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NewThe reward layer for AI training →

Your best customers don’t look special on Day 1.

Churney predicts each new customer’s long-term value and sends it to Meta, Google and TikTok, so they bid for the customers who pay back.

Not sure it fits? Start with the free Growth Predictability Test.

Trusted by leading marketing and data teams globally

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The problem

Ad platforms learn in days. Your revenue shows up in months.

Meta, Google and TikTok optimize toward whatever your conversion event tells them. If that event fires before customers show their value, the platform learns the wrong lesson.

The window

Platforms learn from the first days after a click.

Most of your customer value arrives after that: repeat orders, renewals, upgrades.

The proxy

So campaigns optimize for early events.

Installs, trial starts and first orders fire inside the window, so that is what bidding learns from.

The cost

Budget drifts to customers who never pay back.

You see it in cohort reports a quarter later, after the platform has already scaled the wrong audience.

How it works

From your warehouse
to the bid.

Illustrative data

Leading brands growing with Churney

Country
USA
Category
SaaS
ROAS
+190%

"Churney helped us scale our campaigns without sacrificing efficiency. By focusing on predicted long-term value instead of early conversions, we doubled new customer volume and improved ROAS by 190%. It’s now a core part of how we run performance marketing."

Kaveh Motamed
Sr. Director of Marketing
Country
UA
Category
Apps
ROAS
+45%

"Working with Churney allowed us to test a smarter approach to campaign optimization, one that focuses on users’ long-term value rather than just early conversions. It’s an exciting direction for improving efficiency and user quality in performance marketing."

Tetiana Marynych
Head of Analytics
Country
USA
Category
Retail
ROAS
+29%

"Many companies promise improved ROAS, but Churney actually moves the bottom line. We saw immediate improvements in efficiency that allowed us to scale without second-guessing."

Roi Yosef
CTO, Los Angeles Apparel
Country
TR
Category
Apps
ROAS
+26%

"When we evaluated Churney, we focused on three criteria: Could they construct a predictive signal with appropriate calibration? Could they integrate it reliably into our data pipeline? And could they measure the outcome without bias? They delivered on all three."

Ilker Çam
CTO & Co-Founder
Country
CN
Category
Apps
ROAS
+34%

"Churney helped us move past short-term signals and tap into our users' real long-term value."

Liu Hengyu
Marketing Lead
Country
UK
Category
Apps
ROAS
+12%

"Implementing Churney’s predictive LTV model has been a game-changer for our user acquisition strategy. By identifying high-potential subscribers early, we’ve not only increased the lifetime value of our customers but also significantly improved retention rates and overall campaign performance."

Tom Blackshire
Director of Organic and Paid
Country
IL
Category
Retail
ROAS
+33%

"Churney has improved our advertising efficiency. By optimizing for predicted long-term value, we increased ROAS by 33% while gaining scale. The impact was so clear that we’ve now rolled out Churney’s conversion action across our Google account. Beyond that, Churney has enabled us to launch and scale new product categories that we couldn’t advertise before. It’s transformed the way we approach growth."

Felix Leshno
Co-Founder
Country
DK
Category
Retail
ROAS
+28%

“The key to success is to identify potential high-value customers swiftly and channel marketing budgets towards acquiring them. Our partnership with Churney was crucial in fueling Meta’s machine-learning algorithm with pLTV values.”

René Tingskov
Head of Marketing
Country
DE
Category
Apps
ROAS
+64%
Blinkist logo

"The most meaningful day-to-day change so far is that I'm checking the split a bit more closely than usual in order to learn as much as possible about the signal, for example when it leaves the learning period, when it will show the first good ROAS signals."

Heiko Mandler
Performance Marketing Manager, Blinkist
Country
CN
Category
Apps
ROAS
+55%

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Beyond ad platforms

The signal that trains ad platforms can train your models.

The same prediction works as a reward for fine-tuning LLMs and agents.

See the reward layer →
Predicted value streamStreaming
Ad signalNew customer · predicted 1-year value $412→ Meta
RewardSupport reply · predicted value impact +$18→ RFT run
Ad signalTrial start · predicted 1-year value $38→ TikTok
RewardAgent action · predicted value impact −$6→ RFT run

Illustrative

See whether your data predicts long-term value.

Book a 30-minute call. We look at your setup and tell you plainly whether pLTV is worth testing.

FAQ

Questions performance and data teams ask before they change the bid signal.

If your real customer value shows up after the first conversion, these are the tradeoffs worth understanding upfront.

We already optimize on purchases or trials. Isn’t that enough?

It is enough if first-conversion value is a fair proxy for long-term profit across your entire customer base. For many businesses, it is not.

Ecommerce teams often see strong first-order ROAS while repeat rate, category mix, or margin tell a different story weeks later. Subscription and app teams often see trials or installs convert inside the platform window while paid subscriptions or in-app value show up after.

Optimizing only on early proxies can cap prospecting, distort category expansion, inflate spend on one-and-done buyers, and undervalue your repeat ones. pLTV is for teams whose best customers reveal their value after the platform stops looking.

We built (or tried) pLTV in-house and it didn’t move the needle. Why would this be different?

That usually comes down to signal timing, integration, or measurement, not whether LTV can be modeled at all.

Common failure modes: predictions that arrive too late for the platform learning window; scores that never reach the ad platform reliably or at the right freshness; no clean way to prove lift against business-as-usual bidding; or a model that breaks when product, pricing, or funnel mix changes. Churney’s DIY pLTV cautionary tale walks through where internal builds usually stall.

Churney is built for activation and measurement together: calibrated predictions, platform delivery, and structured testing against what you run today, so your data team is not signing up for another open-ended internal build.

How do we know the lift is real, especially if we’re running other tests?

You should not have to take a vendor’s word for it, especially with concurrent channel changes, creative tests, or seasonality in the mix.

Before launch, agree what business-as-usual means, what gets held out or controlled, and which metrics you trust as source of truth: your attribution stack, not ours. The experiment goes live in week 9 and runs six weeks: a two-week learning phase that is excluded from the evaluation, then four measured weeks. Cohorts mature for two more weeks and the read-out comes in week 17, so you are not judging long-term value on a seven-day window.

Where possible, we run tests through each platform’s official experimentation tools. Meta and Google both offer native experiment frameworks, which helps keep holdouts, controls, and readouts sanitized and clean. For a deeper read on holdouts, BAU, and what “lift” should mean in a pLTV pilot, see what a pLTV test is actually trying to prove.

How is this different from tROAS, value-based bidding, or marketing mix modeling?

Value-based bidding and tROAS optimize from whatever value signal the platform already receives. Churney improves the signal itself: predicted per-user long-term value from your first-party data, sent server-side so the platform can learn from it.

Marketing mix modeling helps you understand channel contribution at a portfolio level, useful for budget planning. pLTV works inside the ad platform, at the user level, every day, so the campaigns you are already running can optimize toward predicted long-term value.

Many teams use both; they answer different questions.

What does this cost, and what if the uplift doesn’t cover the fee?

That is the right question. Headline ROAS improvement only matters if it survives fees, margin, and the cost of running the test.

Churney runs a four-month pilot before production; the onboarding guide shows it week by week. Commercial terms depend on spend, scope, and pilot design. Evaluation should include net uplift math (incremental profit versus pilot and production fees), not a vanity ROAS percentage alone.

Exact pricing, pilot fees, and any performance-linked terms are shared during evaluation and should match legal-approved contract language.