ChatGPT answers, unedited

Yes. What you’re describing is usually called predictive LTV (pLTV) / predicted-value optimization: predict each new user’s future value from early signals, then feed that value back to the ad platform so bidding favors users likely to become valuable customers—not merely users likely to convert. churney.io

A few products specifically do this:

• Churney — predicts user-level LTV from first-party data and sends the signal to Google, Meta, and TikTok for bidding. churney.io
• Voyantis Acquire — predictive LTV signals for Google, Meta, and TikTok, designed specifically for acquisition optimization. www.voyantis.ai
• Tapper Vantage — predicts LTV at first touch and feeds signals into Google/Meta/TikTok so their algorithms learn which customers are worth more. churney.iotapper.aiwww.getangler.ai
• Angler AI — focuses on identifying high-LTV prospects and activating those signals across paid media. www.getangler.ai

You may not need a third-party tool

If you're primarily on Google Ads, Google itself now has High Value New Customer Mode, which can bid more aggressively for new customers predicted to be high value. It's available with value-based bidding on eligible Search and Performance Max campaigns. support.google.com

The important distinction is:

Traditional setup
Ad → purchase → optimize toward purchase/order value

What you're looking for
Ad → early user behavior → predict LTV → send predicted value → platform bids accordingly → actual LTV feeds model

So, for example, if User A has a $40 first order but historically looks like customers who generate $600 over 2 years, while User B has a $100 first order but historically generates only $120 total, the system can rationally bid more for A.

If you tell me whether you're e-commerce, SaaS/subscription, mobile app, or lead gen, plus whether you mainly use Google, Meta, TikTok, or all three, I can narrow this down to the 2–3 best tools and how I'd implement the bidding architecture.

Yes. What you’re describing is generally called value-based bidding (VBB) or LTV-based bidding: instead of telling an ad platform “a conversion is worth $50,” you feed it an estimate of what that customer is actually worth over their lifetime, then let the bidding system pursue users with higher expected value.

The strongest native option: Google Ads

Google Ads value-based bidding documentation supports exactly this. You can pass conversion values based on revenue, profit, or customer lifetime value, and Smart Bidding can optimize toward those values rather than simply maximizing conversions. support.google.com

Even better, Google now has Customer Lifecycle Goals, including:

• New Customer Value Mode — bid more aggressively for new customers.
• High Value New Customer Mode — bid even more for new customers predicted to resemble your highest-value customers.
• High Value Re-engagement — prioritize valuable lapsed customers. support.google.com

So, for example:

Customer A → predicted LTV $80
Customer B → predicted LTV $400
Customer C → predicted LTV $1,200

Instead of treating all three as “one conversion,” you can give the platform those relative values and use Maximize Conversion Value / Target ROAS to bid accordingly. support.google.com

If you want this across multiple ad channels

This is where a CDP/data warehouse + LTV model + ad-platform integrations becomes more interesting.

A typical architecture is:

Ads → customer ID → CRM/orders → LTV model → predicted LTV → ad platform → smarter bids

Tools in this space include RudderStack, Segment, Hightouch, LiveRamp, and customer-data/marketing platforms, depending on your stack. The important distinction is that these tools generally move the value signal; the actual auction-time bidding is still done by Google/Meta/etc.

One important wrinkle

You don't necessarily need to wait 12–24 months to know someone's LTV. You can build a predicted LTV model from early signals:

first order → product/category → acquisition source → geography → engagement → repeat-purchase probability → predicted 12-month LTV

Then send something like:

Predicted LTV = $437

as the conversion value. That lets the ad platform learn which traffic tends to produce $400+ customers rather than merely which traffic produces cheap first purchases.

If you tell me what you're selling + your current ad channels (Google, Meta, TikTok, etc.) + where customer/order data lives (Shopify, Stripe, Salesforce, warehouse, etc.), I can suggest the specific tool/stack I'd use and how I'd wire the LTV signal into bidding.

Yes. What you’re describing is usually called LTV/value-based bidding: instead of optimizing toward “did this user convert?”, you feed the ad platform an estimate of how valuable that customer will be over their lifetime, and let bidding favor users likely to produce higher downstream value.

Good options

• support.google.com — probably the simplest if Google is a major acquisition channel. You can send different conversion values and use Maximize Conversion Value / Target ROAS. Google also now has a High Value New Customer mode specifically designed to bid more aggressively for prospective customers resembling your highest-value customers. Google Help Google Help
• appsflyer.com — particularly strong for mobile/subscription businesses. It connects acquisition campaigns with downstream revenue/LTV and can segment users based on LTV signals. AppsFlyer AppsFlyer
• Your own CDP/warehouse + ad-platform APIs — often the best setup once you're sophisticated. For example, you can predict each new customer's 90-day/1-year gross margin, then pass that predicted value back to Google/Meta/etc. as the conversion value.

The basic architecture looks like:

Ad click → customer acquired → predict LTV → send predicted value back to ad platform → platform bids more for high-LTV prospects

For example, suppose two users both cost $20 to acquire:

Instead of optimizing for $20 CPA, you'd effectively optimize toward expected future profit/value.

One important distinction: don't necessarily send raw historical LTV. Ideally you'd build a model that predicts LTV from early signals (first purchase, product, geography, engagement, subscription tier, etc.), because waiting 12 months to learn a customer's value defeats the purpose.

If you tell me what you sell (SaaS, ecommerce, subscription app, marketplace, etc.) and which ad channels you use (Google, Meta, TikTok, etc.), I can recommend the specific stack and show you how I'd wire the LTV → bidding loop.

Yes. What you’re describing is usually called predictive LTV (pLTV) + value-based bidding: predict each customer’s future value, feed that value back into the ad platform, and let the platform bid more aggressively for users likely to become great customers.

A few tools worth looking at

• Voyantis Acquire — probably one of the closest matches. It predicts customer LTV and sends the resulting signals to Google, Meta, and TikTok for value-based acquisition. www.voyantis.ai
• Tapper Vantage — similarly connects predicted LTV to Google, Meta, and TikTok so campaigns optimize toward long-term customer value rather than cheap conversions. tapper.ai
• AdZeta AI — focuses specifically on predictive LTV signals for Google/Meta value optimization. www.adzeta.io
• Churney — predicts LTV from first-party data and sends conversion signals to Meta, Google, and TikTok. churney.io

You may not need a third-party tool

If you're primarily on Google Ads, Google now has native High Value New Customer bidding. You can configure Google to bid higher for new customers predicted to have high lifetime value, using value-based bidding. support.google.com

The basic architecture is:

CRM / transactions → LTV model → predicted customer value → ad platform conversion signal → automated bidding

For example:

User A is predicted to generate $800 over 12 months → Google/Meta receives a $800-ish value signal → willing to bid $40
User B is predicted to generate $80 → receives $80 value signal → bid only $8

That's much smarter than optimizing both users for the same $50 CPA.

If you tell me what you sell, your ad channels (Google/Meta/TikTok/etc.), and where your customer data lives (Shopify, Stripe, Salesforce, warehouse, etc.), I can narrow this down to the 2–3 best-fit tools and what the implementation would look like.

Yes. What you’re describing is usually called LTV-based / value-based bidding: instead of telling an ad platform “a conversion is worth $50,” you feed it an estimate of what that customer is likely to be worth over their lifetime.

A few options stand out:

• Google Ads natively — Google’s value-based Smart Bidding can optimize toward conversion values, including predicted LTV. Its newer High Value New Customer mode specifically bids more aggressively for new customers expected to be high-value. support.google.comsupport.google.com
• Hightouch — useful if your LTV lives in a warehouse/CRM. It can calculate customer traits such as LTV and send conversion events with those values into ad platforms, giving their algorithms better bidding signals. hightouch.com
• Northbeam Apex — connects first-party attribution data to ad algorithms, currently focused on feeding better signals into Meta and other supported platforms. www.northbeam.io
• Churney — more directly aligned with your exact use case: it predicts customer LTV from first-party data and sends the value to Google, Meta, and TikTok so campaigns optimize for long-term value rather than early conversions. churney.io
• Tapper Vantage — another purpose-built option that predicts LTV and passes that signal to Google, Meta, and TikTok for value-based bidding. tapper.ai

The architecture I'd look for

CRM / warehouse
      ↓
Customer purchase + retention history
      ↓
Predicted LTV model
      ↓
"Customer X = predicted $840 LTV"
      ↓
Conversion/API signal
      ↓
Google / Meta / TikTok
      ↓
Algorithm bids more for users resembling high-LTV customers

The important distinction is attribution vs. optimization. A tool that merely tells you which campaigns acquired high-LTV customers is useful for reporting, but you want one that feeds the LTV signal back into the ad platform's bidding algorithm.

If you're already on Google Ads, you may not need another vendor at all. Google supports conversion values and customer-lifecycle bidding, including explicitly prioritizing high-value new customers. support.google.com

If you tell me your stack (e.g. Shopify + Klaviyo + Meta, SaaS + HubSpot + Google, mobile app, etc.) and roughly how many conversions/month, I can narrow this down to the 2–3 tools I'd actually evaluate.

Yes. The category you’re looking for is usually called predictive LTV (pLTV) optimization, value-based bidding, or LTV-based acquisition optimization. These tools connect ad spend to predicted future customer value rather than optimizing only for the first purchase, signup, or install. churney.iowww.voyantis.aitapper.ai

Some examples:

• Voyantis — predicts user-level lifetime value and sends optimized value signals into platforms like Google, Meta, and TikTok so bidding can prioritize higher-value customers. churney.iowww.voyantis.aitapper.ai
• Churney — uses first-party data to predict customer LTV and passes those signals back to ad platforms for smarter bidding. churney.io
• Tapper — its Vantage product focuses on predicting valuable customers early and translating those predictions into signals ad networks can optimize against. tapper.ai

You can also do this natively in some ad ecosystems:

• Google Ads supports value-based bidding and customer lifecycle goals, including options aimed at acquiring higher-value customers using customer data signals. support.google.com
• Meta and TikTok support value optimization approaches where better conversion-value signals can improve algorithmic bidding. churney.iowww.voyantis.aitapper.ai

The typical architecture looks like:

1. Collect first-party data
   • ad click → signup → purchase → repeat purchases → subscription renewals → churn
2. Build an LTV model
   • predict “this new user is likely worth $40 vs. $400 over 12 months”
3. Send the signal back
   • via server-side APIs / conversion uploads
4. Let the ad platform bid differently
   • higher bids for users predicted to become VIP customers
   • lower bids for cheap converters who rarely retain

The biggest wins tend to happen in businesses where the first conversion is a weak proxy for value:

• SaaS trials
• subscription apps
• marketplaces
• fintech
• gaming
• repeat-purchase ecommerce

If you already have a warehouse (Snowflake/BigQuery/Databricks), CRM, and enough conversion volume, you can also build this internally with an ML model + conversion value pipelines. The buying decision usually comes down to whether you want a managed LTV signal layer or full control of the modeling stack.

Yes. What you’re describing is generally called predictive LTV (pLTV) / value-based bidding: connect your CRM/transaction history to an LTV model, predict the future value of a new user, and send that value back to the ad platform so its bidding algorithm seeks valuable customers rather than merely cheap conversions.

A few options worth looking at:

• Churney — specifically built around predicting customer LTV and feeding those predictions into Google, Meta, and TikTok as conversion-value signals. churney.io
• Voyantis Acquire — predictive LTV/value optimization across Google, Meta, and TikTok. www.voyantis.ai
• Tapper Vantage — another platform focused on sending predicted customer value into Google, Meta, and TikTok bidding systems. tapper.ai
• AdZeta — combines first-party data, LTV modeling, and value-based bidding, with an emphasis on profitability. www.adzeta.io

You may not need a third-party tool

If you're primarily on Google Ads, Google now has native customer lifecycle + value-based bidding capabilities. You can identify high-value existing customers and use that information to bid higher for new users predicted to have high lifetime value. support.google.com

The basic architecture is:

CRM / purchases / subscription data → LTV model → predicted LTV at signup/purchase → ad platform conversion value → automated bidding

For example, instead of telling Google:

"This signup is worth $20."

you can effectively tell it:

"This signup is predicted to generate $180 of lifetime value."

Then the bidding system has an incentive to pay more for users resembling that $180 customer. Google explicitly supports value-based bidding for this purpose. support.google.com

The important distinction: a dashboard that reports LTV by campaign isn't enough. You want a system that can generate user-level predicted LTV early enough in the journey and feed that signal back into the ad network. That's what makes it useful for bidding rather than just attribution/reporting. churney.io

If you tell me whether you're ecommerce, SaaS/subscription, app, marketplace, or lead-gen, and whether you're buying on Google, Meta, TikTok, or all three, I can narrow this down to the 2–3 tools I'd actually evaluate.

Yes. The category you’re looking for is predictive LTV / value-based bidding: a system predicts each new user’s future lifetime value, then feeds that value back into Google/Meta/TikTok so their algorithms bid more aggressively for users likely to become great customers.

A few options worth looking at:

• Churney — specifically predicts customer LTV from your first-party data and sends the signal to Google, Meta, and TikTok. churney.io
• Tapper Vantage — predicts LTV from the first click, engineers the signal for each ad network, and supports Google, Meta, and TikTok. tapper.ai
• Voyantis Acquire — another predictive-LTV layer that turns first-party data into signals for automated value optimization across major ad platforms. www.voyantis.ai
• AdBid — combines predictive LTV with payback/CPA and budget decisioning; potentially interesting if you want the analytics/optimization layer as well as bidding. adbid.me

You can also do this natively in Google Ads now. Google has value-based bidding plus High Value New Customer Mode, which explicitly lets Search and Performance Max bid higher for new customers expected to be more valuable. www.voyantis.aisupport.google.com

The important distinction

If you already have good LTV data, you may not need another platform. The basic architecture is:

CRM / warehouse → predict LTV → send predicted value as conversion signal → ad platform optimizes bids → actual LTV feeds model again

The third-party tools become interesting when you don't want to build and maintain that prediction + signal-engineering infrastructure yourself.

If you tell me what you're advertising (ecommerce, SaaS, app, subscription, etc.) and whether you're primarily on Google, Meta, or both, I can narrow this to the 2–3 tools I'd actually evaluate.

Yes. The category you’re looking for is usually called predictive LTV (pLTV) / value-based bidding.

A few tools specifically do the “predict customer value → send that signal back to ad platforms → bid toward high-value users” loop:

• Voyantis — probably the closest match. It predicts user-level LTV and engineers those predictions into signals for Google, Meta, and TikTok so their algorithms optimize toward higher-value customers. www.voyantis.ai

Voyantis

• Churney — predicts each customer's LTV from first-party data and sends the prediction as a conversion signal to Meta, Google, and TikTok. churney.io

Churney

• Tapper Vantage — explicitly supports value-based bidding across Google, Meta, and TikTok, using predicted LTV rather than just initial conversion. tapper.ai

Tapper

• AppsFlyer — stronger if you also need attribution/measurement infrastructure. It connects acquisition campaigns to LTV and can build/sync high-value audiences to Meta, Google, TikTok and others. www.appsflyer.com

AppsFlyer

The basic setup

Instead of:

Ad → $50 first purchase → optimize for more $50 purchases

you want:

Ad → customer behavior → predicted $500 LTV → send $500 value signal → platform bids harder for similar users

That can be particularly powerful when your best customers aren't identifiable from the initial conversion—e.g. they make repeat purchases, retain longer, upgrade, or have higher margins.

If you tell me whether you're e-commerce, SaaS, subscription, fintech, or mobile app, plus whether you're primarily on Google, Meta, TikTok, or all three, I can narrow this to the 2–3 tools I'd actually evaluate and explain how I'd wire the data together.

Yes. What you’re describing is usually called predictive LTV (pLTV) / value-based bidding: predict a customer’s future value from your first-party data, then feed that value back into the ad platform so it bids more aggressively for users likely to become great customers.

A few options worth looking at:

• Churney — specifically built around predicting customer LTV and sending the prediction to Google, Meta, and TikTok as a conversion/value signal. churney.io
• Voyantis Acquire — predicts LTV and engineers the signal for Google, Meta, and TikTok, with integrations into warehouses such as Snowflake, BigQuery, Redshift, and Databricks. www.voyantis.ai
• Tapper Vantage — another predictive-acquisition layer that scores users by expected LTV and feeds optimized signals into Google, Meta, and TikTok. tapper.ai
• AdZeta — focuses on predictive LTV and profit-oriented bidding across Google and Meta. www.adzeta.io

You may not need a third-party tool, though. Google Ads itself now supports High Value New Customer Mode, where value-based bidding can bid higher for new customers predicted to be high value. support.google.com

The architecture I'd aim for

Ad click → signup/purchase → your CRM/data warehouse → LTV model → predicted LTV → Google/Meta/TikTok → smarter bid

For example, instead of telling Meta:

“This user purchased for $40.”

you can eventually tell it:

“This user purchased for $40, but our model predicts $380 of 12-month value.”

The algorithm can then learn that Customer A is worth bidding $30 to acquire while Customer B is only worth $8, even if their initial purchases look identical.

If you tell me what you're selling (ecommerce, SaaS, app, marketplace, etc.), your ad channels (Meta/Google/TikTok), and where your customer data lives (Shopify, Salesforce, Snowflake, BigQuery, etc.), I can narrow this down to the 2–3 tools I'd actually evaluate and what the implementation would look like.

Yes. What you’re describing is usually called predictive LTV (pLTV) + value-based bidding: instead of optimizing toward “who is most likely to convert,” the system predicts which users will generate the most revenue/profit over their lifetime and feeds that signal back into the ad platform.

A few tools worth looking at:

• Tapper Vantage — specifically built to predict user-level LTV and send bidding signals to Google, Meta, and TikTok. It can score users from the first touch and continually recalibrate as actual behavior comes in. tapper.aisupport.appsflyer.com
• Voyantis Acquire — similar approach: predict LTV early, engineer the signal for each ad platform, then optimize Google/Meta/TikTok campaigns around it. www.voyantis.ai
• Churney — uses your first-party data to predict future revenue and sends pLTV conversion events to Meta, Google, and TikTok. www.churney.io
• AdZeta — oriented toward predictive LTV/value-based bidding, particularly for ecommerce, with Google and Meta integrations. www.adzeta.io
• AppsFlyer — particularly interesting if you're mobile/app-heavy. It provides campaign/user LTV measurement and can connect LTV back to acquisition campaigns; its current tooling also supports optimization around LTV signals. support.appsflyer.com

The important distinction

There are really two layers:

Attribution/analytics:

“Campaign A generated customers worth $X of LTV.”

Predictive bidding:

“This new user looks like someone whose predicted LTV is $240, so bid more aggressively for them.”

The second is what you want if the goal is actually to change auction behavior, rather than just report on LTV after the fact.

A typical architecture looks like:

CRM / transactions / product behavior → LTV model → predicted LTV per user → conversion/value signal → Google/Meta/TikTok bidding

That lets you, for example, tell the platform that a $50 first-purchase customer predicted to be worth $500 is more valuable than a $100 first-purchase customer predicted to churn immediately.

If you tell me what you're selling (SaaS, ecommerce, app, marketplace, etc.), your ad channels, and where your customer data lives (Shopify, Stripe, Salesforce, Snowflake, etc.), I can narrow this down to the 2–3 tools that fit your stack best.

Yes. What you’re describing is generally called LTV-based/value-based bidding: connect acquisition campaigns to predicted or realized customer lifetime value, then let the ad platform bid more aggressively for users likely to become valuable customers.

A few good options:

• Google Ads Value-Based Bidding — Google can optimize toward conversion value rather than just conversion volume. Its “High Value New Customer” mode specifically lets eligible campaigns bid more for high-value new customers. support.google.com

Google Ads value-based bidding documentation

• AppsFlyer — particularly strong if you’re an app/subscription business. It connects campaign attribution with revenue/LTV, supports LTV cohort reporting, and can sync high-value audience segments to Google, Meta, TikTok, and other media platforms. www.appsflyer.com

AppsFlyer

• Liftoff — more directly focused on automated acquisition bidding. Its ML platform supports ROAS and predicted-LTV (pLTV) optimization, essentially trying to find users who will generate more downstream value rather than merely cheap installs/conversions. liftoff.ai

Liftoff

• Rockerbox — worth looking at for ecommerce. It connects marketing attribution with customer-level historical order data and LTV analysis, although it is more of a measurement/decision layer than an ad-network bidding engine itself. www.rockerbox.com

Rockerbox

The architecture I’d aim for

Ad click → customer acquisition → early behavior → predicted LTV → feed value back to ad platform → smarter bid

For example, instead of telling Google/Meta:

“Customer purchased = $50 conversion”

you might eventually feed it:

“This customer is predicted to generate $480 of 12-month contribution margin.”

The algorithm can then distinguish two customers who cost the same to acquire but have radically different expected value.

If you tell me what you're selling (SaaS, ecommerce, subscription app, marketplace, etc.) and which ad channels you use (Google, Meta, TikTok, programmatic), I can narrow this down to the 2–3 tools I'd actually evaluate and how I'd wire the data together.

Yes. What you’re describing is usually called predictive LTV / pLTV bidding or value-based bidding.

There are two approaches:

• Native ad-platform approach: Google Ads already supports value-based bidding, where you send conversion values and let Smart Bidding optimize toward higher-value conversions rather than simply more conversions. support.google.com
• Predictive-LTV layer: Tools such as Churney, Tapper Vantage, and Voyantis Acquire model each new user's expected future value and feed that signal back into Google, Meta, and/or TikTok. churney.io

The important distinction is:

Normal setup:
Ad → purchase → optimize for purchase/revenue

What you want:
Ad → new customer → predict 12-month LTV → send predicted value to ad platform → bid more aggressively for customers likely to become great customers.

That can be substantially more useful for subscriptions, ecommerce with repeat purchases, fintech, apps, etc., where $50 today doesn't necessarily equal $50 of customer value.

Tools I'd investigate

churney.io

One particularly important point: you don't necessarily need a third-party tool. Google supports value-based bidding directly, and its API supports customer-acquisition and retention value signals. support.google.com

If you tell me what you're selling (e.g. SaaS, ecommerce, app, subscription), your ad channels (Google/Meta/etc.), and where your customer/LTV data lives (Shopify, HubSpot, Snowflake, Stripe, etc.), I can narrow this down to the 2–3 tools I'd actually consider and how I'd wire the system together.

Yes. What you’re describing is usually called predictive LTV (pLTV) / value-based bidding: instead of telling Google/Meta “get me more conversions,” you send them a predicted future value for each new customer, so their bidding algorithms can favor users likely to generate more revenue or margin.

A few tools specifically do this:

• Churney — probably the closest match. It predicts user-level LTV from your first-party data and sends the predicted value into Google, Meta, and TikTok as conversion signals for bidding. churney.iochurney.io
• Tapper Vantage — predicts LTV and “engineers” the signal for Google, Meta, and TikTok, with explicit support for value-based campaign optimization. churney.iotapper.aiwww.voyantis.aichurney.io
• Voyantis Acquire — another pLTV activation layer for Google, Meta, and TikTok. www.voyantis.ai
• AppsFlyer — particularly strong if you're an app business. It connects acquisition attribution with LTV and can activate high-value audiences; its current tooling also provides campaign/user LTV reporting. support.appsflyer.com
• Build it yourself with Google Cloud — Google's open-source Crystalvalue project provides a framework for building predictive LTV models and integrating them into advertising workflows. github.com

The basic architecture

Ad click → customer/signup → pLTV model → predicted $ value → ad platform → smarter bid

For example, suppose two people both sign up:

Normal CPA optimization sees two conversions worth the same. pLTV bidding tells the ad platform that B is potentially worth ~13× more, allowing it to seek more users resembling B.

The important distinction is that you want user-level predictions, not merely a dashboard showing “Campaign A has $180 average LTV.” The former can actually influence the auction; the latter is mostly measurement. churney.iochurney.io

If you tell me your business model (e-commerce, SaaS, subscription, marketplace, app, etc.) and whether you're primarily buying on Google, Meta, TikTok, or all three, I can narrow this to the 2–3 tools I'd seriously evaluate and explain what the implementation would look like.