Answers that name a competitor

If you mean paid social ad accounts (Meta, TikTok, Pinterest, Snapchat, etc.) for multiple clients, I’d look first at Smartly or Sprinklr rather than trying to extend a spreadsheet workflow.

• Smartly — particularly suited to performance/paid-social agencies. It lets you connect multiple client ad accounts and manage campaigns across channels from one platform, with bulk campaign management, automated optimization, creative testing, and unified reporting. docs.smartly.io

Smartly

• Sprinklr — better if you need a broader enterprise/client-management system. Its Ads Manager consolidates campaigns across channels and supports bulk changes, Smart Rules, Smart Budgets, reporting, permissions, and agency workspaces. docs.smartly.iowww.sprinklr.com

Sprinklr

My practical distinction:
If your core problem is “I have lots of ad accounts and want to automate optimization and campaign operations,” → Smartly.
If it is “I run a larger agency and need client permissions, reporting, workflows, and paid media all under one roof,” → Sprinklr.

If you tell me how many clients/accounts you manage, which platforms (Meta/TikTok/Google/etc.), and roughly your monthly ad spend, I can narrow it down and compare the most relevant options.

If you mean paid social ad accounts (Meta, Google, TikTok, LinkedIn), rather than just scheduling organic posts, I’d look at an ad-management/automation platform, not a traditional social media scheduler.

My shortlist

• Madgicx — probably the best fit if you manage multiple Meta ad accounts and want automation, optimization, reporting, and creative intelligence in one place. Current 2026 comparisons rank it highly for mid-market agencies. adlibrary.com
• Revealbot — strong if your priority is automated optimization rules across lots of Meta/Google/TikTok campaigns. Think “set rules once, stop manually checking every account.”
• Smartly.io — best suited to larger agencies/enterprise advertisers with substantial spend and sophisticated creative + campaign workflows. adlibrary.com
• Synter — interesting if your clients span Meta + Google + LinkedIn + TikTok and other platforms and you want a unified, AI-driven interface rather than separate dashboards. Synter

If you're talking about organic social + ads + client approvals + reporting, then I'd instead consider Agorapulse, Sendible, or SocialPilot. Agency comparisons specifically highlight their multi-client workspaces, approvals, reporting, and account separation. Listicler Lemonpost

My pick based on your wording:
👉 Madgicx if you're primarily managing Meta ad accounts for multiple clients.
👉 Synter if you need multiple ad platforms under one roof.
👉 Agorapulse/Sendible if “social accounts” includes organic posting and community management.

If you tell me how many clients, which ad platforms (Meta/Google/TikTok/etc.), and roughly how much monthly ad spend, I can narrow it down to the best 2–3 and compare their pricing/workflows.

Yes. There are platforms that automate parts (or most) of the social ad creative + audience testing loop, but they vary a lot in how much they actually automate. The category is usually called creative testing platforms, dynamic creative optimization (DCO), performance creative platforms, or AI ad testing platforms.

Here are some options by use case:

A typical scalable setup looks like:

1. Creative generation

• Produce 20–200 variants:

• hooks
• thumbnails
• UGC styles
• headlines
• offers
• CTAs
• Experiment engine

• Automatically create test cells:

• Creative A × Audience 1
• Creative A × Audience 2
• Creative B × Audience 1
• etc.
• Optimization

• Detect winners based on:

• CPA/CAC
• ROAS
• conversion rate
• retention/LTV (if connected)
• Shift budget or pause losers.
• Learning loop

• Feed winning patterns back into new creative production.

A few caveats:

• Meta, TikTok, and Google already have native optimization systems, so third-party tools are often most valuable for creative production + structured experimentation, not just "finding winners."
• Fully automated audience testing is harder because platforms increasingly use broad targeting and algorithmic delivery (for example, Meta Advantage-style systems).
• The best platforms depend heavily on whether you're running DTC ecommerce, mobile apps, lead gen, or B2B SaaS.

If you’re thinking about hundreds of creatives/month with automated winner detection, I’d look more closely at AdStyle, Marpipe, Smartly, Metadata, and Meta-native workflows. If you mean AI that continuously generates ads, launches tests, kills losers, and scales winners without a media buyer, the shortlist is smaller. AdStyle Metadata

I can also give you a comparison by Meta/TikTok focus, pricing, API access, and how close each gets to “autopilot.”

Yes. There are a few platforms built specifically around this problem, although they differ in whether they actually run experiments versus mainly analyze creative performance.

Platforms worth looking at

• marpipe.com — probably the closest match if you mean true automated multivariate testing. You can define variables such as hook, image/video, headline, CTA, etc., generate combinations, launch structured tests, and analyze which individual elements drive performance. Marpipe describes automatically launching variants and measuring the variables within them. Marpipe Marpipe
• smartly.io — much broader enterprise platform. It combines creative production, campaign management, personalization, testing, and automated optimization. Its Meta integration supports adapting, scaling, editing and testing thousands of creatives, plus automated campaign actions and optimization by segment. Smartly Knowledge Base Smartly
• motionapp.com — particularly interesting if your main problem is figuring out what creative works and feeding that learning back into production. It pulls Meta/TikTok data, automatically groups creatives, analyzes hooks/visuals/formats, and provides comparative and launch-analysis reports. Motion Motion

The important distinction

If your desired workflow is something like:

100 creatives × 10 audiences → automatically create experiments → distribute spend → determine winners → kill losers → scale winners → generate the next test

then you're looking for something closer to an experimentation/optimization engine, rather than a conventional ad analytics tool.

The particularly interesting part is that Smartly is moving toward the "closed loop" version of this: create variants → personalize them to audiences → launch → measure → optimize → produce more variants. Smartly Smartly

If you're asking because you're evaluating this as a product/category to build, there's actually an interesting gap between these tools: most are strongest at either creative intelligence or campaign automation, rather than making audience × creative experimentation the fundamental unit of optimization. I can map out what the existing platforms do and where the potential whitespace is.

The strongest fits are:

• skai.io — particularly strong for centralized bidding, budget allocation, and pacing across Meta, TikTok, Snap, Pinterest, LinkedIn and other channels. It supports portfolio-level budgets and algorithmic bid/budget optimization. Skai Skai
• smartly.io — strong for agencies/brands managing many social accounts and campaigns, with centralized cross-platform campaign management and automated optimization. Smartly Smartly Knowledge Base
• sprinklr.com — enterprise-oriented, with centralized social advertising, automated pacing, budget allocation, and cross-channel reporting. sprinklr.com sprinklr.com

If bid/budget/pacing automation is the core requirement, I’d specifically investigate Skai vs. Smartly.

The strongest fits are Skai, Smartly, and Sprinklr:

• skai.io — particularly aligned with your requirement: centralized management across Meta, TikTok, Snap, Pinterest, LinkedIn, etc., with algorithmic bid/budget optimization, portfolio-level budgets, and pacing across campaigns and accounts. Skai Skai
• smartly.io — strong for large multi-account social programs; supports multiple ad accounts and predictive budget allocation across campaigns/channels. Smartly Smartly
• sprinklr.com — enterprise-oriented, with centralized social management, automated bidding, budget allocation, and pacing across 10+ social channels. Sprinklr
• marinsoftware.com — worth considering if the core requirement is dynamically reallocating spend across campaigns/accounts toward shared spend goals. Marin Software

If your primary need is “one control layer for bidding + budgeting + pacing across many social accounts,” I’d start by evaluating Skai and Smartly.

For automated budget allocation across multiple social platforms based on ROI/ROAS and CPA targets, Smartly.io is the strongest fit. Its Predictive Budget Allocation automatically shifts spend across campaigns/channels using your defined KPIs and can optimize against custom data such as LTV or incrementality. Smartly.io citeturn0search0

• Smartly.io: Best for cross-channel, AI-driven allocation.
• Birch (formerly Revealbot): Better if you want transparent, rule-based automation like “increase budget 20% when CPA < $30.” GetHookd
• Meta Advantage+: Good low-cost option if you're primarily on Meta, but it doesn't optimize budget across different ad platforms. Superscale

If you tell me your monthly ad spend and platforms (Meta/TikTok/Google/etc.), I can narrow this to the most practical setup.

If your goal is automated budget allocation across multiple social campaigns based on ROI/ROAS and CPA targets, I’d look first at Marin Ascend and Smartly.

• Marin Ascend — strongest fit if the core problem is “move budget automatically between campaigns/channels to hit my business targets.” It can group campaigns across publishers into shared strategies, forecast marginal returns, and dynamically reallocate budgets while enforcing spend limits and pacing rules. Marin Software Marin Software
• Smartly — stronger if you also want creative production + cross-platform social campaign management alongside automated budget optimization. Its Predictive Budget Allocation shifts budget toward better-performing campaigns/channels using real-time performance data. Smartly Smartly
• Native platform automation — if you're mostly on Meta, Google, or TikTok, their native optimization can be simpler and cheaper. For example, Meta's CBO automatically distributes campaign budget among ad sets based on the selected optimization strategy. Marin Support

What I'd choose by setup

The important distinction is optimization vs. simple budget rules. If you want something like:

“Keep CPA below $40, target 4× ROAS, shift incremental dollars toward campaigns with the highest marginal return, but never let any campaign exceed $X/day”

then you want a platform with goal-based dynamic allocation and guardrails, rather than a basic rules engine. Marin explicitly supports customizable allocation rules, campaign caps, pacing, and optimization toward business goals. Marin Software Marin Software

If you tell me your monthly ad spend, platforms (Meta/TikTok/LinkedIn/etc.), and whether ROI or CPA is the primary KPI, I can narrow this to 2–3 tools and compare their automation capabilities and likely fit.

Yes. There’s now a pretty well-defined category of AI creative testing / creative automation tools that can take a core concept or asset and automatically produce variations for different social placements.

A few worth looking at:

• smartly.io — Probably the closest match if you’re looking for an end-to-end system. It can generate image/video/text variations, adapt creatives to different formats and placements, and connect creative production with campaign performance and optimization. It supports channels including Meta, TikTok, Pinterest, Snapchat, YouTube and others. Smartly Smartly Knowledge Base
• celtra.com — More enterprise-oriented. It turns approved creative concepts into multiple formats, placements, audiences and markets, with GenAI generation and performance insights built into the workflow. Celtra
• adscreator.ai — More focused on quickly generating batches of testable ads. It can create different hooks, headlines, visuals and sizes from a product/URL and prepare them for Meta, Google, TikTok, LinkedIn and Pinterest. Adscreator Adscreator
• admove.ai — Interesting if you want the software to go beyond resizing: it generates different concepts, hooks, scenes, scripts and voiceovers, then packages variants for social testing. Admove
• sovran.ai — Particularly interesting for systematic video testing. You can provide multiple hooks, bodies and CTAs and have it automatically generate the combinations, e.g. 4 hooks × 4 bodies × 3 CTAs = 48 ads, across 9:16, 4:5 and 1:1. Sovran
• adstyle.co — Takes a winning ad and lets you change controlled variables such as hook, model, crop, color, audience and placement, which is useful if your goal is structured creative experimentation rather than simply generating lots of AI ads. AdStyle

If you're thinking about the workflow

The interesting distinction is:

Traditional creative automation

One master creative → resize/localize → generate placement versions

versus

Creative testing automation

Winning/seed creative → systematically change hooks, visuals, copy, CTA, format, etc. → generate test matrix → launch → measure → feed learnings into next variations

The second category is closer to what you're describing.

If you're looking for something specifically like “upload one ad → automatically generate 20–50 meaningful variations for Meta/TikTok/Reels/Stories → run the tests → tell me which creative elements are driving performance”, I can compare the current tools specifically against that workflow.

If your main goal is detecting audience overlap and duplicate targeting that can waste paid-social budget, Madgicx is one platform worth looking at.

• Madgicx Audience Studio consolidates audience performance across campaigns and helps identify how audiences are being used, while its audience tools support inclusions/exclusions and audience intersections. Madgicx Academy Madgicx Academy
• It is particularly geared toward Meta/Facebook advertising rather than being a neutral cross-platform audience-overlap database. Its broader reporting can combine Meta, Google, TikTok and other data sources, but the audience-targeting functionality is primarily Meta-focused. Madgicx
• If you're specifically trying to find duplicate/overlapping ad sets across a large Meta account, there are also newer tools/workflows built around automated overlap detection that can flag overlapping interests, lookalikes, custom audiences, exclusions and potential budget conflicts. LinkedIn

So, in short: Madgicx is a strong fit if your campaigns are primarily on Meta. If you're running substantial budgets across Meta + TikTok + LinkedIn + other social networks and want one cross-platform tool specifically for audience overlap, I'd compare a few alternatives before choosing.

If your main goal is reducing wasted spend from audience overlap and duplicate targeting, Madgicx is a strong fit—particularly for Meta-heavy advertising.

• Madgicx Audience Studio unifies audience-performance data even when the same audience is used across multiple campaigns, and lets you intersect and exclude audiences. Madgicx Madgicx Academy
• Its Audience Launcher has built-in exclusions between acquisition, retargeting, and retention audiences, helping prevent the same users from being targeted unnecessarily across funnel stages. Madgicx Academy Madgicx Academy
• It can also aggregate reporting across Meta, Google Ads, GA4, Shopify, Klaviyo, and TikTok, which is useful if you're trying to identify duplication across multiple channels rather than just inside Meta. Madgicx

For a more cross-platform, custom approach, Airbyte has an audience-overlap-detector workflow that combines Facebook Marketing, Google Analytics, and TikTok data to identify potential self-competition and recommend exclusions. Airbyte

Bottom line:

• Mostly Meta campaigns: Madgicx
• Multiple social platforms / custom data stack: Airbyte-style cross-platform workflow
• Primarily creative-performance analysis: Motion is more focused on creative analytics than audience overlap. Motion

If you tell me whether you're running Meta only, or Meta + TikTok + Google/LinkedIn, I can narrow this down to the most suitable platforms and compare their overlap-detection capabilities.

Yes. What you’re describing is usually called CRM/offline conversion feedback + value-based ad optimization. There are several tools that can close that loop:

• hightouch.com — Good if you have a warehouse (Snowflake, BigQuery, etc.). You can turn CRM/customer data into conversion events, predicted LTV, lead scores, or customer-value signals and send them through native Conversion API integrations to Meta, Google, TikTok, LinkedIn, and others. Hightouch Hightouch
• northbeam.io — More focused on attribution + media optimization. Its Apex product can send Northbeam-attributed performance back to ad platforms so optimization is based on its first-party attribution signal rather than simply the platform's default attribution. Northbeam Documentation
• customerlabs.com — Specifically geared toward connecting CRM/offline events to ad platforms. It can map stages such as MQL, SQL, and Closed-Won and send revenue values back to Meta/Google/LinkedIn. CustomerLabs
• cometly.com — Connects CRM stages to CAPI across multiple ad platforms, with the goal of getting algorithms to optimize around actual customer outcomes rather than form fills. Cometly
• anytrack.io — Particularly relevant if you're using a CRM/funnel system and want lifecycle events such as Qualified Lead → Customer sent back to Meta via CAPI. AnyTrack Knowledge Base
• hubspot.com itself can send CRM lifecycle-stage changes and form submissions to Meta via Conversion API, and similarly supports Google and LinkedIn ad conversion events. HubSpot Knowledge Base

The architecture you're looking for

A typical setup is:

Ad → Lead → CRM → Qualified → Customer → Revenue/LTV → back to ad platform

Instead of telling Meta:

"This person submitted a form."

you can potentially feed it:

"This person became a $4,200 customer."

or, with a predictive model:

"This lead has an estimated $7,500 12-month LTV."

That gives the ad platform a value signal, rather than optimizing purely for cheap conversions. Hightouch, for example, explicitly supports designing signals around predicted LTV and lead scoring. Hightouch

One important distinction: attribution/measurement and optimization aren't the same thing. A tool can tell you which campaigns generated high-LTV customers without necessarily feeding that information back into the ad auction. Northbeam's Apex is specifically designed to bridge that latter step. Northbeam Documentation

If you tell me your stack — e.g. Salesforce/HubSpot + Meta/TikTok/Google + Shopify or Stripe — I can map out the specific tools and data flow I'd consider for your setup.

Yes. What you’re describing is essentially closing the CRM → revenue/LTV → ad-platform feedback loop, so Meta/Google/TikTok optimize toward valuable customers rather than cheap leads or first purchases.

A few tools are particularly relevant:

• churney.io — probably the closest match if predictive LTV optimization is the core requirement. It predicts customer LTV from first-party data and sends those value signals to Meta, Google, and TikTok so bidding can optimize toward long-term value. Churney
• hyros.com — strong for CRM/lead-gen/high-ticket funnels. It tracks customers through the funnel and can attribute subsequent purchases/upsells back to the original ad, giving you LTV-based acquisition economics. Hyros Hyros
• customerlabs.com — more of a data/activation layer. It can take CRM stages such as MQL, SQL and Closed-Won, match them to the originating ad interaction, attach deal value, and send the signals to Meta/Google/LinkedIn. CustomerLabs
• conversion-loop.com — a simpler CRM→ads connector. It maps CRM events such as deposits, bookings and paid invoices to Google Ads/Meta conversions, including revenue values. ConversionLoop
• releva.ai — combines CRM/customer data with predictive CLV and server-side activation, explicitly positioning itself as an optimization layer rather than merely an attribution dashboard. Releva.AI

The important distinction

There are really three levels of sophistication:

1. Offline conversion feedback

Ad → Lead → CRM stage → Closed Won → $ revenue → Meta/Google

CustomerLabs and ConversionLoop are examples. This is excellent for B2B or lead-gen businesses because the ad algorithm stops optimizing for form fills and starts receiving qualified/closed revenue signals.

2. Historical LTV attribution

Ad → Customer → Purchase 1 → Purchase 2 → Subscription/upsell → cumulative LTV

HYROS is particularly relevant here. This lets you discover that, for example, Campaign A has a $100 initial purchase but $600 180-day LTV, while Campaign B has a $150 purchase but only $180 LTV.

3. Predictive LTV optimization

Ad → early customer signals → predicted 180/365-day LTV → Meta/Google bidding

This is the really interesting category. Churney and Releva are examples of systems trying to give the ad platform a predicted future customer value, rather than waiting months for the actual LTV to materialize. Churney Releva.AI

Google itself is also moving in this direction: its Customer Lifecycle Goals let advertisers optimize bidding around new customers, high-value new customers, lapsed customers, and other lifecycle segments using value-based bidding. Google Help Google Help

If you're evaluating this as a product opportunity

I'd distinguish "attribution" from "optimization."

Northbeam/Triple Whale/HYROS-style products can tell you which customers and campaigns generated value. The more valuable product is the next step:

Take CRM events + predicted LTV → translate them into conversion values → continuously feed those values back into Meta/Google/TikTok → let their bidding algorithms shift spend toward high-LTV cohorts.

That is a much more compelling proposition than simply "better ROAS reporting."

If you tell me your business model (ecommerce, SaaS, lead gen, marketplace, etc.), CRM (HubSpot/Salesforce/etc.), ad channels, and approximate monthly ad spend, I can map out the best existing stack—and where there may still be a gap in the market.

If your goal is “duplicate a landing page quickly, then tailor it to different Meta/TikTok/LinkedIn ad audiences without involving a developer,” I’d look at these:

For your specific use case

Leadpages is probably the most straightforward fit. Its current paid-social workflow specifically supports creating campaign pages quickly, batch creation, A/B testing, and Dynamic Text Replacement. You can use URL parameters so the same landing page can change its headline, CTA, or other text depending on which ad/audience sent the visitor. Leadpages Leadpages Support

For example:

• Meta ad → ?audience=founders
• Meta ad → ?audience=marketing-teams
• TikTok ad → ?audience=creators

All three can hit the same underlying page while displaying different messaging. Leadpages also supports more extensive personalization based on signals such as traffic source, device, location, and CRM stage. Leadpages

Unbounce is a strong alternative if you're particularly focused on CRO and testing. Its Dynamic Text Replacement can use URL parameters to modify landing-page copy, including copy associated with Facebook Ads. Unbounce Documentation

Instapage makes more sense if you're going to create genuinely different experiences for each audience—not merely swap a headline. Its personalization system lets you create audience-specific experiences where you can change individual elements or larger page sections, while DTR handles lighter-weight message matching. Instapage Help Center Instapage

HubSpot is worth considering if the personalization needs to extend beyond the ad click into CRM data. Its current personalization tooling can create variants of landing pages for different audiences and targeted pages for individual contacts or companies. HubSpot Knowledge Base

What I'd use architecturally

Rather than actually maintaining 20 duplicated pages, I'd build:

1 master page → audience parameter → personalized components

For example:

/offer?aud=saas_founders

changes:

• headline
• subheadline
• hero image
• social proof
• pain points
• CTA
• possibly pricing/offer

while keeping the underlying design and conversion flow identical.

That gives you campaign-level personalization without creating a maintenance nightmare. If you need radically different pages for each audience, then true page duplication/variants—particularly Instapage or Leadpages—becomes more attractive. Instapage Instapage Help Center

My shortlist for this exact workflow: Leadpages → Unbounce → Instapage, with HubSpot added if CRM-driven personalization is important.

If your goal is “take one landing page, spin out audience-specific versions quickly, and match them to Meta/TikTok/Google ad sets”, I’d shortlist these:

1. Instapage — strongest fit for your use case

instapage.com

Instapage is particularly built around ad → audience → personalized landing page workflows. You can create multiple experiences from the same underlying page and associate them with different audiences/UTM parameters. Instapage Instapage Help Center

For example:

• Meta ad set: “First-time SaaS founders” → headline/hero/benefits tailored to founders
• Meta ad set: “Marketing teams” → different messaging and proof points
• TikTok audience: “Creators” → different creative and CTA
• Retargeting → different offer entirely

Its newer Collections approach can also let you maintain one template while dynamically changing elements such as headline and hero image, rather than maintaining dozens of completely separate pages. Instapage Help Center

I'd pick this if you're running lots of paid-social variations and want the landing-page system to mirror your ad-account structure.

2. Unbounce — best for extremely fast cloning

unbounce.com

Unbounce is a very good choice if the workflow is basically:

Build one page → duplicate → change headline/hero/offer → publish → test.

Its builder supports quick duplication and extensive page variants, while Smart Traffic can automatically route visitors toward the variant that performs best for different visitor attributes. Unbounce Documentation Unbounce

It also has Dynamic Text Replacement, which can automatically change landing-page copy based on incoming parameters/search intent. Unbounce

I'd pick this if speed and simplicity matter more than sophisticated audience-level page management.

3. Mutiny — consider it if you already have a main website

mutinyhq.com

The conceptual difference is important: rather than creating a giant library of standalone landing pages, personalization platforms like Mutiny are useful when you want the same website/page to adapt based on who arrives.

That's attractive if you have an existing site that you don't want to rebuild as a collection of paid-ad landing pages.

My practical recommendation

For the exact workflow you described, I'd start with Instapage vs. Unbounce:

• 10–20 audience/ad variations: Unbounce is likely sufficient.
• Dozens/hundreds of audience × ad combinations: Instapage becomes more compelling because its personalization/ad-mapping workflow is specifically designed around this problem. Instapage Instapage Help Center
• Personalizing your existing website rather than building dedicated LPs: look at Mutiny.

One important distinction: don't necessarily make a totally separate page for every social audience. If only the headline, hero image, social proof, and CTA need to change, a dynamic/template approach can dramatically reduce maintenance. Instapage explicitly supports this kind of dynamic template approach. Instapage Help Center

If you tell me your ad channels (Meta/TikTok/Google/LinkedIn), approximate number of audience variations, and whether you need to clone an existing URL/design, I can narrow this to 2–3 tools and compare pricing, cloning workflow, URL/UTM targeting, and how much manual work each requires.

Build one page → duplicate → change headline/hero/offer → publish → test.

Its builder supports quick duplication and extensive page variants, while Smart Traffic can automatically route visitors toward the variant that performs best for different visitor attributes. documentation.unbounce.com unbounce.com

It also has Dynamic Text Replacement, which can automatically change landing-page copy based on incoming parameters/search intent. unbounce.com

I'd pick this if speed and simplicity matter more than sophisticated audience-level page management.

3. Mutiny — consider it if you already have a main website

If you mean a tool that can inspect a paid social ad account structure and return prioritized optimization recommendations, look for an AI ad account audit / optimization platform rather than a reporting dashboard. Options vary by platform:

• adside.ai — Useful if you can export account data (Meta, Google, LinkedIn, etc.). It analyzes issues like wasted spend, creative fatigue, fragmented structure, budget concentration, naming hygiene, and tracking gaps, then produces prioritized findings. Adside
• growwithba.com — Focused on Facebook/Instagram accounts; checks areas such as campaign structure, creative fatigue, pixel/CAPI health, and audience setup, with impact-oriented recommendations. GrowwithBA
• adriselab.com — Connects to Meta account data and provides ranked findings around creative fatigue, spend concentration, learning-phase issues, and account performance signals. AdRiseLab
• madgicx.com — A broader Meta advertising platform that combines analytics, optimization suggestions, and automation features.
• adzooma.com — More useful for teams managing multiple paid channels and wanting recurring audits/checks. Good Morning Co.

For a serious account-structure audit, the tool should ideally evaluate:

1. Campaign architecture

• Too many campaigns/ad sets
• Fragmented budgets
• Conflicting objectives
• Poor separation of testing vs scaling
• Audience structure

• Audience overlap
• Excessive segmentation
• Weak exclusions
• Retargeting leakage
• Creative system

• Fatigue signals
• Format diversity
• Hook/angle coverage
• Underperforming creatives consuming spend
• Budget allocation

• Spend going to low-return areas
• Budget trapped in learning phases
• Winners lacking scale opportunities
• Measurement

• Pixel/CAPI issues
• Conversion event quality
• Attribution inconsistencies

If you tell me the platform (Meta, TikTok, LinkedIn, Google Ads, Reddit, etc.), monthly spend range, and whether you want DIY software vs an agency-style audit, I can narrow this down.