ChatGPT answers, unedited

Yes. What you’re describing is essentially value-based bidding tied to predicted customer lifetime value (LTV/CLV) rather than optimizing purely for leads or first purchases.

A practical setup

CRM/CDP → LTV model → ad platform → value-based bidding

For example:

1. Your CRM records that Customer A came from Google Ads campaign X.
2. Your data warehouse predicts Customer A will generate $2,400 of 12-month gross profit.
3. You send that value back to the ad platform as a conversion/value signal.
4. The platform learns which clicks/users tend to produce high downstream value and bids accordingly.

Google Ads can do this natively

support.google.com

Google explicitly supports uploading customer lifetime value and profit data and using those values with value-based bidding. Its conversion-value rules can also incorporate LTV considerations. Google Help Google Help

For lead-gen businesses, Enhanced Conversions for Leads + offline conversion data is particularly relevant: you can send qualified/deeper CRM outcomes back to Google and have them attributed to the original ad interaction. Google Help Google Help

Google also now has a High Value New Customer bidding mode, specifically intended to bid differently for high-value prospective customers. Google Help

The important distinction

You don't necessarily want to send historical LTV as the value of every conversion. A better architecture is often:

Predicted future value at acquisition → ad platform

For example:

That lets the bidding system pursue the type of customer who tends to become valuable, rather than simply whoever makes the largest first transaction.

If you tell me what you sell, your ad channels (Google/Meta/etc.), and where your customer/LTV data currently lives (Salesforce, HubSpot, Shopify, Snowflake, etc.), I can map out the specific tools and data flow I'd use.

Yes. What you’re describing is generally called value-based bidding or LTV-based acquisition: connect ad attribution to predicted customer lifetime value, then feed that value back into the ad platform so bidding favors users expected to generate more downstream value.

A concrete option: Google Ads

Google Ads has this built in fairly directly:

• Conversion value bidding: instead of optimizing for the number of conversions, Smart Bidding optimizes for the value of conversions. Google Help Google Help
• High-value new customers: Google Ads can use a first-party list of high-value customers to identify prospects likely to have high predicted lifetime value and bid more aggressively for them. Google Help Google Help
• Conversion value rules: you can incorporate information such as different customer values or lifetime-value considerations into the values Google uses for bidding. Google Help
• Target ROAS: if you assign meaningful monetary values to conversions, Google's bidding system can adjust bids at auction time toward users expected to produce higher value. Google Help

The important architectural distinction is:

Ad click → customer ID → revenue/retention data → LTV model → predicted LTV → ad platform conversion value → smarter bid

For example, suppose two acquired customers both initially buy for $50:

A normal CPA setup treats them identically. An LTV/value-based setup gives the bidding system a reason to pursue B more aggressively, because the expected economic value is much higher.

If you want a broader stack

There are really three components I'd look for:

1. Attribution/CDP — ties ad exposure → customer → purchases.
2. LTV/prediction layer — estimates future gross profit or LTV, ideally by customer rather than simply historical campaign averages.
3. Ad-platform activation — sends those values back to Google, Meta, TikTok, etc., where their algorithms can use them for bidding.

If you tell me your ad channels (e.g. Google/Meta/TikTok), business model (SaaS/ecommerce/subscription), and where your customer/LTV data currently lives (Stripe, Shopify, Salesforce, warehouse, etc.), I can map out a concrete stack and data flow for doing this.

A normal CPA setup treats them identically. An LTV/value-based setup gives the bidding system a reason to pursue B more aggressively, because the expected economic value is much higher.

If you want a broader stack

There are really three components I'd look for:

1. Attribution/CDP — ties ad exposure → customer → purchases.
2. LTV/prediction layer — estimates future gross profit or LTV, ideally by customer rather than simply historical campaign averages.
3. Ad-platform activation — sends those values back to Google, Meta, TikTok, etc., where their algorithms can use them for bidding.

If you tell me your ad channels (e.g. Google/Meta/TikTok), business model (SaaS/ecommerce/subscription), and where your customer/LTV data currently lives (Stripe, Shopify, Salesforce, warehouse, etc.), I can map out a concrete stack and data flow for doing this.

Yes. What you’re describing is generally called value-based bidding / LTV-based bidding: connect ad-platform acquisition data to downstream revenue or predicted customer lifetime value, then feed that value back into bidding.

A few ways to do it:

• Google Ads natively: Google’s Value-Based Bidding supports conversion values based on revenue, profit, or lifetime value, with Smart Bidding optimizing toward those values. Google also has High Value New Customer bidding for finding new customers resembling your most valuable customers. Google Help Google Help
• Google + your own LTV model: If you have a CRM/data warehouse that predicts 12-month or lifetime value, you can import those values as conversion values/offline conversions. Google explicitly documents using LTV as an input to bidding. Google Help Google Help
• Attribution/revenue-intelligence platforms: Tools such as AdBid position themselves as a layer connecting campaign attribution to cohort quality and predictive LTV across Google, Meta, TikTok, Snapchat, etc. AdBid

The architecture I'd look for

Ad click → customer ID → CRM/product data → predicted LTV → ad platform → bid

For example:

Facebook campaign A acquires customers with predicted 12-month gross profit of $420 → feed that value back → platform learns to bid more aggressively for users resembling those customers.

The important distinction is LTV vs. first-order revenue. If Customer A spends $50 initially but typically becomes a $1,000/year customer, while Customer B spends $100 once and churns, ordinary ROAS can favor B. An LTV-aware system can value A appropriately.

If you tell me your ad channels (Google/Meta/TikTok/etc.), business model (subscription/ecommerce/SaaS/marketplace), and where your customer/LTV data lives (Stripe, Shopify, Salesforce, Snowflake, etc.), I can give you a shortlist of the actual tools and a recommended setup.

Yes. This is a real category now: predictive LTV / value-based bidding. The basic setup is:

Ad click → user/customer → predict future LTV → send that value back to Google/Meta/TikTok → algorithms bid more aggressively for users likely to become high-value customers.

Good options

• Google Ads native value-based bidding — probably the first thing I'd evaluate if you're primarily on Google. Google can optimize toward conversion value and supports high-value new-customer bidding; you can also import customer lifetime value or profit data as conversion values. Google Help Google Help Google Help
• churney.io — specifically predicts individual customer LTV from first-party data and feeds the predicted value into Google, Meta, and TikTok so the ad platforms optimize toward longer-term value. Churney
• voyantis.ai — predictive LTV acquisition/optimization across Google and Meta, designed to turn predicted customer value into bidding signals. Voyantis
• tapper.ai — predicts LTV from the first click and sends engineered value signals to Google, Meta, and TikTok. Tapper
• amperity.com — more of a CDP/customer-data approach: resolves customer profiles and feeds CLV/predicted-value signals into paid media. Amperity
• adbid.me — focuses more broadly on connecting campaign attribution, revenue, cohort quality and predictive LTV for paid-media decisions. AdBid

The important distinction

If you're asking "I have CRM/customer data and want my ad platforms to actually bid differently based on predicted LTV", I'd focus on Churney, Voyantis, Tapper, or native Google value-based bidding, rather than a conventional attribution dashboard.

The architecture you want looks roughly like:

CRM + purchases + retention + engagement
→ LTV model
→ predicted 90/180/365-day value
→ conversion/value signal
→ `Google / Meta / TikTok bidding**

That is materially different from simply reporting that Campaign A produced customers with a higher LTV. The latter tells you what happened; the former gives the ad algorithm a signal it can use while deciding who to acquire.

If you tell me your business model (e-commerce, SaaS, subscription, app, lead-gen, etc.) + ad channels (Google/Meta/TikTok) + where your customer data lives (Shopify, HubSpot, Salesforce, Snowflake, etc.), I can narrow this to the 2–3 tools I'd actually shortlist and explain the integration/data flow.

Yes. What you’re describing is usually called value-based bidding or LTV-based acquisition: connect each acquisition source/campaign to the downstream revenue or predicted lifetime value of the customers it acquires, then let the ad platform bid more aggressively for users likely to become high-value customers.

A few good options:

• ads.google.com — probably the most direct fit if Google is a major channel. Its value-based bidding can optimize toward conversion values including predicted customer lifetime value, and its “High Value New Customer” setting specifically lets you bid more for prospects expected to become valuable customers. Google Help Google Help
• AppsFlyer — stronger if you need an attribution/measurement layer across multiple ad networks, web, mobile, CTV, etc. It connects campaigns to downstream LTV and can identify which campaigns actually produce high-LTV customers. AppsFlyer AppsFlyer
• liftoff.ai — particularly relevant for app acquisition. It can ingest behavioral/event data and use it for campaign optimization toward valuable users. Liftoff Docs Liftoff Docs

The architecture I'd recommend

Ad → customer ID → CRM/product events → predicted LTV → attribution → bid value

For example, instead of telling Google:

“A signup is worth $10.”

you send something closer to:

“This signup has an estimated $240 12-month value.”

The bidding system can then spend more to acquire that user than someone predicted to be worth $30. Google explicitly supports uploading customer lifetime value/profit data as conversion values for this purpose. Google Help

If you're running multiple channels (Google + Meta + TikTok + programmatic, etc.), I'd lean toward AppsFlyer or another attribution/CDP layer feeding LTV signals into the individual ad platforms, rather than trying to make each ad platform independently calculate LTV.

If you tell me whether this is e-commerce, SaaS, subscriptions, or mobile app acquisition, and which ad channels you're using, I can recommend the specific stack and data flow I'd use.

Yes. What you’re describing is usually called predictive LTV / value-based bidding: connect ad attribution to your CRM/order history, predict a customer’s future value, then send that value back to the ad platform so bidding favors customers likely to be worth more—not merely those cheapest to acquire.

A few options worth looking at:

• Churney — specifically built around predicting customer LTV and sending the predicted value into Google, Meta, and TikTok for bidding. Churney
• Tapper Vantage — predicts LTV from the first click and turns those predictions into signals for Google, Meta, and TikTok. Tapper
• Voyantis Acquire — predictive LTV with value-based optimization across Google and Meta. Voyantis
• Amperity — more of a customer-data/CDP approach; it enriches paid-media signals with CLV and predicted value from your customer data. Amperity
• AdBid — more focused on revenue intelligence/attribution, connecting campaigns with cohort quality and predicted LTV. AdBid

You may not need a third-party tool

If you're primarily on Google Ads, Google now has native Customer Lifecycle Goals and a High Value New Customer setting. You can assign additional conversion value to high-LTV new customers, and Google's value-based bidding can use that signal. Google Help Google for Developers

The basic architecture I'd aim for is:

Ad click → customer ID → first purchase → predicted 12/24-month LTV → send value back to ad platform → value-based bidding

For example, instead of telling Meta/Google:

"Customer bought $80."

you tell it:

"Customer bought $80, but based on their characteristics and early behavior, we predict $640 of lifetime value."

The bidding algorithm can then preferentially find more people resembling that $640 customer.

If you tell me your business model (e-commerce, SaaS, subscription, marketplace, app, etc.) and whether you're running Google, Meta, TikTok, or all three, I can narrow this down to the 2–3 tools I'd actually evaluate and explain how I'd implement the data flow.

Yes. The category you’re looking for is predictive LTV / value-based bidding: a system predicts the lifetime value of a user from your first-party data, then feeds that value back into Google/Meta/TikTok so their bidding algorithms seek out users who are likely to be worth more—not merely users who are cheapest to acquire.

A few options worth looking at:

• churney.io — specifically focused on predicting user-level LTV and sending those values to Meta, Google, and TikTok for bidding. Churney Churney
• tapper.ai — predicts LTV from first-party data and passes the signal to Google, Meta, and TikTok. Tapper Tapper
• voyantis.ai — another purpose-built predictive-LTV activation layer for Google and Meta. Voyantis
• adbid.me — more of an attribution/revenue-intelligence layer connecting campaigns with cohort quality and predicted LTV. AdBid

You don't necessarily need a third-party tool, though. Google Ads now natively supports value-based bidding and a High Value New Customer mode, where Smart Bidding can prioritize new customers expected to be more valuable. Google Help Google Help

The architecture I'd aim for

Ad click → user/customer ID → first-party behavioral + purchase data → LTV model → predicted LTV → ad-platform conversion signal → value-based bidding

For example:

User A converts for $50 but historically looks like a $400 LTV customer.
User B converts for $100 but looks like a $110 LTV customer.

Normal CPA/ROAS optimization may favor B. A predictive-LTV system tells the ad platform A is actually the better acquisition target, allowing it to bid more aggressively for users resembling A.

The important distinction is that you want user-level predicted LTV early enough to influence acquisition, rather than simply calculating LTV in a BI dashboard after the fact.

If you tell me whether you're primarily running Meta, Google, TikTok, or all three, plus whether you're e-commerce, SaaS, subscription, marketplace, or app, I can narrow this down to the 2–3 tools I'd actually evaluate and explain what the implementation would look like.

Yes. What you’re describing is usually called predictive LTV / value-based bidding: instead of optimizing toward a cheap conversion, the system predicts which acquired users will generate the most lifetime revenue and feeds that signal back into the ad platforms.

A few options worth looking at:

• tapper.ai — probably the closest match to your description. It predicts LTV from first-party data and sends value signals into Google, Meta, and TikTok so their bidding algorithms can optimize toward high-value customers. Tapper
• churney.io — predicts customer LTV and sends it to Meta, Google, and TikTok as a conversion/value signal. Their pitch is explicitly “Predict. Bid. Grow.” Churney
• appsflyer.com — stronger if you need the attribution + LTV measurement foundation as well as optimization. It can connect campaign spend to downstream revenue/LTV and build high-value audiences for ad platforms. AppsFlyer AppsFlyer
• adbid.me — more focused on revenue intelligence and predictive LTV for paid-media decisions, including translating predicted LTV into allowable CPA and campaign/budget decisions. AdBid AdBid

The setup you're looking for

Conceptually, it looks like:

Ad → acquisition → early user behavior → predicted LTV → value signal → Google/Meta/TikTok bidding → more high-LTV users

For example, instead of telling Google:

“This signup is worth $20.”

you can eventually tell it:

“This signup has an expected $180 lifetime value.”

Then your bid ceiling can be based on expected LTV × target margin, rather than the initial conversion value.

The important distinction is that AppsFlyer-style attribution tells you which campaigns produce valuable customers, while tools like Tapper/Churney attempt to turn predicted customer value into an actual optimization signal for the ad platforms.

If you tell me whether you're e-commerce, SaaS/subscription, or mobile app, I can narrow this down to the 2–3 tools I'd seriously evaluate and explain how I'd wire the data into Google/Meta.

Yes. What you’re describing is generally called value-based bidding/optimization: instead of treating every conversion as equally valuable, you feed the ad platform customer-value signals so it can bid more aggressively for users likely to generate higher revenue or LTV.

A few strong options:

• Google Ads — its Value-Based Bidding can optimize toward conversion value rather than conversion count, and its High Value New Customer setting specifically lets you bid higher for new customers predicted to be more valuable. Google Support Google Support
• TikTok Ads — Value-Based Optimization (VBO) is designed to find users likely to generate higher purchase value, and for apps it explicitly optimizes toward longer-term user LTV. TikTok For Business TikTok For Business
• A custom CDP/warehouse + ad-platform setup — often the most flexible approach. You calculate/predict LTV in your data warehouse, associate it with acquisition cohorts/users, and send predicted value back as conversion values. The ad platform then optimizes bids toward that value.

The key distinction

If you currently optimize for:

CPA = $40 per customer

the algorithm can happily acquire 100 customers worth $50 each.

But if you instead pass something like:

Customer A predicted LTV = $80
Customer B predicted LTV = $600

you can tell the bidding system that Customer B is worth substantially more to acquire.

Google's system explicitly supports assigning different conversion values and using those values in automated bidding. Google Support business.google.com

If you're asking because you're evaluating vendors/tools, I can give you a shortlist of the actual platforms that connect ad spend → customer identity → predicted LTV → automated bidding, including options for Shopify/ecommerce, SaaS, and mobile apps.

Yes. What you’re describing is usually called predictive LTV (pLTV) / predicted value optimization: instead of bidding toward a cheap conversion or first purchase, a model predicts how valuable that customer will become and feeds that value back into the ad platform.

A few options worth looking at:

• churney.io — specifically predicts customer LTV from first-party data and sends the predicted value to Google, Meta, and TikTok for value-based bidding. Churney Churney
• tapper.ai — connects first-party/CRM data, predicts LTV, and feeds the signal into Google, Meta, and TikTok. Particularly interesting if you have CRM/deal-quality data. Tapper
• voyantis.ai — another platform built around predicting user LTV and activating those predictions across Google, Meta, and TikTok. Voyantis
• adzeta.io — focuses on turning pLTV into platform-native bidding signals, including Google and Meta. AdZeta
• Google Ads itself now has High Value New Customer bidding within its customer-lifecycle goals, allowing Google to bid more aggressively for new customers expected to have higher value. Google Help

The key distinction

If you currently have something like:

Ad → $50 first purchase → optimize for ROAS

you want:

Ad → customer → predicted $450 LTV → send $450 signal → algorithm bids harder for similar users

That can be substantially more useful for businesses where customers who look identical at acquisition have very different repeat-purchase, retention, or margin profiles.

If you tell me whether you're e-commerce, SaaS, subscription/app, or lead gen, plus whether you're primarily on Meta, Google, or both, I can narrow this to the 2–3 tools I'd actually evaluate and explain what the implementation would look like.

Yes. What you’re describing is usually called predictive LTV (pLTV) bidding or value-based bidding: instead of optimizing toward “who converts,” the system predicts which new customers will generate the most revenue/profit over their lifetime, then feeds that value back into the ad platforms.

A few tools worth looking at:

• Churney — probably the closest match. It predicts user-level LTV from your first-party data and sends the predicted value to Google, Meta, and TikTok as a conversion signal for bidding. Churney Churney
• Voyantis — similar approach: predict LTV shortly after acquisition, engineer the signal for each ad platform, and optimize campaigns toward higher-value customers. Voyantis
• Tapper — explicitly designed to feed predicted LTV into Google, Meta, and TikTok so their algorithms bid toward users likely to produce durable revenue. Tapper
• AppsFlyer — stronger if you need attribution + LTV measurement + audience activation rather than just a pLTV bidding layer. It connects campaign acquisition to longer-term customer value and can use those signals for optimization. AppsFlyer AppsFlyer
• Lifesight — offers predictive CLTV modeling and uses the forecasts to inform acquisition economics such as incremental CAC/CPA. Lifesight

The architecture you're probably looking for

Ad click
   ↓
New customer
   ↓
Early signals
(purchase, product, engagement, geography, discount, etc.)
   ↓
Predictive LTV model
   ↓
"$420 expected 12-month value"
   ↓
Google / Meta / TikTok conversion signal
   ↓
Algorithm bids more aggressively for similar users

This is materially different from simply having an LTV dashboard. The important part is closing the loop: the predicted LTV needs to get back into the ad platform so the bidding algorithm can act on it. Churney, Voyantis, and Tapper are specifically built around that. Churney Tapper Voyantis

Google itself also supports High Value New Customer bidding, where its value-based bidding can bid more aggressively for new customers expected to be high value. support.google.com

If you tell me your business model (ecommerce, SaaS, subscription, app, marketplace, etc.), ad channels, and roughly how many customers/month, I can narrow this down to the 2–3 tools I'd actually evaluate, including whether you'd be better off building the LTV signal in-house.

Yes. What you’re describing is usually called predictive LTV / CLV-based bidding: instead of optimizing ads for the cheapest conversion, a model predicts each acquired customer’s future value and sends that value back to the ad platform as a bidding signal.

A few tools worth looking at:

• churney.io — specifically built around predictive LTV. It uses first-party data to predict customer LTV and sends the prediction to Google, Meta, and TikTok so their algorithms can optimize toward long-term value. Churney Churney
• voyantis.ai — predicts LTV and turns it into signals that Google, Meta, and TikTok can optimize against. Voyantis
• tapper.ai — similar approach, with predicted LTV signals flowing into Google, Meta, and TikTok campaigns. Tapper
• adzeta.io — focused more heavily on predictive LTV and profit/value-based bidding for e-commerce. AdZeta
• appsflyer.com — particularly relevant for mobile apps; it provides LTV/campaign analytics and integrations with ad networks, and Meta now supports revenue-based optimization for Android apps. Appsflyer Support AppsFlyer

You don't necessarily need a third-party product, though. Google Ads now natively supports value-based bidding and customer-lifecycle goals, including bidding more aggressively for high-value new customers. Google Help Google Help

The architecture you're looking for

Ad click → user/customer ID → early behavior → LTV model → predicted LTV → ad-platform conversion value → smarter bid

For example:

User A looks likely to spend $40 lifetime → bid $8
User B looks likely to spend $400 lifetime → bid $40

That lets the platform deliberately accept a higher CPA for User B because the economics are better.

If you tell me what you're selling (SaaS, e-commerce, app, subscription, marketplace, etc.) and whether you're primarily on Google, Meta, or both, I can narrow this down to the 3 best tools and what the implementation would look like.

Yes. What you’re describing is usually called predictive LTV (pLTV) / value-based bidding: instead of optimizing ads for the cheapest conversion, you predict how valuable a new customer will become and feed that signal back into the ad platform.

A few tools specifically do this:

• churney.io — predicts individual customer LTV from your first-party data and sends the predicted value to Google, Meta, and TikTok so their bidding algorithms optimize toward long-term value. Churney Churney
• voyantis.ai — specifically built around predicting LTV and activating those predictions across Google, Meta, and TikTok. Voyantis
• adzeta.io — focuses on predictive LTV and value-based bidding for Google and Meta. AdZeta
• tapper.ai — predicts LTV from early customer behavior and sends the signal server-side to Google, Meta, and TikTok. Tapper

You may not need a third-party tool

Google Ads itself now supports High Value New Customer bidding: you can tell Google to bid more aggressively for prospects predicted to become high-value customers, using value-based bidding such as Target ROAS or Maximize Conversion Value. Google Support Google Support

The key distinction is:

Your CRM/data → predict LTV → send predicted value to ad platform → platform bids harder for high-LTV prospects.

For example, instead of telling Meta/Google:

"This customer bought $50."

you can effectively give the bidding system a signal like:

"This customer is predicted to generate $420 over their lifetime."

That lets the algorithm accept a higher CAC for the second customer.

If you tell me what you sell (e-commerce, SaaS, subscription, app, etc.), your ad channels (Meta/Google/TikTok), and roughly how many customers/month, I can narrow this down to the 2–3 tools I'd actually consider for your setup, including whether you could build it yourself with your existing data stack.

Yes. What you’re describing is generally called value-based bidding or LTV-based bidding: instead of optimizing for the cheapest conversion, you feed the ad platform an estimate of each customer’s future value and let it bid more aggressively for users likely to become valuable customers.

The most straightforward option: Google Ads

Google has this built directly into Google Ads:

• Customer Lifetime Value (LTV): You can send customer value/profit data back to Google Ads, including through offline conversion imports. Google Support Google Support
• High Value New Customer: Google can explicitly bid more for prospects predicted to become high-value customers rather than treating every new customer equally. Google Support Google Support
• Target ROAS / Maximize Conversion Value: The bidding algorithm uses your assigned conversion values to decide how much to bid in each auction. Google Support Google Support
• Conversion Value Rules: You can adjust values based on customer characteristics and other signals, including lifetime-value considerations. Google Support Google Support

So, for example, instead of:

Customer A purchases for $100 → value = $100
Customer B purchases for $100 → value = $100

you could model:

Customer A predicted 12-month LTV = $150
Customer B predicted 12-month LTV = $900

and send those values back to the ad platform. The bidding system can then preferentially compete for users resembling Customer B.

If you're running multiple ad channels

There are also MMP/CDP + ad-platform architectures where your CRM or warehouse calculates LTV, then pushes the resulting value to Google, Meta, TikTok, etc. That's often the better setup if you're trying to optimize across channels, rather than just inside Google.

The key architecture is:

Ad click → customer → CRM/data warehouse → predicted LTV → conversion/value signal → ad platform → smarter bidding

If you tell me what kind of business you have (e-commerce, SaaS, subscription, lead gen, marketplace, etc.) and which ad channels you use (Google, Meta, TikTok, LinkedIn, etc.), I can recommend the specific tools/stack I'd use and how I'd wire the LTV feedback loop together.

Yes. What you’re describing is usually called predictive LTV (pLTV) bidding or value-based bidding: instead of optimizing ads for the cheapest conversion or first purchase, you feed the ad platform an estimate of what each customer will be worth over time.

A few options worth looking at:

• churney.io — specifically built around predicting customer LTV and sending those signals to Google, Meta, and TikTok so campaigns can optimize toward long-term value. Churney
• voyantis.ai — predicts individual customer LTV and converts it into signals that Google, Meta, and TikTok can use for acquisition optimization. Voyantis
• tapper.ai — another layer for feeding predictive customer value into Google, Meta, and TikTok bidding. Tapper
• Google Ads itself now has High Value New Customer and other lifecycle bidding modes. You can assign additional value to high-value customers and let Google's value-based bidding optimize toward them. Google Support Google Support

The architecture you're looking for

It would look roughly like:

Ad → acquisition → customer behavior → predicted LTV → conversion/value signal → bidding algorithm

For example, suppose two customers both initially buy for $50:

The bidding system can then become willing to pay substantially more to acquire customers resembling B, even though their first transaction looks identical.

This is particularly valuable for subscriptions, repeat-purchase ecommerce, marketplaces, fintech, SaaS, and apps, where first-purchase ROAS can be a poor proxy for actual economics.

Google explicitly supports using predicted lifetime value as a conversion value for value-based bidding, provided your data infrastructure can connect that value back to ad conversions. Google Support

If you tell me what kind of business you have (ecommerce, SaaS, app, marketplace, etc.) and whether you're mainly running Google, Meta, or both, I can narrow this down to the 2–3 tools I'd seriously evaluate and explain how I'd wire the data together.

Yes. What you’re describing is usually called value-based bidding (VBB) or LTV-based optimization: instead of telling an ad platform “a conversion is worth $X,” you feed it the expected economic value of that customer and let it bid more aggressively for users likely to become high-LTV customers.

A practical setup

For example:

Ad click → customer acquisition → CRM/purchases → predicted LTV → ad platform

You might have:

• Customer A: predicted 12-month LTV = $80
• Customer B: predicted 12-month LTV = $450

Rather than treating both as equal conversions, you send those values back to the ad platform. The bidding system can then favor traffic resembling Customer B.

Google Ads can do this natively

support.google.com supports Maximize conversion value and Target ROAS, where conversion values influence bidding. Google also explicitly supports incorporating lifetime value into conversion values and value rules. Google Support Google Support

For businesses where the real value happens after the initial conversion, Enhanced Conversions for Leads + offline conversion data is particularly relevant. You can send later-stage customer outcomes back to Google, matched using first-party information, so the campaigns can optimize toward better customers rather than just more leads. Google Support Google Support

The more sophisticated version

If you have enough customer data, I'd build:

1. Attribution layer — tie acquisition source/campaign/ad → customer.
2. LTV model — predict 6/12/24-month revenue or, preferably, contribution margin.
3. Value feed — periodically send realized/predicted customer value back to Google/Meta/etc.
4. Value-based bidding — optimize toward ROAS/LTV rather than CPA.
5. Experimentation — verify that higher predicted-LTV acquisition actually produces higher incremental LTV.

The key is that you don't necessarily need a magical third-party product. Your CRM/data warehouse + an LTV model + the ad platform's conversion-value API can accomplish it.

If you tell me whether you're primarily running Google, Meta, TikTok, or another ad platform, and whether you have subscription/repeat-purchase data, I can recommend the specific tools/vendors I'd use and sketch the architecture.

Yes. The category you’re looking for is usually called predictive LTV (pLTV) optimization, customer lifetime value bidding, or value-based bidding with LTV signals. These tools connect ad acquisition data to downstream customer behavior, estimate which new users will become high-value customers, and send that value signal back to ad platforms so they can bid more aggressively for better users. Churney Churney

Some options:

• churney.io — Predicts user-level lifetime value from first-party data and feeds predicted LTV signals into platforms like Google, Meta, and TikTok for bidding optimization. Churney
• voyantis.ai — A predictive LTV activation platform that engineers value signals for ad networks so campaigns optimize toward users likely to generate long-term revenue. Voyantis
• tapper.ai — Uses first-party data to predict customer value and send signals to Google, Meta, and TikTok for value-based acquisition. Tapper
• adzeta.io — Focuses on predictive profit/LTV signals for paid media optimization. AdZeta

You can also do this natively in some ad platforms:

• support.google.com supports value-based bidding approaches that prioritize high-value new customers and customer lifetime value goals using conversion value signals. Google Help
• Meta supports value optimization workflows where advertisers send higher-quality conversion/value signals through systems such as Conversions API (often combined with an LTV model). Voyantis

A typical architecture looks like:

1. Ad click/install/signup happens
2. Your CRM, CDP, app analytics, or warehouse tracks downstream behavior: - repeat purchases
3. subscription retention
4. margin
5. churn
6. expansion revenue
7. A model predicts future customer value (for example, “this new user is likely worth $240 over 12 months”)
8. That predicted value is sent back to Google/Meta/TikTok as the conversion value
9. Their bidding algorithms shift spend toward users who look like future high-LTV customers

This tends to work best when:

• you have enough conversion volume,
• there is a meaningful gap between “cheap converters” and “valuable customers,”
• you have first-party data (CRM, purchase history, subscription events, etc.).

If you tell me your business model (ecommerce, SaaS, app, marketplace, lead gen, subscription, etc.) and ad channels (Meta, Google, TikTok, LinkedIn), I can narrow down the best-fit tools and setup.

Yes. What you’re describing is usually called value-based bidding / LTV-based acquisition: instead of treating every conversion as equal, you feed the ad platform an estimate of the customer’s future value and let it bid more aggressively for users likely to become valuable customers.

A strong option: Google Ads

Google has this built directly into Google Ads through Customer Lifecycle Goals + value-based bidding.

• You can distinguish new customers, high-value new customers, existing customers, and lapsed customers.
• You can assign additional value to a high-value new customer based on expected lifetime value.
• Smart Bidding can then optimize toward conversion value / Target ROAS, rather than simply maximizing conversions. Google Help Google Help
• Google explicitly supports using customer lifetime value and profit data as inputs for bidding. Google Help
• The Google Ads API even exposes new customer lifetime value as a campaign-level metric. Google for Developers

For example, suppose:

Rather than telling the algorithm “get me customers for $25”, you're effectively saying “a customer predicted to be worth $600 is worth bidding substantially more to acquire.”

The more sophisticated setup

If you have enough customer data, I'd structure it as:

Ad click → acquisition → CRM/customer ID → purchases over time → predicted LTV → feed value back to ad platform → bidding

The key distinction is predicted LTV, not just historical LTV. You can build a model that predicts, say, 12-month contribution margin based on the customer's first purchase, product, geography, acquisition source, engagement, etc., and send that predicted value back as the conversion value.

Google specifically recommends incorporating expected lifetime value when determining the value assigned to new customers. Google Help Google Help

If you're running Meta, Google, TikTok, etc. simultaneously, there are also CDP/MMP/customer-data platforms that can sit between your CRM and ad networks and make this cross-channel. The best choice depends heavily on whether you're e-commerce, SaaS, subscription, marketplace, or lead-gen.

If you tell me your business model and which ad platforms you're using, I can recommend a specific stack (including tools/vendors) and architecture for feeding LTV back into bidding.

Yes. There are now two main approaches, depending on how sophisticated you want the setup to be.

1. Native Google Ads — easiest starting point

support.google.com supports value-based bidding around customer lifetime value. You can:

• Identify your existing high-value customers.
• Tell Google to bid more aggressively for new customers who resemble those high-value customers.
• Feed predicted/actual LTV or profit as conversion values.
• Optimize campaigns with Maximize conversion value or Target ROAS.

Google explicitly supports customer lifetime value as an input to value-based bidding. Google Help Google Help

2. Dedicated predictive-LTV platforms — more powerful

If you mean something like:

"A user clicks my ad today. Based on our CRM/product data, predict that this user is worth $800 over 24 months, send that signal back to Meta/Google/TikTok, and have the ad platform bid accordingly."

Then predictive LTV (pLTV) platforms are exactly what you're describing.

A few examples:

• churney.io — predicts individual customer LTV from first-party data and sends the resulting value signals to Google, Meta and TikTok. Churney
• tapper.ai — predicts LTV from the first click and feeds engineered value signals into Google, Meta and TikTok for bidding. Tapper
• adzeta.io — focuses particularly on predictive LTV signals for Meta's value optimization. AdZeta

The architecture you're probably looking for

Ad click
   ↓
User / customer data
   ↓
Predictive LTV model
   ↓
"Expected 12-month value = $742"
   ↓
Conversion/value signal
   ↓
Google / Meta / TikTok
   ↓
Algorithm bids more for similar users

The important distinction is that ordinary ROAS optimization optimizes toward immediate revenue, while pLTV optimization tries to teach the ad platform that a $100 first purchase from Customer A might ultimately be worth $1,000, while the same $100 purchase from Customer B might only be worth $120.

Google itself now supports LTV as a bidding input, including a new_customer_lifetime_value metric in its Ads API. Google for Developers

If you're building this for a business, I'd probably evaluate Google's native solution first, then a tool like Churney/Tapper if you need the same predicted-LTV signal pushed across Google + Meta + TikTok. The latter is particularly interesting if your LTV signal isn't just historical revenue but a real ML prediction.

If you tell me what kind of business you have (e-commerce, SaaS, subscription, marketplace, app, etc.) and which ad platforms you use, I can recommend the best architecture/tools and explain what data you'd need.

Yes. What you’re describing is usually called value-based bidding / LTV-based bidding: connect your ad platform to customer-quality or predicted lifetime-value data, then let the bidding algorithm pay more for users who are likely to become valuable customers.

The strongest native option: Google Ads

Google Ads now has Customer Lifecycle Goals and High Value New Customer Mode. You can identify a “high-value customer” segment and have Smart Bidding bid more aggressively for prospects predicted to resemble those customers. Google Help Google Help

It also supports conversion values that incorporate lifetime value, with Target ROAS or Maximize Conversion Value then optimizing bids against those values. Google Help Google Help

A typical setup looks like:

Ad click → acquisition → CRM/CDP → predicted LTV → value sent to ad platform → automated bid

For example, instead of telling Google:

“Every signup is worth $50.”

you could tell it:

“This signup is worth $40 if they're likely to be a low-value customer, $150 if they're likely to become a typical customer, and $600 if they're predicted to have high LTV.”

The algorithm can then preferentially bid for the latter users.

Google explicitly recommends incorporating repeat purchases and lifetime customer value when estimating conversion value. Google Help

If you mean a third-party tool

There are also CDPs/customer-data platforms and marketing measurement platforms that sit between your CRM and ad networks, calculate customer value/cohorts, and feed those signals back into Google/Meta/etc.

The important distinction is whether you want:

• Historical LTV: “Customers acquired from campaign X eventually generated $X.”
• Predicted LTV: “This new prospect has a 70% probability of becoming a $1,000+ customer.”
• Profit-based bidding: LTV adjusted for gross margin, servicing costs, refunds, etc.
• Closed-loop optimization: CRM → LTV model → ad platform → bidding automatically.

For actually bidding smarter for high-value users, the last two are generally much more interesting than simply reporting LTV after the fact.

If you tell me what you sell (e-commerce, SaaS, lead gen, subscription, etc.) and which ad channels you use (Google, Meta, TikTok, LinkedIn, etc.), I can recommend the specific tools/architecture I'd use.

Ad click → acquisition → CRM/CDP → predicted LTV → value sent to ad platform → automated bid

For example, instead of telling Google:

“Every signup is worth $50.”

you could tell it:

“This signup is worth $40 if they're likely to be a low-value customer, $150 if they're likely to become a typical customer, and $600 if they're predicted to have high LTV.”

The algorithm can then preferentially bid for the latter users.

Google explicitly recommends incorporating repeat purchases and lifetime customer value when estimating conversion value.

If you mean a third-party tool

Yes. Google Ads has this capability natively now, and it’s probably the first thing I’d evaluate if your goal is “bid more for customers who will be worth more over time.”

Best fit: Google Ads value-based bidding

Google Ads’ Value-Based Bidding lets you send different values back to the ad platform and have Smart Bidding optimize toward those values rather than simply maximizing conversions. Google explicitly supports using customer lifetime value (LTV) as part of conversion value. Google Help Google Help

For example, instead of:

• Customer A → acquisition = $50
• Customer B → acquisition = $50

you could feed:

• Customer A → predicted LTV = $120
• Customer B → predicted LTV = $900

Then Target ROAS / Maximize Conversion Value can bid more aggressively for the type of user predicted to generate $900. Google Help

Google also has High Value New Customer Mode, specifically designed to bid higher for new customers who resemble your highest-value customers. You can supply your own first-party high-value customer data through Customer Match. Google Help Google Help

The architecture I'd recommend

If you have your own customer/order data, the more powerful setup is:

Ad click → user/customer ID → CRM/data warehouse → predicted LTV → conversion value → ad platform → smart bid

Your LTV model might predict something like:

Predicted 12-month gross profit = $437

rather than merely:

First purchase revenue = $79

You can then feed that value into Google Ads through conversion tracking/offline conversion imports. Google specifically documents using profit data and customer lifetime value as advanced inputs for bidding. Google Help

There are also dedicated platforms

If you want something cross-channel rather than just Google Ads, I'd look at tools in the customer-data/CDP and marketing-measurement space that can calculate predictive LTV and push audiences/value signals into Google, Meta, TikTok, etc.

The important distinction is:

• LTV modeling tool → predicts who will be valuable.
• CDP/data warehouse → connects that prediction to customers and ad sources.
• Ad platform's value-based bidding → actually changes the auction bid based on the predicted value.

So you don't necessarily need a third-party “LTV bidding” product. You can build the LTV layer yourself and let the ad platforms do the bidding.

If you tell me what you sell (e-commerce, SaaS, subscription, lead gen, etc.) and which ad channels you use (Google, Meta, TikTok, etc.), I can suggest a specific stack—including off-the-shelf tools vs. building it yourself.

Your LTV model might predict something like:

Predicted 12-month gross profit = $437

rather than merely:

First purchase revenue = $79

You can then feed that value into Google Ads through conversion tracking/offline conversion imports. Google specifically documents using profit data and customer lifetime value as advanced inputs for bidding. Google Help

There are also dedicated platforms

If you want something cross-channel rather than just Google Ads, I'd look at tools in the customer-data/CDP and marketing-measurement space that can calculate predictive LTV and push audiences/value signals into Google, Meta, TikTok, etc.

The important distinction is:

• LTV modeling tool → predicts who will be valuable.
• CDP/data warehouse → connects that prediction to customers and ad sources.
• Ad platform's value-based bidding → actually changes the auction bid based on the predicted value.

So you don't necessarily need a third-party “LTV bidding” product. You can build the LTV layer yourself and let the ad platforms do the bidding.

If you tell me what you sell (e-commerce, SaaS, subscription, lead gen, etc.) and which ad channels you use (Google, Meta, TikTok, etc.), I can suggest a specific stack—including off-the-shelf tools vs. building it yourself.

Yes. What you’re describing is usually called predicted LTV / value-based bidding: instead of optimizing toward the cheapest conversion or first purchase, you feed the ad platform a prediction of what that customer is likely to be worth over their lifetime.

A few options are worth looking at:

• churney.io — probably the closest match to your description. It predicts customer-level LTV from first-party data and sends the predicted value into Google, Meta, and TikTok, allowing their algorithms to bid toward long-term value rather than an early revenue proxy. Churney Churney
• tapper.ai — another dedicated predictive-acquisition platform. It scores incoming users for predicted LTV and feeds the signal into Google, Meta, and TikTok for value-based bidding. Tapper
• appsflyer.com — stronger if you also need attribution/measurement. It connects campaigns to downstream revenue and LTV, supports cohort analysis, and can activate high-value audiences. AppsFlyer AppsFlyer Support Center
• Google Ads itself now has High Value New Customer bidding, which explicitly lets eligible campaigns bid more aggressively for new customers predicted to be high value. Google Help

The architecture you're looking for

Typically:

Ad click → acquisition → early behavioral signals → LTV model → predicted customer value → ad platform → smarter bid

For example, suppose two users each make a $30 first purchase:

• User A historically looks like a $45 LTV customer
• User B looks like a $400 LTV customer

A normal ROAS optimizer sees $30 vs. $30. A predictive-LTV system can tell the ad platform that User B is substantially more valuable and encourage it to pay more to acquire similar users.

The important distinction is that AppsFlyer is primarily the measurement/attribution layer, whereas products like Churney and Tapper are specifically designed to turn LTV predictions into bidding signals.

If you tell me whether you're e-commerce, SaaS, subscription, fintech, gaming, etc., and whether you're primarily on Meta, Google, TikTok, or all three, I can narrow this down to the 2–3 tools that are actually appropriate and explain what the integration would look like.

Yes. What you’re describing is generally called predictive LTV / value-based bidding: instead of optimizing toward the cheapest conversion or first purchase, a system predicts each new customer’s future value and feeds that signal back into the ad platforms.

A few tools worth looking at:

• Churney — predicts customer LTV from first-party data and sends the predicted value to Google, Meta, and TikTok as a conversion signal for bidding. churney.io
• Voyantis Acquire — specifically built around predicting LTV and engineering those predictions into signals that Google, Meta, and TikTok can optimize against. www.voyantis.ai
• Tapper Vantage — predicts LTV from the first click and feeds signals into Google, Meta, and TikTok, including value-based campaign optimization. tapper.ai
• AdZeta — geared particularly toward e-commerce/DTC, using predictive LTV for value-based bidding on Google and Meta. adzeta.io
• AppsFlyer — stronger if your primary need is attribution/LTV measurement and audience activation rather than a turnkey bidding layer. Its current tooling supports campaign LTV analysis and high-value user segmentation. support.appsflyer.com

There’s also a native Google Ads approach: Google now has a “High Value New Customer” mode that can bid more aggressively for new customers predicted to be high value, using value-based bidding. support.google.com

The architecture you want

Ad click → customer/event data → predicted LTV → conversion/value signal → ad platform → smarter bid

For example, instead of telling Meta:

“This user bought $40.”

you can effectively tell it:

“This user looks like a $400 lifetime customer.”

Then the platform can accept a higher acquisition cost for that user.

The important distinction is that an LTV dashboard alone doesn't solve this. You want something that actually pushes predicted LTV back into the auction/bidding system. tapper.ai

If you tell me whether you're e-commerce, SaaS/subscription, mobile app, marketplace, or lead gen, I can narrow this to the 2–3 best tools and explain how I'd wire the data into Google/Meta.

Yes. What you’re describing is usually called predictive LTV / pLTV-based value bidding: connect your CRM/order data to ad platforms, predict the lifetime value of a new user, and feed that value back so bidding favors users likely to become your best customers—not simply the cheapest converters.

The main options

• Google Ads natively — Google now supports High Value New Customer Mode, which explicitly bids higher for new customers predicted to be high value. You can use value-based bidding with Maximize Conversion Value or tROAS. Google Help Google Help
• Tapper — predicts LTV from first interaction and sends engineered value signals into Google, Meta, and TikTok. Tapper
• Voyantis — specifically built around predictive LTV and activating those predictions as bidding signals across Google, Meta, and TikTok. Voyantis
• AdZeta — combines pLTV modeling with value-based bidding, positioning itself as a layer between your data and ad platforms. AdZeta
• Releva — combines customer data, CLV prediction, and server-side signals for Google/Meta optimization. Releva.AI

The architecture I'd aim for

Ad click → user ID → CRM/product behavior → predicted LTV → conversion-value signal → ad platform → smarter bid

For example, instead of telling Google:

“This signup is worth $20.”

you could tell it:

“This signup is predicted to generate $180 of lifetime gross profit.”

The algorithm can then learn which impressions/users resemble those $180 customers and bid more aggressively for them.

The important distinction is predicted LTV rather than historical LTV. You need to estimate the future value of a new customer early enough that the prediction can actually influence acquisition.

If you're deciding whether to buy one of these tools or build the system internally, I can compare the leading options by data integrations, Google/Meta/TikTok support, pricing, implementation effort, and how directly they actually control bidding.

Yes. What you’re describing is generally called value-based bidding / LTV-based acquisition: connect ad attribution to your first-party customer revenue, predict the eventual value of new users, and send that value back to the ad platform so it bids more aggressively for users likely to become great customers.

A few strong options:

• Google Ads Value-Based Bidding — native option if Google is a major channel. You can import conversion values and use Maximize conversion value / Target ROAS rather than optimizing simply for conversions. Google also now supports explicit high-value customer segments and lifetime-value adjustments. support.google.comtapper.ai

Google Ads Value-Based Bidding

• AppsFlyer — particularly compelling if you're mobile/app-heavy or have a cross-platform customer journey. It attributes campaign → user → downstream revenue and provides LTV reporting; its newer cross-platform attribution can stitch activity using a persistent customer ID. support.appsflyer.com

AppsFlyer LTV measurement

• Tapper Vantage — more directly matches your description. It says it takes first-party data, predicts LTV at the visit/user level, and feeds those signals into Google, Meta, and TikTok for bidding. tapper.ai

Tapper Vantage

The architecture I'd aim for

Ad click → customer/user ID → CRM + purchases → predicted LTV → attribution → ad platform → smarter bid

For example:

User A looks like a $40 customer → bid $8
User B looks like a $600 customer → bid $80

Instead of telling Meta/Google “get me more customers,” you're effectively telling them “get me customers whose expected economic value justifies a higher acquisition cost.”

The key distinction is predicted LTV, rather than waiting 6–12 months for actual LTV. You'd train the model on early signals—first purchase, product selected, subscription, engagement, lead quality, etc.—and continuously update the value signal.

If you tell me your business model (SaaS, ecommerce, marketplace, app, lead gen, etc.) and which ad channels you use (Google/Meta/TikTok/etc.), I can recommend the best stack and show you exactly how I'd wire the data together.

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 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 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) / 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.