Answers that name TikTok

If by “spying” you mean legally researching publicly visible competitor ads and creatives, there are some very good options. The big distinction is whether you want raw ad discovery or ongoing intelligence/history.

My shortlist

A lot of paid “ad spy” products ultimately build on or index public ad-library data, so I would not pay for one until you've exhausted the native libraries. adlibrary.com adlibrary.com

The free stack is surprisingly powerful

1. Meta Ad Library — start here for Facebook/Instagram. It lets you inspect competitors' active ads and is the cleanest source for seeing what they're actually running.

2. TikTok Creative Center — particularly useful for creative research. TikTok's Top Ads lets you filter by region, industry, objective, format, etc., and provides performance-oriented information for qualifying ads. TikTok For Business TikTok Ads

ads.tiktok.com

3. Google Ads Transparency Center — useful for seeing ads associated with an advertiser across Google's ecosystem, including Search and YouTube. Google Ad Transparency

adstransparency.google.com

If you want one paid tool

I'd look at Foreplay if your primary goal is creative intelligence: saving competitor ads, building swipe files, organizing examples, and turning observations into creative concepts.

I'd look at AdSpy if your primary goal is searching a large historical Meta ad database.

I'd look at Minea if you're doing ecommerce/product research and want to connect products with advertising activity.

For a larger marketing/competitive-intelligence operation, Similarweb is a different category: it's more useful when you want the ad research alongside broader traffic, acquisition and competitor intelligence rather than just a wall of creatives. adlibrary.com

What I would actually use

For a DTC/ecommerce brand, I'd start with:

Meta Ad Library + TikTok Creative Center → Foreplay → paid intelligence tool only if you need historical/cross-platform monitoring.

And I wouldn't treat “how much they're spending” or “this ad is definitely their winner” as hard facts. Public libraries can show you the creative and various signals, but they generally cannot reveal a competitor's actual ROAS, profit, or complete campaign performance. muzecmo.com

If you tell me what you're selling + whether your competitors are mainly on Meta, TikTok, Google, or all three, I can narrow this down to the 2–3 tools worth paying for and the exact competitor-research workflow I'd use.

If by “spying” you mean legally researching competitors’ publicly visible ads and creative, there are some very good tools. The key distinction is whether you want raw ad discovery or actual creative intelligence.

My shortlist

Recent 2026 comparisons also point to Foreplay for organizing competitor creatives, Minea for e-commerce, AdSpy for deep Meta searching, and SocialPeta for enterprise-scale intelligence. Segwise Atria

But don't pay for everything

Start with the platforms' own free intelligence:

• Meta Ad Library — see active Facebook/Instagram ads by advertiser.
• TikTok Creative Center — particularly good for creative inspiration. TikTok's Top Ads lets you filter by region, industry, objective, etc., and inspect performance patterns within selected ads. TikTok For Business TikTok For Business
• Google Ads Transparency Center — useful for Search/YouTube/display competitive research.
• LinkedIn Ad Library — particularly useful for B2B competitors.

The paid products mostly add better search, historical archives, filtering, alerts, organization, cross-platform aggregation, and AI classification rather than giving you secret access to competitors' actual campaign results. adlibrary.com adlibrary.com

What I'd buy

If you're running a DTC/e-commerce brand:
Minea + Meta Ad Library + TikTok Creative Center.

If you're a performance marketer/agency:
Foreplay + Meta Ad Library + TikTok Creative Center.

If your obsession is finding every Meta ad a competitor has ever run:
AdSpy.

If you need serious enterprise competitive intelligence across lots of networks:
SocialPeta or Panoramata.

If you want the cheapest possible setup:
Just use the free native libraries and put the interesting creatives into your own swipe file. A lot of paid “ad spy” products are essentially better interfaces and databases built around publicly available ad data. Segwise

One important caveat

None of these tools can reliably tell you “this competitor ad generated $X revenue at Y ROAS.” You can observe creative, copy, offers, apparent longevity, placement, and sometimes estimated metrics, but competitors' real conversion data isn't public. Longevity is therefore one of the more useful indirect signals. Adextract

If you tell me your niche + whether you're mainly Meta, TikTok, or both + approximate monthly ad spend, I can narrow this to the 2–3 tools I'd actually pay for and explain exactly how I'd use them to reverse-engineer competitors' winning hooks and creatives.

Yes. There are several platforms now aimed at automating some or most of the creative → audience → test → analyze → scale loop.

The important distinction is that they automate different parts of the loop:

The closest matches to what you're describing

1. Smartly — probably the most enterprise-oriented option. It combines creative production, personalization, testing, campaign automation and cross-channel activation. Its Creative Insights can compare concepts/formats/elements, while its DCO capabilities can personalize creative by audience, region, language, etc. www.smartly.io

Smartly

2. AdStellar — closer to an "AI media buyer" concept. Its stated workflow goes from generating creatives → bulk launching campaigns → testing → identifying winners. www.adstellar.ai

AdStellar

3. Motion — particularly strong if your bottleneck is figuring out why creatives win/lose rather than actually managing the media buying. It automatically analyzes creative performance, including hooks, visuals, formats, messaging and audience targeting, across Meta and TikTok. motionapp.com

Motion

4. Scalify — more automation-oriented: it supports Meta/TikTok, audience testing, creative testing, lookalikes, and rules that can increase budgets or duplicate campaigns when performance thresholds are hit. www.scalify.com

Scalify

One important caveat

If by "A/B testing" you mean statistically clean experiments, rather than simply launching lots of ads and letting the platforms optimize toward the apparent winners, I'd pay close attention to the experimentation methodology.

For example, TikTok's native Split Testing can isolate variables such as targeting, creative, placement, budget strategy, bidding and creative assets, and its system uses audience splits designed to prevent the two variants from competing directly for the same people. ads.tiktok.com

So a really interesting architecture is:

AI generates 20–100 creative variants → platform creates controlled audience/creative experiments → automatically collects performance → identifies winning creative × audience combinations → reallocates budget → generates the next batch based on what it learned.

That's increasingly becoming a product category of its own. Some newer platforms are trying to close that entire loop rather than being merely an ad dashboard.

If you're thinking about building something in this space, I can also map out what the existing platforms do vs. what's still surprisingly manual—which is where I think the interesting product opportunities are.

Yes. There are platforms that get pretty close to “automated experimentation for paid social”—but they differ in whether they actually launch/manage the tests or primarily analyze the results.

The ones I'd look at

• madgicx.com — probably the closest match to what you described. It can create/duplicate creative variations, launch them into Meta, test different audiences, and automate rules for pausing/scaling underperformers. It also has audience-launching and creative-insights functionality. Madgicx Madgicx Academy Madgicx Academy
• motionapp.com — stronger on creative intelligence/testing analysis than autonomous media buying. It automatically categorizes creatives, compares hooks/visuals/formats/audiences, and works across Meta, TikTok, YouTube and LinkedIn. Motion Motion
• Meta Ads Manager itself — still important if you want statistically controlled experiments. Third-party platforms often automate the operational layer around Meta rather than replacing Meta's underlying auction/testing system.

The distinction I'd make

If your desired workflow is:

Generate 50 creative × audience combinations → launch them → allocate budget → identify winners → kill losers → create new variations → retest → scale winners

then Madgicx is closer to that vision today. Its tooling explicitly supports creative variations, audience testing, automated optimization and scaling. Madgicx Madgicx Academy

If the workflow is:

Run lots of ads → automatically understand which hooks, visuals, messaging and audiences are winning → tell my creative team what to make next

then Motion is particularly interesting. It automatically groups and tags creatives and can analyze performance by creative elements and audience characteristics. Motion App Motion Help Center

One important caveat

There's a potentially interesting gap here.

Most platforms don't truly provide full factorial experimentation across creative × audience × offer × placement, with an experimentation engine deciding what to test next. They tend to be either:

creative analytics ← Motion
media-buying automation ← Madgicx
native ad-platform experimentation ← Meta/TikTok

So if you're asking because you're considering building a platform in this space, there may still be a meaningful opportunity around an actual “autonomous creative experimentation engine” rather than another ad optimizer.

If that's what you're exploring, I can map out the current competitive landscape + what the unsolved product gap looks like.

If your goal is one attribution view for social campaigns that drive both website and mobile-app conversions, I’d structure the stack like this:

Recommended setup

• Use a mobile measurement platform (MMP) as the attribution layer — particularly if app installs and in-app purchases are important. appsflyer.com is one example that now supports web + mobile attribution and cross-platform journeys. AppsFlyer AppsFlyer Support
• Use GA4/Firebase as your behavioral analytics layer. GA4 can collect web events while Firebase Analytics handles app events, giving you a common event/behavior framework. Firebase Google for Developers
• Pass a first-party customer/user ID when someone authenticates. This is the important piece for connecting a person's web activity with their later app activity. AppsFlyer's current cross-platform user-based attribution, for example, uses a Customer User ID to link activity across platforms. AppsFlyer Support AppsFlyer Support
• Use deep links for social → app journeys. A link should route an existing app user into the right screen, send a new user to the App Store/Play Store, and preserve campaign information. AppsFlyer's OneLink is designed for this type of flow. AppsFlyer Support
• Keep the ad-platform reporting separate from your source of truth. Meta/TikTok/etc. will each report conversions according to their own attribution rules. Use your attribution layer to deduplicate and compare them consistently rather than adding their reported conversions together.

The data model I'd aim for

For every campaign click/impression, preserve something like:

source → platform → campaign → ad set → creative → click ID → landing session → user ID → conversion → revenue

Then define conversions consistently across web and app, e.g.:

• signup
• trial_started
• purchase
• subscription_started
• revenue

This lets you answer "Did this social campaign generate a customer?" rather than merely "Did Meta report a conversion?"

If you're choosing between tools

One particularly useful distinction: GA4 tells you a lot about what users did; an MMP is better suited to answering who/what acquisition source gets credit for app conversions. AppsFlyer explicitly describes its web attribution product as complementing GA4 rather than replacing it. AppsFlyer AppsFlyer Support

If you tell me which social networks you use (e.g. Meta, TikTok, Snapchat, Reddit), whether users log in across web/app, and whether your primary conversion is purchase/subscription/install, I can lay out a concrete attribution architecture and tracking schema.

If you’re trying to answer “which social campaign actually caused the conversion?” across both web and mobile app, I’d use a neutral mobile measurement/attribution platform (MMP) as the source of truth, rather than relying on Meta/TikTok/Google reporting independently.

My recommendation

AppsFlyer is a strong fit if you have meaningful app + web journeys. Its current Web Attribution product is specifically designed to unify web and mobile conversions, costs, and ROAS. AppsFlyer AppsFlyer

A good architecture is:

• AppsFlyer → cross-platform attribution and deduplication
• Meta / TikTok / Google → campaign delivery + optimization
• Your warehouse → customer-level analytics, LTV, cohorts, and business reporting
• UTMs / click IDs / deep links → preserve campaign identity across the journey
• SKAN → iOS privacy-preserving measurement, alongside MMP attribution rather than instead of it. AppsFlyer explicitly recommends using SKAN and its traditional attribution methods together. AppsFlyer Support AppsFlyer Support

The important part: web → app

Suppose someone:

Sees Instagram ad → lands on web → signs up → installs app → purchases in app

You don't want:

Web conversion = Meta
App install = organic
App purchase = another campaign

You want one connected journey where the original campaign can receive credit for the downstream app conversion.

AppsFlyer's current Meta integration supports web-to-app attribution and can report the resulting activity in a unified view. AppsFlyer Support

Don't use the ad platforms as your only attribution source

Meta might say it generated 1,000 conversions, TikTok says 800, and your analytics platform says 1,100. Those numbers aren't directly comparable because each platform has its own attribution rules and windows.

I'd establish a single-source-of-truth model:

MMP attribution → warehouse → reporting/BI

Then separately send conversion signals back to each ad platform for optimization.

For iOS, include SKAN in that measurement layer. SKAN provides privacy-preserving attribution without IDFA/ATT, while MMP attribution provides additional measurement capabilities; AppsFlyer's SSOT functionality can also help identify/deduplicate users attributed through both methods. AppsFlyer Support AppsFlyer Support

If you're choosing between tools

Bottom line: for a social-first business with both web and mobile apps, I'd make AppsFlyer the attribution layer, your warehouse the analytical source of truth, and the ad platforms optimization endpoints. This gives you a much cleaner answer to CAC/ROAS than trying to reconcile each social network's self-reported conversions. AppsFlyer AppsFlyer

If you tell me your stack (e.g. Meta + TikTok + Google, iOS/Android, Shopify/web app, Segment/Snowflake/BigQuery), I can lay out the exact attribution architecture and event flow I'd recommend.

Yes. There are two broad categories:

• Native ad-platform AI: Meta, Google, TikTok, and X can expand beyond manually selected audiences and/or create lookalikes using machine learning.
• Third-party audience intelligence tools: These sit on top of your first-party customer/conversion data and can identify high-value customer clusters, generate similarity-based audiences, and sync them to multiple ad platforms.

For example:

• Meta: Advantage+ Audience can use your audience inputs as signals and expand beyond them. Meta also supports lookalike-style audience strategies. Meta Blueprint
• Google Ads: Optimized Targeting finds additional users likely to convert, while Google Lookalike segments use first-party data. Google is transitioning Lookalike segments toward being signals/suggestions during 2026. Google Help Google Help
• TikTok: Lookalike Audiences can be built from Custom Audiences, with Narrow/Balanced/Broad expansion options. TikTok Ads TikTok Ads
• X: Audience Expansion uses a machine-learning similarity model and lets advertisers choose defined, expanded, or broad expansion. X Business
• Cross-platform tools: Products such as VectAd and Airbridge Audience Manager are designed to use first-party/conversion data to create segments and push them to Meta, Google, and TikTok. Airbridge VectAd

If by “predicts” you mean “an AI tells me which new audience segments I should test before I spend the money”, that's a more specific category. A useful system would analyze your past customers/conversions, identify high-value patterns, generate several candidate segments/lookalikes, estimate expected incremental reach/value, and then push the selected audiences into the ad platforms.

If you tell me whether you're advertising on Meta, TikTok, Google, LinkedIn, or multiple platforms, I can point you toward the tools that actually do that rather than just offering basic lookalike creation.

Yes. There are two categories of tools that do this:

• Native ad-platform AI: Meta, Google, and TikTok increasingly automate audience expansion themselves.
• Third-party audience intelligence tools: These sit on top of your first-party/customer data and recommend or build segments across multiple ad platforms.

Best options

• Google Ads / Demand Gen — Google is currently moving Lookalike Segments toward an AI “suggestion” model. Your customer/website/app seed data becomes a signal, and Google can expand beyond strict similarity thresholds when that is likely to improve conversions or CPA. Google Help Google Help
• TikTok Ads Manager — supports Narrow/Balanced/Broad lookalikes, and TikTok's Smart+ campaigns can use Audience Suggestions and then expand beyond those suggestions. TikTok Ads TikTok TikTok For Business
• VectAd — a newer third-party option specifically positioned as an AI layer between your first-party customer data and Meta, Google, and TikTok. It uses customer/segment similarity to identify audiences and can push audiences to those platforms. VectAd

The distinction I'd make is important: platform-native tools mostly optimize delivery, whereas a dedicated audience-intelligence tool can potentially answer the more strategic question:

“Given everything we know about our highest-value customers, what new audience segments should we test next, how should we expand them, and which platform should receive each segment?”

If that's what you're looking for, I can give you a shortlist of 5–10 tools that specifically predict/recommend audience expansion and lookalikes, with a comparison of Meta/Instagram, TikTok, Google, LinkedIn support, AI capabilities, data requirements, and pricing.

If you mean automatically move budget between Meta, TikTok, LinkedIn, etc. based on target ROI/ROAS and CPA, I’d shortlist tools in three categories:

• Rockerbox Optimize — strong fit if you care about ROAS/CPA goals plus attribution. It supports smart budget allocation and lets you set fixed or dynamic goals for social campaigns. Rockerbox
• Sprinklr Marketing Smart Budget — better suited to larger teams needing automated redistribution and customizable optimization metrics. Its system is explicitly designed to minimize CPA and can use third-party metrics. Sprinklr
• Trapica Automation AI — worth considering if you want more autonomous execution across Meta, Google, TikTok, LinkedIn and other channels, with ROI/CPA objectives. Trapica
• Tune by Ways — particularly interesting for cross-channel portfolio allocation, with 40+ integrations and automated budget calculations based on targets such as ROAS. GetTune

What I'd choose based on your setup

The important distinction is optimization within each ad platform vs. actually reallocating the total portfolio across platforms. Meta's and TikTok's native automation can optimize delivery inside their ecosystems, but they don't give you the same cross-platform budget decision layer. TikTok, for example, describes Smart+ as automating targeting, bidding and delivery within its platform. TikTok For Business

If you tell me your channels (e.g. Meta + TikTok + LinkedIn), monthly ad spend, and whether you want recommendations or fully automatic budget changes, I can narrow this to the most appropriate 2–3 tools and compare pricing/integrations.

If you mean automatically shifting ad spend between social campaigns to hit ROI/ROAS and CPA targets, the best tool depends on your scale and how much control you want:

My recommendation by scenario

• Spending <$10k/month on ads: Start with native optimization + Revealbot for guardrails.
• $10k–$100k/month across Meta/TikTok/LinkedIn: Consider Madgicx or Revealbot.
• $100k+/month across multiple social channels: Look at Smartly.io or Sprinklr.
• You need true portfolio-level allocation (“move budget from TikTok to Meta when CPA changes”): prioritize cross-channel platforms rather than single-network optimizers. Cometly

For most growth teams, the ideal setup is:

1. Define hard constraints (monthly budget, max CPA, minimum ROAS).
2. Let automation shift spend within those limits.
3. Keep humans approving major channel-level moves.

If you tell me your monthly ad spend, platforms (Meta/TikTok/LinkedIn/etc.), and whether your goal is CPA, ROAS, or revenue maximization, I can narrow it to the best fit.

Yes. There are several tools now aimed specifically at generating lots of creative variants for paid-social testing, rather than just being general-purpose image/video generators.

A few worth looking at:

• sovran.ai — particularly focused on creative testing. You can build modular ads from different hooks, bodies, CTAs, etc., and it automatically generates the combinations. It supports formats such as 9:16, 4:5, and 1:1. Sovran
• adscreator.ai — generates batches of images, copy, headlines and creative directions and resizes them for multiple placements, including Meta, TikTok, LinkedIn, Google and Pinterest. Adscreator Adscreator
• admove.ai — more of an end-to-end creative testing workflow. It can research the product, generate concepts/scripts/visuals, create multiple variants, and export them for channels such as Meta and TikTok. AdMove
• ads.tiktok.com — TikTok's native AI tooling. It can generate and remix TikTok-ready videos/images and sync them to Ads Manager. It's particularly useful if TikTok is your main placement. TikTok Ads TikTok Ads
• Meta Advantage+ creative — if you're primarily running Facebook/Instagram, Meta itself can automatically adjust things like aspect ratios and expand images for placements such as Reels. Facebook

If you're describing a specific workflow

If what you mean is:

Upload one product/creative → automatically generate 20–100 meaningful variations → adapt each to Feed/Reels/Stories/etc. → launch them as a structured creative test → track which hooks/angles win

then Sovran and AdMove are closer to that concept than something like Canva or a generic AI image generator.

The important distinction is variation generation vs. true creative testing. A tool can make 50 slightly different images, but a useful testing platform should let you deliberately vary dimensions such as:

• Hook
• Visual concept
• Offer
• Product benefit
• Social proof
• CTA
• UGC vs. polished creative
• Aspect ratio / placement
• Audience or persona

That makes the test results much more interpretable.

If you're thinking about building a product like this, I can also and .

That makes the test results much more interpretable.

If you're thinking about building a product like this, I can also map out the current competitive landscape and identify what appears to be missing from these tools.

Yes. There’s a growing category of AI creative-testing / creative-automation software specifically designed to turn one concept or asset into many testable social variations.

A few worth looking at:

• AdStyle — Built around structured creative testing: generate variations from a winning ad, change specific variables (hook, model, crop, audience, placement), and compare performance. AdStyle
• AdMove — Takes a product URL and generates concepts, scripts, scenes, voiceovers, and multiple creatives for Meta, TikTok, and YouTube. Admove
• Sovran — Particularly interesting if you're testing video. You can create reusable Hook × Body × CTA combinations and automatically render hundreds of permutations in formats such as 9:16, 4:5, and 1:1. Sovran Sovran
• Shhots AI — Turns a product image into batches of UGC/product/cinematic video variants for Meta and TikTok, with different hooks, scenes, styles, and aspect ratios. Shhots AI
• Lamina — Generates brand-controlled 1:1, 4:5, and 9:16 variants from a brief and can integrate with Meta/TikTok publishing workflows. Lamina
• Bannerflow — More enterprise-oriented. It specializes in scaling a master creative into many formats/versions and can automate testing and optimization across channels. Bannerflow Bannerflow

There are also native platform solutions. For example, TikTok's Smart Creative can automatically combine creative inputs, detect fatigue, and refresh creatives; TikTok says it supports TikTok and Pangle placements. TikTok

If you're thinking about the product category

The interesting distinction is between:

"AI ad generator" → makes you 10 ads.

vs.

"Creative testing engine" → takes one winning concept and systematically generates:

Hook A/B/C × Visual A/B × CTA A/B × Format A/B/C

→ renders the combinations → pushes them into Meta/TikTok → measures results → identifies the winning creative variable → generates the next test.

The second category is much closer to what you're describing. Sovran and AdStyle are particularly aligned with that workflow. Sovran AdStyle

If you're asking because you're evaluating/building a product in this space, I can also map out the existing players and identify where there is still a meaningful gap.

Yes. There are now tools specifically built around this idea: take CRM/first-party customer data → calculate or predict LTV → send that value back to Meta/Google/TikTok → let their bidding algorithms optimize toward higher-value customers rather than just cheap conversions.

Tools worth looking at

• Churney — probably the closest match to what you're describing. It connects first-party/CRM data, predicts customer LTV, and sends predicted-LTV conversion signals to Meta, Google, and TikTok. It specifically positions itself around optimizing toward long-term ROAS rather than early purchase signals. Churney
• AdZeta — focused heavily on Meta. It uses CRM/customer data to generate predictive LTV and passes those signals through Meta's Conversions API for value optimization. AdZeta
• Customer.io — more of a customer-data/engagement platform than an LTV bidding product, but it can push first-party events into Meta. It can be useful as part of the data pipeline if you already have your LTV model elsewhere. Customer.io
• Google Ads itself now has substantially more native support for this. Its Customer Lifecycle Goals allow CRM/CDP data to distinguish new, existing, lapsed, and high-value customers, and value-based bidding can optimize toward those segments. Google Help Google Help

The architecture you're looking for

The important distinction is that you don't necessarily want to upload an LTV audience and call it done.

Ideally:

CRM / warehouse
→ purchases, subscription events, renewals, churn, margin, customer attributes
→ LTV model
→ predicted 90/180/365-day value
→ server-side conversion event
→ Meta / Google / TikTok
→ value-based bidding

So, for example, instead of telling Meta:

"User purchased $50"

you can ultimately provide a signal more like:

"This acquisition is predicted to generate $480 of lifetime revenue."

The ad platform can then learn which types of users tend to generate those higher values.

Google explicitly supports lifetime-value-related conversion values and CRM/CDP connections, while Google's current documentation says Customer Lifecycle Goals can optimize toward high-value new customers and high-value lapsed customers. Google Help Google for Developers

If you're specifically running Meta ads

I'd investigate Churney vs. AdZeta first.

The key questions I'd ask each vendor are:

1. Can you ingest my actual CRM events?

E.g. HubSpot/Salesforce/Stripe/Recharge/Shopify/database.

2. Do you calculate predictive LTV, or merely pass historical LTV?
3. Can you send the signal server-side through Meta CAPI?
4. Can the signal be optimized at the individual conversion level, rather than merely creating high-value audience segments?
5. Can you optimize for profit/margin rather than revenue?
6. Can the same model feed Meta + Google + TikTok?
7. Can I run an incrementality/holdout test to establish whether the LTV optimization actually improves long-term ROAS rather than just reported platform ROAS?

That last point is particularly important. A platform can report better short-term ROAS while you're actually acquiring worse customers.

Also, this isn't purely theoretical anymore: Google is explicitly pushing first-party data and predicted/historical customer value into its bidding ecosystem, and its current documentation says Customer Match/CRM data can be used for lifecycle optimization. Google Google Help

If you tell me what CRM/data stack you have (e.g. Salesforce + Stripe, HubSpot + Shopify, etc.), your business model (SaaS/DTC/subscription), and whether you're mainly on Meta, Google, or both, I can map out the specific tools + data flow I'd use.

"This acquisition is predicted to generate $480 of lifetime revenue."

The ad platform can then learn which types of users tend to generate those higher values.

Google explicitly supports lifetime-value-related conversion values and CRM/CDP connections, while Google's current documentation says Customer Lifecycle Goals can optimize toward high-value new customers and high-value lapsed customers.

If you're specifically running Meta ads

I'd investigate Churney vs. AdZeta first.

The key questions I'd ask each vendor are: