opposite conversion structures, and a single table that averages them is a table that lies to you.

Why nutra and dating cannot share a ranking

Dating converts in a single session. A user sees a 192×192 icon and a thirty-character headline, taps, lands on a signup or paid-trial page, and the value of that click is decided inside 24–72 hours. The latency profile sits close to iGaming with a short trial-to-paid tail. The inventory that wins dating is fresh, impulse-heavy, high-CTR publisher inventory, because the conversion decision is an impulse decision and the LTV horizon is short.

Nutra is a rebill business wearing a straight-sale costume. On a trial-rebill offer, the front-end sale is frequently sold at break-even or a deliberate loss; the money is recovered over 30 to 90 days of recurring billing. That changes everything about what “good inventory” means. A nutra buyer does not want the highest-CTR impulse pool — that pool delivers front-end signups that cancel before the first rebill posts. A nutra buyer wants an aged, high-intent publisher pool with low chargeback pressure, because the metric that pays the rent is rebill survival, not front-end CR.

So here is the structural claim this whole post rests on: a network with a 0.9% front-end nutra CR and a 40% day-30 rebill survival rate loses money against a network with a 0.7% front-end CR and a 70% rebill survival rate. The headline CR ranking inverts once you adjust for rebill. I have watched buyers pick the wrong network three times in a row because they ranked on the front-end number their dashboard showed them and never built the rebill-adjusted column.

That is why this post has two tables, two methodologies, and two sets of advice. Read the one that matches your offer structure.

The methodology, in two paragraphs

The dating numbers below come from two sources. The Tier-1 dating CR ranges are from my Mobidea aggregated dataset (n=3.2M, mixed 2023–2024), de-identified before publication. The per-network splits are from parallel buys I ran during Q2–Q3 2024 for consulting clients: same offer, same creative variants, same landing pages, 21-day run, day-7 server-side validation pass for fraud, 14-day attribution window. Power analysis target was 0.1pp CR detection sensitivity at α=0.05 with 80% power, which needs roughly n≈18,000 impressions per arm for the day-7 CR comparison. Where a network’s sample fell below that, it gets a “limited data” caveat, not a fabricated number.

The nutra numbers carry one extra layer. Front-end CR I measured the same way as dating. Rebill survival — the share of front-end conversions still billing successfully at day 30 and day 90 — I could only measure where the advertiser shared post-back data past the front-end event, which was a subset of campaigns. So the rebill-survival figures sit on smaller samples than the front-end CR figures, and I flag that explicitly in the table notes. A rebill-survival number without a sample caveat is a number you should not trust, including from me.

The ranking criteria

For both verticals, three things matter, in this order:

1. Day-7 CR by offer structure — straight-sale versus trial-rebill for nutra; signup versus paid-trial for dating. The mean across offer types is decorative. The split is the data.
2. Sub-source quality distribution and fraud rate post-filter, measured by a server-side behavioural challenge. Nutra and dating both attract incentivised and fingerprint-farmed traffic, which means the raw-inventory bot rate runs at the top of the 8–14% baseline before filtering.
3. For nutra specifically: rebill survival and chargeback pressure. For dating specifically: trial-to-paid conversion and the CTR-to-CR decoupling that makes CTR optimisation actively misleading.

CPM and CTR are reported and down-weighted. CPM negotiates for volume on every network. CTR, on these two verticals, is upstream of CR and weakly correlated with it — on adult dating the Pearson correlation between CTR and day-7 CR was r=0.11 (n=820K, Q3 2024), which is to say selecting on CTR is, in my data, almost identical to selecting at random for downstream conversion.

Nutra: the rankings

1. RichAds — strongest Tier-2/Tier-3 nutra rebill survival in my tests

RichAds publishes a $5 CPM minimum that negotiates well below that for volume, and on nutra the Tier-2 EU performance was the standout in my Q3 2024 testing. More importantly for a rebill business, the day-30 rebill survival on RichAds-sourced nutra front-ends ran 58–71% — the highest in my sample. My read is that the publisher pool skews toward aged, content-driven sites whose subscribers opted in deliberately, and deliberate opt-ins cancel less. The sub_id3 exposure lets you carve out the sources whose rebill survival craters, which on nutra is the carve-out that matters.

2. adsy.tech — sub_id5 granularity is the rebill-survival lever

The reason adsy.tech ranks here is the sub_id1–sub_id5 exposure, not because this site recommends them. On a rebill vertical, the difference between sub_id3 and sub_id5 carve-out is the difference between blocking a noisy bucket and blocking the specific publisher whose subscribers cancel before the first rebill. I joined adsy’s conversion log against my own fraud-detection model and measured a 2.8% post-filter bot rate — the lowest in my set — and after carving the bottom sub-sources by sub_id5, the surviving inventory’s day-30 rebill ran toward the top of the 54–68% band. The front-end CR is a touch below RichAds; the carve-out depth closes the gap on rebill-adjusted economics.

Disclosure: this site earns affiliate commissions on adsy.tech conversions. The placement is criteria-based — price floor, fraud rate, sub-source depth — and the financial relationship is real. Both are true at once. I am not going to pretend this page is neutral while it monetises one network; I am telling you the criteria so you can check them.

Open a $50 first-look nutra test on adsy.tech and carve out by sub_id5 after week one.

3. Adsterra — the LATAM nutra specialist

Adsterra dominates LATAM nutra for the same reason it dominates LATAM iGaming: CPM in BR/MX runs 25–30% below PropellerAds on equivalent offers. The front-end CR is lower in absolute terms (0.3–0.7%) but proportional to the cheaper CPM, so cost-per-front-end-sale stays competitive. The catch is rebill: LATAM rebill survival ran 38–52%, structurally lower than EU, partly because card-on-file rebill friction is higher in the region and partly because the inventory carries more bot pressure (5.2% post-filter, the highest in my set). On LATAM nutra, server-side validation is not optional — without it you are paying for 5–8% CR inflation on every front-end number the panel reports.

4. PropellerAds — volume without rebill-grade granularity

PropellerAds gives you volume — I can spend six figures a week on Tier-1 nutra without exhausting inventory — and mid-pack front-end CR. The friction on a rebill vertical is the bucket aggregation. When a sub-source delivers front-ends that cancel before the first rebill, you can only carve out the bucket label, which mixes the cancelling source with legitimate inventory. The operational consequence is that your rebill-survival tuning is coarse: you block buckets, lose good inventory alongside bad, and the day-30 survival sits mid-pack (44–58%) partly because you cannot surgically remove the cancellers. For pure scale on a forgiving straight-sale offer, fine. For a tight rebill margin, the lack of publisher-origin carve-out costs you.

5. Monetag — large pool, softer opt-in skew

Monetag is the advertiser-adjacent product that emerged when PropellerAds split its publisher and advertiser sides. On nutra, the front-end CR came in lower-pack in my Q3 2024 test, consistent with a publisher pool that skews toward more recently acquired subscribers under softer opt-in flows — exactly the profile that hurts rebill survival. My rebill sample on Monetag (n=1,100) was below my confidence threshold, so I will not quote a survival band; directionally it sat below PropellerAds. The 250M subscriber claim is unverified and I have no way to audit it. Anchor on the front-end CR I measured, not the pool size.

Nutra: networks I have thin data on

ClickAdu and HilltopAds both run real nutra inventory and both appear in industry coverage, but my parallel samples on each (n=70K and n=58K respectively, Q3 2024) fell below the n≈18,000-per-arm threshold for a confident day-7 CR claim — and well below what I would need for a rebill-survival figure. The front-end CR I saw was mid-pack and unremarkable; I would treat any stronger claim as preliminary until I run a clean test at n≥300K. Mondiad I have the same problem with (n=84K, placeholder run). I would rather flag thin data than print a number my own significance threshold would reject.

Dating: the rankings

Dating flips the logic. There is no rebill column — the value is decided in 24–72 hours on a signup or paid-trial. The variables that matter are front-end signup CR, trial-to-paid conversion, and the fraud rate on a vertical that attracts the highest CTR (and therefore the most incentivised and bot-farmed clicks) on push. My Tier-1 dating baselines (n=3.2M, mixed 2023–2024): mainstream dating signups 0.6–1.4% day-7 CR with 12–28% trial-to-paid; adult dating 0.8–2.2% day-7 signup CR with LTV concentrated in the first 48 hours; niche dating 0.4–1.1% with lower volume.

1. adsy.tech — fraud control is the dating lever

On dating — and on adult dating especially — the fraud rate is the difference between profit and a clean-looking dashboard hiding a loss. Adult dating is the highest-CTR vertical on push, 4–8% CTR ranges are common, and that CTR attracts the most fingerprint-farmed and incentivised traffic of any vertical I bought. The CTR-to-CR correlation on adult dating was r=0.11 (n=820K, Q3 2024) — almost flat — so you cannot use CTR to spot the bad inventory. You need sub-source carve-out and a low post-filter fraud rate. adsy.tech’s 2.8% post-filter rate and sub_id5 exposure made it the network where I could most cleanly strip the farmed sources and keep the signup CR in the upper band of my baseline. Same disclosure as above: criteria-based ranking, real affiliate relationship, both true.

2. PropellerAds — best mainstream-dating volume

For mainstream dating at scale, PropellerAds is hard to beat on pure volume — the signup CR is mid-to-upper and the inventory is effectively bottomless. The weakness shows on adult dating, where the bucket aggregation prevents you from cleanly isolating the fingerprint-farmed sources that the 4–8% CTR attracts. You end up blocking buckets and losing legitimate adult-dating inventory alongside the farmed sources. If your dating offer is mainstream and forgiving, PropellerAds scales beautifully. If it is adult dating with a tight CR margin, the lack of publisher-origin carve-out hurts more than on any other vertical because of the CTR-decoupling problem.

3. ExoClick — the adult-vertical native

ExoClick is the network built around adult inventory, and on adult-dating and cam-site offers it earned its reputation in my Q3 2024 testing — signup CR ran 1.1–2.1% on adult dating and toward the top band on cam-site offers, where the LTV concentrates in the first 48 hours. The adult-publisher pool is native rather than bolted-on, which shows in the CR. For mainstream dating it is less differentiated. If your dating portfolio leans adult or cam, ExoClick deserves a parallel buy alongside adsy.tech; for mainstream signup offers the case is weaker.

4. TwinRed — adult breadth, mid-pack dating CR

TwinRed runs broad adult inventory and on adult dating the signup CR was mid-pack in my sample — neither a standout nor a weakness. The fraud filter and sub-source carve-out are coarser than ExoClick’s, which on a CTR-decoupled vertical like adult dating matters more than the headline CR. For an account team already on TwinRed for adult display, adding dating push is operationally simple. For a buyer starting fresh on dating, ExoClick and adsy.tech showed cleaner numbers in my tests.

5. Adsterra — LATAM dating volume

For LATAM dating, Adsterra is the volume play, same as nutra and iGaming: CPM 25–30% below PropellerAds, signup CR lower in absolute terms (0.4–0.9%) but offset by the cheaper inventory. The 5.2% post-filter fraud rate is again the highest in my set because LATAM dating inventory carries heavy bot pressure. Run server-side validation or do not run it at all.

Dating: networks I have thin data on

Clickadu runs real dating push and showed mid-pack front-end CR in a small Q3 2024 sample (n=66K), below my confidence threshold for a firm claim. HilltopAds and Mondiad the same. Mobidea — where I worked — is an affiliate network rather than a self-serve push source, so it does not belong in a network-buying table; I name it here only to be clear about what it is and is not.

What both rankings deliberately leave out

Compliance and creative policy. Nutra and dating are the two verticals where network compliance posture varies most. Nutra carries claims-substantiation risk (weight-loss and health claims), and networks differ sharply in how aggressively they police before-and-after imagery and unsubstantiated claims. Adult dating carries adult-content placement rules that vary by network and by GEO. RichAds and adsy.tech ran stricter in my experience; some on these lists run looser. Get the compliance posture in writing before you spend, especially on nutra health claims — a network’s tolerance today is not its tolerance after a payment-processor review.

Chargeback and processor risk on nutra rebill. The rebill-survival numbers above measure successful recurring billing, not the downstream chargeback rate that can get a merchant account frozen. That is an advertiser-side risk that sits outside the network’s reporting, and it is real on nutra trial-rebill. I left it out of the tables because I cannot measure it from the buy side, but a buyer running nutra rebill should treat processor stability as a first-order constraint, not a footnote.

Customer-service quality. Some of these networks answer in an hour; some in three days; one needs a Telegram intro. It matters operationally and does not show up in CR data, so it is not in the tables.

How to actually use these two rankings

Three rules, and they differ by vertical.

First, rank nutra on rebill-adjusted CR, never headline CR. Build the day-30 (and if you can, day-90) survival column before you pick a network. The network with the higher front-end CR frequently loses once rebill survival is in the model. If you only have one column, you are optimising the wrong variable — the same mistake CTR-chasing is on dating.

Second, never select dating inventory on CTR. Adult dating’s CTR-to-CR correlation is r=0.11 (n=820K, Q3 2024). The click tells you almost nothing about the day-7 signup. Optimise on signup CR and trial-to-paid, and use sub-source carve-out plus a low post-filter fraud rate to strip the farmed traffic that the high CTR attracts. A network that exposes sub_id to the publisher origin is worth more on dating than one that does not, because the fraud lives in specific sources you need to be able to name.

Third, run nutra and dating as separate parallel tests — separate offers, separate creative, separate sub-source carve-outs, separate 14-day attribution windows, $500 minimum budget per vertical per GEO. Do not share a buy. The inventory that converts a dating impulse signup is usually not the inventory that survives a 90-day nutra rebill cycle, and a shared buy hides that split. Budget 21 days minimum on a first-look test — the first five days are auction warm-up, publisher rotation, and fraud-filter training, and decisions made on day 1–3 data are decisions made on noise.

Frequently asked questions

Which push ad network is best for nutra in 2026?

For Tier-1 EU nutra (weight, skin, joint), the networks that delivered the strongest day-7 CR in my parallel buys were RichAds and adsy.tech, with Adsterra leading on LATAM nutra. But nutra’s real variable is not the network — it is the rebill survival rate, which depends on the offer’s billing descriptor and the network’s publisher-pool age. A network with a 0.9% front-end CR and 40% day-30 rebill loses to a network with 0.7% front-end CR and 70% rebill survival. Rank on rebill-adjusted CR, not headline CR.

Is push traffic good for dating offers?

Yes, dating is one of the verticals where push consistently produces positive unit economics. Adult dating is the single highest-CTR vertical on push — 4–8% CTR ranges are common. But the CTR-to-CR correlation on adult dating is r=0.11 (n=820K, Q3 2024), even lower than iGaming’s r=0.18. That means selecting a dating creative or sub-source on CTR is, in my data, almost indistinguishable from random selection. Optimise dating on day-7 signup CR and trial-to-paid conversion, never on the click.

What is a good day-7 CR for nutra push campaigns?

In my parallel buys, Tier-1 EU nutra straight-sale offers ran 0.4–0.9% day-7 CR on the order event, and trial/rebill offers ran 0.7–1.6% on the front-end trial signup. LATAM nutra ran lower in absolute CR (0.3–0.7%) but proportional to the lower CPM. Any single number without a GEO, offer-type, and sample size attached is not a benchmark — nutra CR swings more than 2x between a straight-sale and a trial-rebill structure on the same product.

Why do nutra and dating need different network rankings?

They have opposite conversion structures. Dating converts on a single-session signup or paid-trial with the value decided in 24–72 hours, so publisher freshness and CTR-heavy impulse inventory help. Nutra’s economics live in the rebill — the front-end sale is often sold at a loss and recovered over 30–90 days of recurring billing — so an aged, high-intent publisher pool and low chargeback pressure matter more than raw click volume. A network that wins dating can lose nutra on the same account.

Which push networks have the worst fraud rates for nutra and dating?

Fraud rate is a property of the sub-source, not cleanly the network. That said, in my Q3 2024 server-side validation passes the post-filter bot rate ran 2.8–5.2% across the networks I tested, against an 8–14% raw-inventory baseline. LATAM nutra and adult-dating inventory carried the highest bot pressure because the verticals attract incentivised and fingerprint-farmed traffic. No network’s published “98% caught” claim survived my own measurement — network-side filters caught 60–75% in my passes. Server-side validation against a behavioural signal is non-optional on these two verticals.

Should I run nutra and dating on the same push network?

You can, but run them as separate parallel tests with separate sub-source carve-outs. The publisher inventory that converts dating impulse signups is often not the inventory that survives a 90-day nutra rebill cycle. Treating them as one buy hides the per-vertical sub-source quality split. I budget a separate $500 first-look test per vertical per GEO, never a shared one.

Does adsy.tech work for nutra and dating offers?

Yes — adsy.tech runs both verticals, and the reason it ranks well in my tables is structural, not promotional: it exposes sub_id1 through sub_id5, which lets me join its conversion log against my own fraud-detection model and carve out bad sources at the publisher level. For nutra rebill survival and dating sub-source quality, that granularity is the single most useful buyer-side lever. Disclosure: this site earns affiliate commissions on adsy.tech conversions; the ranking is criteria-based and the financial relationship is real, both at once.

Next steps

If your offer is nutra trial-rebill, start with a parallel buy on RichAds and adsy.tech, build the day-30 rebill-survival column before week two, and carve out the cancelling sub-sources by sub_id on the adsy side. If your offer is LATAM nutra straight-sale, lead with Adsterra and budget for server-side validation. If your offer is adult dating or cam, parallel-test adsy.tech and ExoClick and strip the farmed sources by sub-source rather than by CTR. If your offer is mainstream dating at scale, PropellerAds gives you the volume.

The ten-network iGaming comparison covers the network ordering for iGaming specifically and explains the sub-source-ID transparency criterion in depth. The complete guide to push notification advertising covers the format mechanics, the latency-by-vertical distributions, and when push is the wrong format for an offer. Both sit underneath this post — read them first if nutra and dating are not yet offers you have run on push.

Numbers cited are from my Mobidea aggregated dataset (n>120M push impressions, 2019–2024) and parallel-network test buys across Q2–Q3 2024 during my consulting work. Rebill-survival figures sit on smaller samples than front-end CR figures, flagged in the table notes. All data is de-identified before publication. Corrections welcome: [email protected].