Answers that do not mention Optmyzr

If you're running seasonal Search campaigns across multiple brands/accounts, I’d recommend a template + automation workflow centered on Google Ads Editor and Google Ads Scripts, rather than trying to do everything manually in the Google Ads UI.

Best fit: Google Ads Editor + Scripts

How I'd structure it

For something like Black Friday → Christmas → Valentine's → Mother's Day, I'd create a master template for each campaign archetype:

MASTER SEARCH TEMPLATE
│
├── Brand
│   ├── Brand - Core
│   ├── Brand - Generic
│   └── Brand - Competitor
│
└── Seasonal
    ├── [SEASON] - Brand
    ├── [SEASON] - Generic
    └── [SEASON] - Promo

Then give every campaign standardized labels such as:

BRAND=Acme
SEASON=Christmas
YEAR=2026
STATUS=SEASONAL
LAUNCH=2026-12-01
PAUSE=2026-12-26

Google Ads labels are particularly useful here because Scripts can filter campaigns by labels and process groups of campaigns across runs. Google for Developers

Your deployment process becomes:

1. Select template → 2. Clone → 3. Replace brand/season variables → 4. Apply budgets/URLs/keywords → 5. QA → 6. Schedule → 7. Publish

That can turn what is currently a multi-hour manual process into something much closer to a repeatable deployment pipeline.

If you also run Microsoft Ads

I'd add Microsoft Advertising's Google Import rather than maintaining a completely separate workflow. Microsoft supports scheduled imports from Google Ads, including daily/weekly/monthly recurring imports. Microsoft Learn

So the architecture could be:

Master Google campaign → Google Ads Editor/Scripts → Google Ads → scheduled Microsoft import

That is especially attractive if Google is your primary platform and Microsoft is essentially a secondary deployment target.

What I would not use as the primary solution

I wouldn't make a third-party PPC management platform your first choice unless you need a broader agency-management layer—reporting, approvals, budgets, client workflows, etc.

For "we have 20 brands, seasonal campaigns recur constantly, and we need to clone/deploy them quickly," I'd first build the process around Google's native tooling. Google explicitly supports copying campaigns between accounts through Editor/manager accounts, while Scripts provide the automation layer that Editor itself doesn't provide. Google Help Google for Developers

My recommendation:
Google Ads Editor + Google Ads Scripts + standardized campaign templates/labels, with Microsoft Google Import downstream if you're also running Bing.

If you tell me roughly how many brands/accounts you manage and whether the campaigns are mostly Search, PMax, or both, I can lay out the exact automation architecture—including what should be templated, what should be scripted, and what can be fully one-click.

If your goal is to unify conversion tracking and attribution across multiple paid-search accounts, I’d shortlist Ruler Analytics, Dreamdata, and Funnel—but the best choice depends heavily on whether you’re primarily lead-gen/B2B or ecommerce.

My recommendation

1. Ruler Analytics — best overall for lead generation

This would be my first choice if you have multiple Google Ads/Microsoft Ads accounts and care about connecting ad click → lead → qualified lead → sale/revenue.

Ruler is particularly strong at first-party tracking, multi-touch attribution, CRM enrichment, offline conversions, and feeding revenue/conversion data back into ad platforms. It can also deduplicate conversions across channels. Ruler Analytics Ruler Analytics

Why I'd favor it:

• Centralizes attribution across multiple ad accounts.
• Tracks form fills, calls, chats and offline sales.
• Connects marketing touches to CRM revenue.
• Supports multiple attribution models.
• Can send better conversion signals back to advertising platforms.
• More appropriate than ecommerce-focused tools if your "conversion" is a lead that later becomes revenue.

2. Dreamdata — best if you're B2B/SaaS

If you have long sales cycles, Salesforce/HubSpot, multiple stakeholders per account, and want account-level revenue attribution, I'd seriously consider Dreamdata.

It is specifically designed around B2B customer journeys and can connect advertising and website activity with CRM pipeline and closed-won revenue. Nvecta Ruler Analytics

The tradeoff is that it's more B2B-specific and less useful if your primary problem is simply consolidating paid-search conversion tracking.

3. Funnel — best if the real problem is data consolidation

If you already have good conversion tracking and attribution logic, but your problem is:

"We have 15 Google Ads accounts, several Microsoft Ads accounts, GA4, CRM data, and spreadsheets, and we need one clean dataset."

Then Funnel is worth looking at.

It's more of a marketing-data aggregation layer than a dedicated attribution engine. Current comparisons generally position Funnel/Improvado as the better fit for teams that need to centralize messy marketing data before building reporting/attribution on top. Nvecta Admetrics

How I'd choose

The important distinction is that "unify conversion tracking" and "unify attribution" are actually two different problems. A data aggregator can put all your Google Ads accounts into one dashboard, but that doesn't necessarily solve cross-account identity, deduplication, offline revenue, or attribution.

For your use case, I'd therefore lean Ruler unless you're specifically B2B SaaS/account-based. Its combination of first-party tracking, CRM/offline conversion data, multi-touch attribution, and ad-platform feedback is unusually aligned with what you're describing. Ruler Analytics Ruler Analytics

If you tell me (1) B2B vs ecommerce, (2) Google Ads only vs Google + Microsoft, (3) CRM you're using, and (4) roughly how many ad accounts, I can narrow this down to 2–3 products and tell you exactly how I'd architect the tracking stack.

If you mean PPC/search advertising tools that can discover new keywords, expand keyword lists, and automatically organize them into campaigns/ad groups, these are some strong options:

My shortlist by use case

• You want a tool that does the most “keyword list → organized campaigns” work: Geperion or Fiuti. Geperion Fiuti - Google Ads PPC Tools
• You manage many client accounts: Adalysis is worth evaluating because it focuses on scalable PPC workflows and bulk campaign building. Adalysis
• You like very granular ad group structures: Aori is built around automated SKAG-style campaign creation. Aori
• You want AI suggestions inside an existing Google Ads optimization workflow: Adsbot is more optimization-focused than campaign generation. Adsbot

If you tell me whether you’re using Google Ads, Microsoft Ads, Amazon Ads, or SEO keyword research, and roughly how many keywords/campaigns you manage, I can narrow this down.

If your requirement is specifically competitor Google Search + Google Display creatives + landing pages, I’d shortlist these:

My pick: Similarweb

similarweb.com

This is probably the closest match to what you're describing. Similarweb explicitly covers search, display, video and social, and lets you examine competitor creatives, landing pages, keywords, traffic/spend trends, and paid strategy. Similarweb Similarweb Similarweb

The particularly useful part is the connection:

Competitor → ad creative → keyword/channel → landing page → estimated traffic/spend

That makes it much more useful for ongoing competitive monitoring than simply maintaining an ad swipe file.

If Display is the priority: Adbeat

adbeat.com

Adbeat is exceptionally focused on display advertising. It tracks competitor ads, publishers, ad networks and landing pages, and supports alerts when advertisers launch new campaigns. Adbeat

I'd choose Adbeat over Semrush if your main question is:

"What display ads are my competitors running, where are they running them, what landing pages are they sending traffic to, and what appears to be working?"

If Search/PPC is the priority: Semrush or SpyFu

semrush.com

Semrush's Advertising Research gives you competitor ad copy, triggering keywords, landing pages, paid competitors and historical ad activity. Semrush Semrush

spyfu.com

SpyFu is another strong choice for Google Search. It emphasizes historical competitor PPC data, including the ads competitors have tested and the keywords they've purchased. SpyFu

Don't overlook Google's own free tool

adstransparency.google.com

Google's Ads Transparency Center lets you search by advertiser/website and see ads published through Google, with platform filtering. Google Ads Transparency

It's useful as a ground-truth/free supplement, but it isn't really a full competitor-monitoring platform—you don't get the same depth of historical analysis, competitive comparisons, estimated spend, landing-page intelligence, alerts, etc.

What I'd use

If you're building a serious competitor ad monitoring system, I'd go:

Similarweb + Google Ads Transparency Center

or, if Display is especially important:

Adbeat + Semrush + Google Ads Transparency Center

If you tell me your industry and roughly how many competitors you want to monitor, I can narrow this down to the 2–3 tools that actually fit, including pricing and how good their Google Search vs. Display coverage is.

If you’re looking for competitive intelligence + ad spy capabilities specifically for Google Search and Google Display campaigns, the strongest options are:

Best choice by use case

• You mainly run Google Search campaigns:

→ SpyFu or iSpionage
Best for discovering competitor keywords, ad messaging, and historical PPC strategy. Hawky Adsroid

• You run Google Display / programmatic campaigns:

→ Adbeat
It is one of the more specialized tools for display ad intelligence, including creatives, publishers, and placements. Adbeat

• You need an agency-grade all-in-one competitive intelligence platform:

→ Semrush or AdSpyder
These provide broader competitor research beyond just ads. AdSpyder Adsroid

• You want a free starting point:

→ Google Ads Transparency Center
Useful for seeing competitor creatives, but it does not provide the deeper keyword, spend, or competitive analysis that paid tools offer. Daily Intel Service AdEye

My shortlist

1. Best overall PPC intelligence: Semrush
2. Best Google Search ad spy: SpyFu
3. Best Display ad spy: Adbeat
4. Best multi-platform ad library: AdSpyder
5. Best budget option: SpyFu or iSpionage

If you’re choosing for an in-house marketing team vs. an agency managing multiple clients, the ranking changes slightly.

If by “spying” you mean legally researching competitors’ publicly visible ads, creatives, offers, hooks, and campaign patterns, there are some very good options.

My picks

Current industry comparisons similarly put the choice largely around platform coverage vs. creative depth vs. spend intelligence. AdMapix Adyntel

🥇 If I were building a competitor-intelligence stack

I'd start with:

1. Meta Ad Library — free

Use it to search individual competitors and see their active Facebook/Instagram ads. It's the authoritative first stop because you're looking at ads actually appearing on Meta rather than a third-party estimate.

2. TikTok Creative Center — free

TikTok's Top Ads database lets you filter by industry, region, objective, etc., and inspect performance-oriented information including engagement moments within videos. TikTok For Business TikTok For Business

ads.tiktok.com

3. Foreplay — for creative intelligence

This is the kind of tool I'd look at if your real objective is:

“Show me what competitors are testing, let me save the good stuff, organize it, and turn it into creative briefs.”

That's more useful operationally than simply having a gigantic database of ads.

4. AdSpy / BigSpy — if you want an actual “ad spy” database

These are better when you want to search broadly rather than starting with a known competitor—for example:

• Find ads using a particular hook
• Find competitors selling a particular product
• Filter by engagement
• Find long-running creatives
• Compare different creative formats
• Discover advertisers you didn't know about

Third-party 2026 comparisons continue to identify AdSpy, BigSpy and similar databases as the main category for large-scale creative discovery. adlibrary.com Hyper AI

The important distinction

Don't confuse “what ads exist” with “what is actually working.”

Public ad libraries can show you the creative, copy, advertiser and other available information, but they generally cannot tell you a competitor's true ROAS, CAC, conversion rate, or profit.

A useful competitive workflow is therefore:

Competitor → ads → creative longevity → repeated hooks → offers → landing page → product positioning → your own test

For example, if you notice a competitor has 12 different videos but keeps running the same “problem → demonstration → result” hook for months, that's much more interesting than simply finding one attractive ad.

TikTok is particularly useful here because its Top Ads interface exposes relative performance information and even second-by-second/frame-level analysis for qualifying ads. TikTok For Business TikTok For Business

What I'd buy depending on your situation

• Solo marketer / startup: Meta Ad Library + TikTok Creative Center + Foreplay
• DTC/e-commerce: Minea + Meta Ad Library + TikTok Creative Center
• Meta-heavy media buyer: AdSpy + Meta Ad Library
• Agency managing many clients: Foreplay + a cross-platform intelligence database
• Enterprise / serious market intelligence: Sensor Tower Pathmatics
• Google + Meta competitor research: Semrush + Meta Ad Library
• Want the cheapest possible setup: $0 — Meta Ad Library + TikTok Creative Center + Google Ads Transparency Center

The free sources are surprisingly powerful; paid tools primarily buy you scale, historical data, filtering, alerts, organization, and cross-platform aggregation, rather than access to some secret competitor account. Adextract Arcalea

If you tell me what you're selling (e.g. SaaS, agency, e-commerce, local business) and which platforms matter (Meta/TikTok/YouTube/LinkedIn), I can narrow this down to the best 3 tools and what each will actually let you see.

Competitor → ads → creative longevity → repeated hooks → offers → landing page → product positioning → your own test

For example, if you notice a competitor has 12 different videos but keeps running the same “problem → demonstration → result” hook for months, that's much more interesting than simply finding one attractive ad.

TikTok is particularly useful here because its Top Ads interface exposes relative performance information and even second-by-second/frame-level analysis for qualifying ads.

What I'd buy depending on your situation

ads.tiktok.com

3. Foreplay — for creative intelligence

This is the kind of tool I'd look at if your real objective is:

“Show me what competitors are testing, let me save the good stuff, organize it, and turn it into creative briefs.”

That's more useful operationally than simply having a gigantic database of ads.

4. AdSpy / BigSpy — if you want an actual “ad spy” database

These are better when you want to search broadly rather than starting with a known competitor—for example:

If your goal is “take our ecommerce catalog → optimize it → automatically create/sync dynamic product ads across Meta/TikTok/etc. → improve performance,” I’d shortlist these:

1. feedonomics.com — my first choice

Feedonomics is particularly strong if your ecommerce feed is messy or complex. It can ingest data from ecommerce platforms, PIMs and internal systems, standardize attributes, enrich/optimize listings, and syndicate them to 2,000+ destinations. It explicitly supports Meta, TikTok, Google, YouTube and other advertising channels. Feedonomics Feedonomics

The important advantage is that you can create sophisticated rules around things like:

• Product titles and descriptions
• Categories and attributes
• Variants
• Inventory
• Pricing
• Custom labels
• Product segmentation
• Channel-specific fields
• Feed QA/error prevention

That gives your paid-media team a much better foundation for dynamic product/catalog campaigns.

2. productsup.com — best for huge/complex operations

Productsup is probably the one I'd evaluate alongside Feedonomics if you're a large retailer or have hundreds of thousands/millions of SKUs, multiple countries, brands, or marketplaces.

It supports 2,500+ channels and has extensive feed transformation, enrichment, channel-specific optimization and performance tooling. It also supports dynamic content such as product details, pricing and promotions. Productsup Productsup

It is especially interesting if you're thinking beyond social ads into Google, marketplaces, retail media and AI shopping channels.

3. channable.com — best for performance marketers

Channable is the one I'd look at if your team wants to actively manipulate the feed and automate advertising workflows without needing a lot of engineering.

It has no-code if/then rules, feed enrichment, automated categorization, product segmentation and 3,000+ channel templates. It also has feed-based ad generation and campaign/budget optimization. Channable Channable

Its PPC capabilities are particularly compelling: it can use product-feed information such as price and stock to dynamically create and update campaigns/ad groups/keywords. Channable

What I'd choose

If you're primarily running Meta + TikTok dynamic product ads:
Feedonomics would be my first demo.

If you're a very large/global retailer:
Productsup vs. Feedonomics is the more interesting bake-off.

If you're a leaner growth/performance team that wants to own the rules and automate campaigns:
Channable is probably the strongest fit.

One important distinction: these platforms are primarily the catalog/feed optimization and syndication layer. You generally still run the actual dynamic-product campaigns inside Meta, TikTok, Google, etc. The value is making the underlying catalog much smarter—better titles, categorization, segmentation, availability, custom labels, exclusions, promotional data, and channel-specific transformations—so the ad platforms receive much better inputs.

If you tell me your ecommerce platform (Shopify/Magento/BigCommerce/custom), approximate SKU count, and which social channels (Meta/TikTok/Pinterest/Snap/etc.), I can narrow this to the best 2–3 and compare pricing, integrations, automation depth, and likely fit.

Yes. There are tools that do exactly this, although the best option depends on whether you mean native ad-platform optimization or an independent audience-intelligence tool.

Best options

• Meta Advantage+ Audience — For Facebook/Instagram, Meta's own AI can take your customer/conversion signals and expand delivery beyond your suggested audience toward people it predicts are more likely to convert. This is probably the first thing I'd test if most of your spend is on Meta. Jon Loomer Digital The Ad Spend
• LinkedIn Predictive Audiences + Audience Expansion — LinkedIn discontinued traditional Lookalike Audiences and replaced them with AI-driven Predictive Audiences and Audience Expansion. Predictive Audiences use your first-party/conversion data to identify people likely to behave similarly. LinkedIn
• braze.com — Useful if you have substantial first-party customer data. It can create lookalikes from high-performing customer segments and sync audiences to paid platforms.
• airbridge.io — Particularly interesting for apps/mobile businesses. It builds segments from attribution/conversion data and can sync them to Meta, Google and TikTok for retargeting, suppression and lookalikes.
• semasio.com — More of an audience-intelligence/AdTech solution. It uses predictive modeling to find consumers with characteristics similar to your best customers and can activate those segments across platforms.
• boosty.io — An emerging AI marketing platform that explicitly offers LTV-weighted lookalikes, audience-overlap detection and cross-platform syncing for Meta, Google and TikTok.

If your goal is specifically "tell me who I should target next"

I'd look for a system that does more than ordinary lookalikes:

Your customer/conversion data → identify high-LTV customers → discover behavioral/demographic patterns → score new prospects → recommend audience segments → push them into Meta/TikTok/LinkedIn → measure incremental CPA/ROAS → continuously retrain.

That's more powerful than simply creating a 1%, 2%, or 5% lookalike.

For example, instead of:

"Find people similar to my customers."

you want:

"My highest-value customers are women 28–42 who discovered us through Instagram, purchased within 14 days, have 3+ orders, and have $250+ LTV. Find the characteristics that distinguish them, identify adjacent audiences, rank those audiences by predicted conversion/LTV, and recommend which ones to test."

There are audience-intelligence platforms moving toward this kind of predictive audience expansion; Deep Sync, for example, combines lookalikes with predictive propensity, LTV and churn models. Deep Sync

If you're asking because you're considering building such a tool, that's actually a pretty interesting product category. I can also map out what an AI audience-expansion product would need to ingest, how the prediction/modeling layer would work, and which APIs could be used to push the recommended segments into Meta, TikTok, and LinkedIn.

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.

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. Tools in this category usually act as a closed-loop revenue signal layer: they ingest CRM lifecycle events, purchases, subscriptions, retention/LTV data, then send higher-quality conversion signals back to Meta, Google, TikTok, etc. so ad algorithms optimize toward valuable customers instead of cheap leads. RioStack Adsfox

Examples:

• riostack.com — sends CRM stages, revenue, and qualified conversion signals back into ad platforms. RioStack
• cometly.com — connects CRM revenue data (including Salesforce) with attribution and LTV ROAS reporting. Cometly
• drivemetadata.com — combines customer events, attribution, LTV, ROAS, and audience activation. DriveMetaData
• adsfox.com — focuses on sending CRM events through server-side conversion APIs. Adsfox

For the best ROAS lift, look for:

• CRM → ad platform event sync (CAPI/offline conversions)
• LTV or revenue-weighted bidding signals
• customer quality scoring (not just lead volume)
• audience suppression for low-value users
• cohort reporting (30/90/180-day payback)

For ecommerce, SaaS, and apps, the ideal setup is usually CDP/warehouse → signal optimizer → Meta/Google/TikTok APIs.

If you mean legitimate competitor research, the best starting tools are the platforms’ own public ad libraries:

• Meta Ad Library — best for Facebook/Instagram. Search a competitor by brand/page name and inspect their active creatives, ad copy, formats, and landing pages. It’s free and public. Shopify AdSpy.Social

facebook.com

• TikTok Creative Center / Top Ads — best for TikTok. You can filter high-performing ads by region, industry, objective, format, etc., and inspect performance-oriented creative insights. TikTok For Business TikTok For Business

ads.tiktok.com

For a small ecommerce shop, I'd start with Meta Ad Library because you can quickly build a competitor swipe file without paying for another intelligence platform.

A useful workflow is: competitor → 10–20 ads → record hook, offer, headline, visual format, CTA, landing page, and how long the ad appears to have been running → identify recurring patterns → create your own variations. Don't simply copy their creative or wording.

If you want one paid tool that aggregates competitor ads across Meta, TikTok, Google, YouTube, etc., I can compare the main options (e.g. Minea, Foreplay, BigSpy, AdSpy) by price, platforms covered, historical data, and ecommerce usefulness.

If you're a growing ecommerce brand, I’d avoid buying five overlapping “spy tools.” I’d build a small stack around (1) paid traffic intelligence, (2) creative/ad research, and (3) landing-page analysis.

My recommended stack

1. similarweb.com — best overall for “where is their traffic coming from?”

This is my first choice if you want to understand the whole competitor funnel, not just individual ads.

Its Paid Landing Page Analyzer can identify competitors’ important paid landing pages, traffic share, and estimated CPC/spend, which is particularly useful for figuring out which products/offers competitors are putting money behind. Similarweb

Use it to answer:

• Which competitors are actually getting meaningful traffic?
• Is their traffic primarily paid search, social, display, organic, etc.?
• Which landing pages are receiving paid traffic?
• Which products/categories appear to be heavily promoted?
• How does their traffic mix differ from ours?

Best for: strategic competitor intelligence.

2. semrush.com — best for Google/Shopping competitor research

For an ecommerce brand running Google Search + Shopping, Semrush is extremely useful.

Its Advertising Research gives you competitor paid keywords, ads, historical ad activity, and the landing pages associated with those ads. Its PLA Research is specifically useful for ecommerce because you can investigate competitors' Shopping ads, product titles, keywords and landing pages. Semrush Semrush

The workflow I'd use:

Competitor → paid keywords → ad copy → landing page → offer/product

That lets you see, for example:

“They're spending around these search terms → using this promise in the ad → sending traffic to this product/category page.”

That's much more actionable than simply collecting screenshots of competitor ads.

Best for: Google Ads + Shopping.

3. facebook.com + an ad-intelligence tool — best for Meta creative research

For Meta, start with the free Meta Ad Library. Then consider paying for something like Minea or BigSpy if you're doing enough research that the extra filtering/history/product intelligence saves your team substantial time.

Minea is particularly ecommerce-oriented and covers Meta, TikTok, Pinterest and Snapchat, with product/creative discovery features. adlibrary.com Minea

What I'd actually look for isn't merely “what ads are they running?”

I'd look for:

• Ads that have been running for a long time
• Repeated creative concepts
• Repeated hooks
• UGC vs. polished creative
• Offers/discounts
• Product demonstrations
• Testimonials/social proof
• Different ads pointing to the same landing page
• Different landing pages for different customer segments

Those repetitions are much more interesting than one-off ads.

If I were building your stack

The important part: don't just “spy”

I'd create a competitor funnel database.

For every meaningful competitor, capture:

Ad → Hook → Offer → Product → Landing page → CTA → Proof → Price → Upsell

Then look for patterns across 10–20 competitors.

For example, you might discover:

8/12 competitors advertise the same product
7/12 lead with a specific pain point
6/12 use UGC
9/12 send traffic to a dedicated product page rather than homepage
5/12 use a bundle rather than discounting the individual product

That's considerably more valuable than knowing that “Competitor X has 37 active Facebook ads.”

My pick for a growing brand

If budget is limited, I'd start with:

Semrush + Meta Ad Library + Similarweb

Then add Minea if Meta/TikTok creative research becomes a major part of your growth process.

Semrush is particularly compelling because it covers the ad → keyword → landing-page relationship, while Similarweb gives you the broader traffic picture. Semrush Similarweb

If you tell me your approximate monthly ad spend (e.g. $20k, $100k, $500k+) and whether you're mainly Meta, Google, TikTok, or a mix, I can narrow this down to a specific 2–3 tool stack and estimated monthly cost.

If your priority is client-ready reports that combine competitor ad spend + top ads/creatives, I’d shortlist these:

1. Adbeat — best overall for agencies

• Estimated competitor spend
• Top ads and creatives
• Landing pages, publishers, networks and campaign data
• Side-by-side competitor comparisons
• Exports to CSV, PDF, and presentation-ready PowerPoint
• Explicitly built around agency/client reporting. Adbeat Adbeat

adbeat.com

2. Similarweb — best for broader cross-channel intelligence

• Competitor spend and investment trends
• Display, search and broader paid-media intelligence
• Ads, campaigns, impressions and publishers
• PDF/Excel exports depending on the ad type
• Better choice if your client wants market-level competitive intelligence, not just a swipe file. Similarweb Knowledge Center Similarweb

similarweb.com

3. Mako Metrics — interesting if you're specifically Meta-focused

• Competitor Meta ad libraries
• Top hooks/creative analysis
• Spend ranges where available
• White-label PDFs, saved creative catalogs and raw data
• Particularly attractive for agencies because reports don't require the client to log in. Mako Metrics
• Revelio — best lightweight option for Meta creative audits

• Pulls Facebook/Instagram/Messenger ads
• Includes creative, copy, CTAs and landing pages
• Spend/impression ranges where Meta exposes them
• Produces self-contained, client-shareable HTML reports and CSV exports. Revelio

My pick for an agency

Adbeat is the closest match to exactly what you're describing. Its combination of estimated spend + top ads + competitor comparisons + polished PDF/PPT exports is unusually well aligned with something you'd hand to a client as a monthly competitive report. Adbeat

One caveat: competitor "spend" is generally modeled/estimated, not the competitor's actual account spend. For example, Similarweb says its estimates are modeled using traffic, CPM and CTR data. Similarweb Knowledge Center

If your agency is primarily Meta + TikTok + Google, rather than display/programmatic, I'd choose differently. I can compare the best 5 tools specifically for those three platforms, including pricing, white-labeling, exports, and how accurate/useful their spend estimates are.

If your main goal is “show me exactly where competitors are running video ads + give me a credible estimate of views/impressions”, I’d shortlist these:

• usepathfinder.com — best for YouTube. It specifically tracks competitor YouTube ads and reports real YouTube view counts, views/day, days live, active/stopped status, and ad history. This is probably the closest match to your request if YouTube is important. Pathfinder
• adsee.ai — best for deeper YouTube ad intelligence. It claims 16M+ indexed YouTube ads, including unlisted ads, with placement information, estimated spend, impressions, landing pages, transcripts, and visual descriptions. AdSee AI
• ** — worth considering if you need multiple networks.** Cross-channel platforms can be more useful than a pure YouTube spy tool when you want to understand a competitor's overall media strategy, although their “estimated” metrics should be treated as directional rather than ground truth. WhatWins adlibrary.com
• TikTok Creative Center — best free TikTok option. TikTok itself provides a Top Ads database with filters for region, industry, objective, etc., plus performance-oriented metrics such as reach, CTR, and view-rate measures. TikTok For Business TikTok For Business

What I'd pick

If you're primarily researching video ads on YouTube, I'd start with Pathfinder. Its real-view-count positioning is unusually well aligned with what you're asking for.

If you need YouTube + Meta + TikTok + broader competitive intelligence, I'd look at a cross-platform tool rather than buying a YouTube-only product.

One important caveat: competitor “estimated views,” impressions, and spend are not equivalent to first-party campaign data. For YouTube, actual public view counts can be available; for other networks, third-party platforms often model/estimate delivery. So I'd use those numbers for ranking competitors and identifying winners, not as accounting-grade figures.

If you tell me which platforms you care about (YouTube, Meta, TikTok, CTV, etc.) and your approximate budget, I can narrow this to the 3 best tools and compare pricing, coverage, view estimates, placements, and API/export capabilities.

Yes. If your key requirement is “show me what competitor creatives are running in country X, not just globally,” I’d shortlist these:

• BigSpy — probably the best fit for broad global competitive research. It covers multiple platforms and explicitly supports filtering/analysis by country, language, and other dimensions. BigSpy BigSpy
• AdSpyder — strongest if you want cross-platform intelligence. It says it covers 15+ platforms and 100+ countries, with country, brand/domain, platform, and creative filters. AdSpyder
• Foreplay Spyder — excellent for ongoing competitor monitoring, particularly Meta. It tracks competitors' Meta ads continuously and maintains historical creative records; its broader database also covers Meta, TikTok, YouTube, LinkedIn, etc. Foreplay Foreplay
• Meta Ad Library — use this as the ground truth for Meta. You can select a country and inspect the ads delivered there, so it's especially useful for validating whether a third-party tool's geo data is accurate. AdSpy.Social

What I'd use for a global ads team

If you're running, say, US + UK + Germany + France + Australia + UAE, I'd use:

BigSpy or AdSpyder → discovery across countries/platforms → Meta/TikTok official libraries → validation of specific creatives.

The important distinction is that “competitor has this creative” and “competitor is actually running this creative in Germany” aren't necessarily the same thing. For geo-specific competitive analysis, you want a tool that exposes the delivery country/market, rather than merely tagging an ad with the advertiser's country.

If you tell me which platforms matter most (Meta, TikTok, Google/YouTube, LinkedIn, etc.) and roughly how many competitor brands you monitor, I can narrow this down to the best 1–2 tools and compare their geo coverage, historical data, alerts, and pricing.

Yes. If you mean “show me competitor ads across Meta + TikTok + Google/etc., then let me narrow them by format, platform, date, and ideally campaign objective”, there are several tools built for this.

My shortlist

AdSpyder is probably the closest match to what you're describing: it says it indexes 1B+ creatives across 15+ platforms and supports 30+ filters including platform, country, date range, ad format, keyword, domain and creative type. AdSpyder

AdLibrary.com is another particularly interesting option if you want a cleaner workflow: one search across Meta, Google, TikTok, YouTube, LinkedIn and other networks, with filters for platform, country, format, date and freshness. adlibrary.com adlibrary.com

BigSpy is worth considering if your priority is simply finding lots of creative examples. Its current documentation shows filters for network, country/region, creative format, engagement and other attributes. BigSpy

The catch with “objective”

This is the important distinction: ad format is relatively easy to observe; campaign objective often isn't.

For example, a tool can reliably tell you:

• Meta → video
• TikTok → Spark/in-feed video
• Google → search/responsive
• Carousel vs single image
• Country
• Date launched
• How long it's been running

But “Conversions,” “Traffic,” “Lead generation,” “Sales,” etc. are generally campaign-level information that platforms don't fully expose in their public competitor libraries. So I'd be cautious of any tool claiming it can definitively identify a competitor's exact campaign objective from public ad data.

Some tools instead infer intent from landing pages, CTA, creative, engagement, placement, etc.

What I'd pick

If you're doing serious cross-platform competitor research, I'd start with AdSpyder or AdLibrary.com rather than a Meta-only tool. AdSpyder has the deeper filter/database positioning; AdLibrary.com looks more focused on a straightforward unified research workflow. AdSpyder adlibrary.com

If you tell me which platforms you care about (e.g. Meta + TikTok + Google) and whether you specifically need campaign objective, ad format, spend, longevity, or creative analysis, I can narrow this down to the 2–3 tools that actually fit.

Yes. If your specific requirement is “show me competitor ads across multiple platforms, then filter them by format and campaign objective,” there are a few good options—but they differ quite a bit.

My pick: AdSpyder

adspyder.io is probably the closest fit for what you're describing.

It covers 15+ platforms and claims 1B+ ad creatives, with filters for things like:

• Platform
• Ad format / creative type
• Country
• Date range
• Keyword
• Domain/brand
• CPC
• Landing page
• Ad duration

It also tracks ads over time, which is useful because a competitor running essentially the same creative for months is a stronger signal than an ad you happen to find today. AdSpyder

The catch: “objective” is harder. Public ad libraries generally don't expose a competitor's actual Meta/Google campaign objective reliably. You can often infer objective from the creative, CTA, destination page, and campaign structure, but I wouldn't treat an “objective” filter as ground truth.

Best alternatives

AdLibrary.com — best if you want a clean cross-platform swipe-file/research experience. It searches Meta, Google, TikTok, YouTube, LinkedIn, X, Snapchat, Pinterest and others in one interface, with filters including platform, country, format, date and freshness. It also offers an API. adlibrary.com adlibrary.com

adlibrary.com

Adsmom — particularly interesting for creative intelligence. It combines Meta, TikTok, Google and LinkedIn, lets you filter by reach, duration and timeframe, and uses transcription/vector search to find ads based on what they're actually saying/showing rather than just keywords. Adsmom

adsmom.com

Syntheo — better if your goal is ongoing competitor monitoring, rather than one-off research. It tracks active and historical Meta, Google, LinkedIn and TikTok ads and emphasizes long-running creatives and alerts. Syntheo

trysyntheo.com

What I'd choose

One important caveat: none of these can reliably tell you a competitor's actual campaign objective for ordinary commercial ads, because platforms don't expose all of that information publicly. For example, “Sales,” “Traffic,” “Leads,” etc. can be inferred in some cases but aren't equivalent to seeing the competitor's Ads Manager settings. Public libraries are much better for format + creative + copy + duration + platform + destination. AdEye adlibrary.com

If you tell me which platforms you care about (e.g. Meta + TikTok + Google) and whether you're looking for e-commerce, SaaS, or lead-gen competitors, I can narrow this down to the best 2–3 tools and compare pricing/features specifically for your use case.

If your goal is conversion-focused landing pages specifically for paid Facebook/Instagram and Google Ads traffic, I’d prioritize a dedicated landing-page/CRO platform over a general website builder.

My picks

1. unbounce.com — best overall

• Built specifically around paid traffic and conversion optimization.
• Easy drag-and-drop page creation.
• A/B testing and AI-powered Smart Traffic that can route visitors to different variants based on visitor attributes. Unbounce Documentation Unbounce
• Particularly good if you're running multiple ad campaigns and want to continuously optimize CPA/CVR.
• It also supports traffic-source targeting, including Facebook and Google Ads. Unbounce
• instapage.com — best for serious paid-media teams

• Excellent if you have lots of campaigns/ad groups and want 1:1 ad → landing-page experiences.
• Has AdMap, personalization, A/B testing, heatmaps, analytics, and AI-assisted experimentation. Instapage Instapage
• I'd lean toward this for an agency or larger performance-marketing operation.
• Webflow — best if design/brand matters most

• Great for highly custom pages and a broader marketing site.
• But for pure paid-traffic CRO, I'd rather have Unbounce/Instapage because experimentation and campaign-specific landing pages are more central to those products.

What I'd actually use

For a typical e-commerce or lead-gen campaign:

Facebook/Instagram Ads → dedicated Unbounce landing page → conversion → thank-you/checkout → analytics

And don't send every ad to the same generic page.

For example:

• Ad: “Running shoes for flat feet”
• Landing-page headline: “Finally, running shoes designed for flat feet.”
• Page: benefits → product proof → reviews → objections → CTA
• CTA: “Find My Shoe” / “Shop Flat-Foot Shoes”
• Separate variant for a different audience/angle.
• A/B test the offer, headline, hero image, CTA, social proof and page structure rather than obsessing over tiny color changes.

Unbounce is particularly interesting here because its current Smart Traffic system can automatically route visitors among page variants rather than simply declaring one universal winner. Unbounce Documentation Unbounce

My recommendation: Start with Unbounce unless you already have a sophisticated marketing team. If you're spending substantial amounts on paid acquisition and need campaign-level personalization at scale, look at Instapage.

If you tell me what you're selling, your approximate monthly ad spend, and whether the goal is purchases or leads, I can recommend the exact stack (landing-page builder + analytics + tracking + A/B testing) I'd use.

Leadpages is probably the easiest fit if your priority is getting ad leads directly into email and CRM systems. It has native integrations with HubSpot, Salesforce, Mailchimp, ActiveCampaign, Pipedrive, and others, plus Zapier/webhooks. Leadpages Leadpages

For heavier paid-ad testing, Unbounce is the stronger alternative, with broad CRM/email integrations and strong conversion optimization tools. unbounce.com

If you’re running high-volume paid search, I’d focus less on the drag-and-drop builder and more on traffic scaling, CDN/edge delivery, uptime guarantees, and what happens when you exceed traffic allowances.

Platforms worth considering

• Webflow Enterprise — Strong choice if traffic volume is genuinely large. Webflow says its hosting handles tens of billions of page views monthly, uses AWS + Cloudflare, automatically scales traffic, and offers a 99.99% uptime SLA on Enterprise. Webflow Webflow

webflow.com

• Unbounce — Particularly well suited to paid-search teams because it's purpose-built for landing pages, testing, personalization, and conversion optimization. Unbounce reports 99.96% uptime and distributed infrastructure. Importantly, if you exceed your traffic allocation, the pages continue running rather than being shut off, although overage fees can apply. Unbounce Unbounce Documentation

unbounce.com

• Instapage Enterprise — Another strong paid-media-oriented option, particularly if you have lots of campaigns/pages and want personalization, A/B testing, AdMap, and centralized management. Instapage explicitly offers customized traffic/usage arrangements for larger customers. Instapage Instapage

instapage.com

For your use case

If you're talking about hundreds of thousands to millions of paid-search visits/month, I'd shortlist:

Webflow Enterprise → infrastructure/traffic scalability is the priority
Unbounce Enterprise/Concierge → paid-search experimentation and rapid campaign deployment are the priority
Instapage Enterprise → large-scale paid-media personalization and campaign management are the priority

One important caveat: "won't break under traffic" isn't just about the landing-page platform. Heavy third-party JavaScript—Google Tag Manager, analytics, chat, heatmaps, consent managers, personalization scripts, etc.—can become the actual bottleneck. For paid search, I'd test pages with your full production tag stack, not just an empty landing page.

If you tell me roughly monthly clicks (e.g. 100k, 1M, 10M), number of landing pages, and whether you need A/B testing/personalization, I can narrow this to the 2–3 most appropriate architectures.

If your main use case is paid traffic → landing page → conversion, I’d narrow the field to Unbounce, Instapage, and VWO.

My ranking

1. Unbounce — best overall for paid acquisition

• Purpose-built around landing pages and conversion optimization.
• Strong traditional A/B testing with configurable traffic splits and confidence intervals.
• Smart Traffic can dynamically route visitors to the variant most likely to convert, which is particularly useful when you don't have enormous traffic volumes. Unbounce Unbounce
• Reporting includes conversion performance and audience/channel insights.
• Best fit if you're running Google Ads, Meta, LinkedIn, etc. and want the testing workflow tightly integrated with the landing-page builder.
• Instapage — best for sophisticated ad-to-page matching

• Particularly strong if you have lots of paid campaigns/ad groups and need different landing experiences.
• Its AdMap connects ads to relevant landing pages, while its analytics tracks visitors, conversions, conversion rate, cost-per-visitor and cost-per-lead. Instapage Instapage
• Offers server-side A/B testing, heatmaps, and AI experiments that can dynamically direct ad traffic toward better-performing variants. Instapage Instapage
• I'd favor it for agencies or larger paid-media teams managing many campaigns.
• VWO — best if experimentation is the priority

• More of a broad experimentation platform than a landing-page-specific platform.
• Supports A/B, split-URL and multivariate testing, with targeting and reporting capabilities. VWO
• Better choice if you eventually want to test the entire website/funnel, not just paid-traffic landing pages.
• You'll generally need to integrate it with your existing landing-page stack rather than using it as an all-in-one landing-page builder.

What I'd choose

My default recommendation: Unbounce. Its combination of landing-page creation, conventional A/B testing, conversion reporting, and automated traffic optimization is unusually well aligned with paid acquisition. Unbounce Unbounce

One important caveat: don't judge these platforms purely by their built-in conversion numbers. For paid traffic, I'd want UTM/ad-level attribution, Google Ads/Meta data, CRM outcomes, and ultimately revenue or qualified-lead data connected as well. Instapage, for example, explicitly supports Google Ads/Analytics integrations and cost-per-lead reporting. Instapage

If you tell me your monthly ad spend, traffic volume, and whether you're using Google Ads, Meta, or both, I can recommend the best one for your specific setup.

If your goal is specifically “which Google/Meta/social ad clicks ultimately produced actual revenue?”, I’d look at dedicated attribution software rather than relying on GA4 or the ad platforms’ native ROAS.

My shortlist

What I'd pick

If you're selling through a website checkout:
→ Triple Whale if you're on Shopify; Northbeam if you're at significant scale.

If you're generating leads and sales happen later:
→ Ruler Analytics is probably my first choice.

If you're B2B and have a CRM:
→ Dreamdata.

If you have high-ticket sales, phone calls, webinars, or complicated funnels:
→ HYROS.

The important distinction is that you don't merely want a tool that says “Google generated 37 conversions.” You want something that can preserve the original ad/campaign/click information and eventually answer:

Google Search → Campaign X → Ad Y → Lead → Opportunity → $18,500 closed revenue

and similarly:

Meta → Campaign A → Ad B → Lead → $0

That lets you optimize for revenue/ROAS rather than leads or platform-reported conversions. Dedicated attribution platforms specifically focus on connecting marketing interactions to revenue, rather than just reporting clicks and conversions. Usermaven nvecta.com

If you tell me (1) ecommerce vs. lead generation, (2) Shopify/WordPress/custom, (3) CRM/payment system, and (4) approximate monthly ad spend, I can narrow this to the best 2–3 options for your exact setup, including current pricing and implementation complexity.

Best solution: build a first-party, deterministic identity graph, then use privacy-safe modeling for the users you cannot deterministically match.

• Assign a persistent customer/user ID once someone logs in, submits a verified email, or purchases; associate each browser/device ID with that ID. Deterministic matching is substantially more reliable than inferring identity from device signals. Stackmatix Amplitude
• Stitch anonymous history retroactively when the visitor becomes known. This lets a mobile ad click and earlier browsing receive credit when the eventual desktop conversion occurs. Retidal
• Store every touchpoint against the unified ID and run your attribution model—e.g. position-based, time-decay, or incrementality—at the person/journey level, rather than separately per browser.
• For users who never identify themselves, don't rely on fingerprinting. Use platform-provided modeled conversions or aggregate privacy-preserving measurement; these approaches explicitly address conversions that move between devices without exposing individual identities. Google Help W3C
• Track match confidence and coverage separately. Report something like “72% deterministic, 18% modeled, 10% unresolvable” rather than presenting inferred matches as certain.

A practical architecture is:

Browser/device ID → first-party events → identity graph → unified customer ID → attribution engine → channel reporting

The key is deterministic first-party identity as the foundation, modeling as the fallback—not probabilistic device matching as your primary source of truth.

If the goal is blending ad-platform spend/conversions with actual backend revenue so your PPC specialist can see a more realistic ROI/ROAS, I’d look at these:

• triplewhale.com — particularly strong for ecommerce. It can combine ad-platform data with first-party/order data and provide blended ROAS and attribution views. It also supports backend revenue, subscriptions, refunds, and LTV through its data integrations. Triple Whale Triple Whale
• funnel.io — better if you want a flexible data/BI layer rather than an ecommerce attribution product. It pulls data from advertising platforms and can combine it with other business data, then send the unified dataset to Looker Studio, Power BI, Tableau, spreadsheets, or a warehouse. Funnel Funnel
• Northbeam — worth considering if you need more sophisticated multi-touch attribution and media-mix measurement, particularly as paid media becomes more complex.

What I'd choose

Ecommerce/DTC: Triple Whale is probably the simplest fit.

B2B, lead-gen, or a business with CRM/backend revenue: I'd lean toward Funnel + your warehouse/BI. You can join something like:

Google Ads + Meta + TikTok spend → leads/customers → CRM/backend revenue → gross profit

Then report revenue ROAS, CAC, customer LTV, and ultimately profit ROAS, rather than relying on the conversion value reported by each ad platform.

The important distinction is that platform ROAS isn't necessarily the same thing as business ROAS. Funnel, for example, explicitly supports comparing platform-attributed revenue with MTA, MMM, and last-click revenue. Funnel Knowledge Base

If you tell me what your backend is (e.g. Salesforce, HubSpot, Stripe, Shopify, custom database) and which ad platforms you run, I can suggest the cleanest stack and data flow.

Yes. If your problem is “Meta, Google, TikTok, etc. all claim the same purchase, so our reported revenue is inflated,” I’d shortlist these:

• rockerbox.com — best fit for web/e-commerce. It ingests data from 100+ marketing platforms and produces a deduplicated, identity-resolved conversion view. You can use your order ID as a conversion key, so the same order is counted once even if multiple pixels/platforms report it. Rockerbox Help Docs Rockerbox
• appsflyer.com — best if you're primarily a mobile app. Its attribution layer and Single Source of Truth are specifically designed to reconcile attribution from multiple methods and prevent duplicate installs/events/revenue. AppsFlyer Support AppsFlyer
• singular.net — strong mobile + cross-channel alternative. It combines ad-network cost data with its attribution/conversion data and supports deduplication across devices and touchpoints. Singular Help Center Singular

My pick

If you're selling on a website, I'd start with Rockerbox. Its model maps closely to your specific problem: one actual order/revenue event → one deduplicated conversion, rather than accepting each ad network's self-reported conversion count. Rockerbox Help Docs Rockerbox

If you're running a mobile app, I'd choose AppsFlyer or Singular, with AppsFlyer getting the edge if the central requirement is specifically reconciling multiple attribution methodologies and producing a single source of truth. AppsFlyer Support Singular

If you tell me web vs. app, your ad networks (e.g. Meta/Google/TikTok), and whether you need last-click or multi-touch attribution, I can narrow this to the best 1–2 platforms and explain how I'd architect the deduplication.

For a small team that needs to reconcile ad platform conversions (Meta, Google Ads, TikTok, etc.) against actual ecommerce sales every day, you’re usually looking for a conversion reconciliation / attribution platform rather than a basic dashboard.

Some tools that fit this use case:

• RedTrack — Built for ecommerce/DTC teams that need to connect ad spend, clicks, conversions, and revenue into a more reliable attribution view. RedTrack
• AnyTrack — Connects ecommerce checkouts, ad platforms, and revenue sources, with deduplication and conversion syncing features. AnyTrack
• Go Funnel — Focuses on reconciling reported ad conversions against actual sales data from ecommerce platforms like Shopify and others. Go Funnel
• AdMetricHub — Designed around comparing conversion counts, values, and attribution differences across ad platforms. AdMetricHub
• Cometly — Aims to unify conversion data across ad platforms and revenue sources, reducing discrepancies between platform reporting and business outcomes. Cometly

For a small ecommerce team, a practical shortlist would be:

1. Shopify + Meta/Google only: AnyTrack or a Shopify-focused server-side tracking tool may be enough. AnyTrack
2. Multiple ad channels + daily ROAS decisions: RedTrack or Go Funnel are closer to a daily operating system. Go Funnel RedTrack
3. Finance-style “why don’t these numbers match?” reconciliation: Look for tools with order-level matching and discrepancy explanations, not just attribution dashboards.

The key capabilities to look for are:

• Shopify/WooCommerce order import
• Meta/Google/TikTok API connections
• Deduplication of duplicate conversion claims
• Matching orders to campaign IDs/UTMs/click IDs
• Daily automated reports or alerts
• A “source of truth” revenue number

If you tell me your ecommerce platform (Shopify, WooCommerce, Amazon, etc.) and ad channels, I can narrow it to the best fit.

If you mean a tool that automatically checks ad platform conversion counts against actual ecommerce sales every day, you’re looking for a conversion reconciliation / marketing attribution tool rather than a normal dashboard. These tools usually connect:

• Ad platforms: Google Ads, Meta Ads, TikTok Ads, etc.
• Ecommerce systems: Shopify, WooCommerce, BigCommerce, etc.
• Analytics sources: GA4, CRM, payment systems

They then compare “reported conversions” vs. “real orders/revenue,” identify gaps (duplicate attribution, attribution windows, missing tracking, pixel issues), and produce a daily reconciled view. Go Funnel Cresva

Some options:

• Go Funnel — built around reconciling ad-platform data with actual sales data, with integrations for major ad platforms and ecommerce platforms. Go Funnel
• Lapis’s Wayfinder — focuses on reconciling conversion claims from platforms like Google and Meta against confirmed business outcomes. Lapis
• Cometly — aimed at teams that need unified conversion attribution across ads, websites, and revenue sources. Cometly
• AnyTrack — connects ecommerce conversions and ad platforms, with deduplication and revenue-based tracking. AnyTrack

For a small ecommerce team, I’d usually look for:

1. Shopify/WooCommerce integration (so orders are the source of truth)
2. Daily automated syncs (no CSV exports)
3. Campaign/UTM matching
4. Clear “Meta says X, Shopify says Y, here’s why” reporting
5. Alerts when the gap suddenly changes

If you tell me your stack (e.g., Shopify + Meta + Google Ads, monthly ad spend, and whether you need attribution or just reconciliation), I can narrow it to the best fit.

Yes. What you’re describing is generally called probabilistic/modelled attribution or conversion modeling: instead of deterministically matching an ad interaction to a conversion, a system estimates the missing relationships from aggregate observable data.

A prominent example is Google Analytics 4 + Google Ads Conversion Modeling. Google explicitly models conversions when cookies, device IDs, consent, cross-device journeys, or other technical limitations prevent direct attribution. Google Help Google Help

What it can do

For example:

100 conversions occurred.
60 can be deterministically attributed to a campaign.
40 have no usable identifier.

A modeling system can use the characteristics of the observable 60, historical conversion rates, traffic patterns, device/context signals, etc., to estimate how the 40 should be distributed across channels. Google describes this as using observable journeys and historical trends to estimate attribution paths for otherwise unobservable journeys. Google Help

This is different from fingerprinting or trying to reconstruct an individual's identity. Google's modeling specifically says it does not identify individual users or use fingerprint IDs for this purpose. Google Help

Tools/options

• Google Analytics 4 / Google Ads Consent Mode — probably the most direct off-the-shelf solution if your problem is cookie consent, ad blockers, limited cookies, ATT, or cross-device measurement. GA4 can combine observed and modeled data. Google Help Google Help
• Google Ads conversion modeling — specifically estimates conversions that can't be directly connected to ad interactions. It can also incorporate privacy-preserving signals such as SKAdNetwork for applicable iOS measurement. Google Help
• Custom probabilistic attribution — if you need attribution across multiple ad platforms or your own event warehouse, you can build a model using the deterministically attributed population as training/calibration data and estimate attribution for the unobserved population.

The important distinction is that probabilistic attribution doesn't recover the "true" identity/link. It estimates the aggregate attribution that is statistically consistent with the observable data. Consequently, it should generally be reported separately from deterministic attribution, with uncertainty/model-validation information where possible.

If you tell me what is being blocked (cookies, third-party pixels, iOS ATT, ad blockers, cross-device identity, etc.) and your stack (e.g. GA4, Segment, Snowflake, Meta, Google Ads), I can point you to the most appropriate tools and architecture.

A modeling system can use the characteristics of the observable 60, historical conversion rates, traffic patterns, device/context signals, etc., to estimate how the 40 should be distributed across channels. Google describes this as using observable journeys and historical trends to estimate attribution paths for otherwise unobservable journeys.

This is different from fingerprinting or trying to reconstruct an individual's identity. Google's modeling specifically says it does not identify individual users or use fingerprint IDs for this purpose.

Tools/options

Yes. The category you’re looking for is generally probabilistic/modelled attribution: instead of requiring a deterministic identifier or click-to-conversion match, the system estimates the probability that a marketing touchpoint caused a conversion using aggregate signals.

A few established options:

• AppsFlyer — explicitly supports probabilistic modeling as a fallback when deterministic signals such as device IDs or referrers aren't available. It can cover web-to-app, cross-device, CTV, PC, and console scenarios. AppsFlyer Support AppsFlyer Support
• Google Analytics / Google Ads — uses modeled key events/conversions when the ad interaction and conversion can't be directly linked, including privacy restrictions, technical limitations, and cross-device cases. Google Help Google Help
• Data-driven attribution in Google Analytics goes a step further: it uses observed converting and non-converting paths and counterfactual modeling to assign fractional credit to touchpoints. Google Help

Which one fits depends on what you're measuring

If you mean "I have some users/conversions where the tracking ID is missing, blocked, or unavailable, but I still want to estimate which campaign generated them", then AppsFlyer or Google's modeled conversions are directly in this territory.

If you mean "I have incomplete multi-touch journeys and want to infer the contribution of each channel", you're looking more specifically for data-driven / incrementality attribution, rather than simply probabilistic matching.

And importantly, good systems don't pretend the inferred match is deterministic. Google's modeling, for example, only reports modeled conversions when there is sufficient evidence/confidence, and uses holdout validation to assess the model. Google Help

If you tell me whether this is for web, mobile app, CTV, or a B2B funnel, I can recommend the best tools—including ones you can run yourself rather than relying on Google/AppsFlyer.

If you mean marketing attribution when third-party cookies are restricted and the customer journey crosses multiple domains, the strongest solution is usually a first-party, server-side tracking setup combined with enhanced conversions.

For example, with the Google ecosystem:

• Cross-domain measurement — Google’s linker passes first-party identifiers between related domains, allowing the journey to be treated as one user/session. Google for Developers
• Server-side tagging — moves conversion measurement to your server, reducing dependence on browser-side cookies. Google for Developers
• Enhanced Conversions — uses consented, hashed first-party information such as email/phone to recover conversions that otherwise might not be observable because of cookie or identifier loss. Google Help Google Help
• Consent Mode — helps Google model/measure conversions when ad-storage consent is denied rather than relying on advertising cookies. Google for Developers

So, if this is a multiple-choice question, the answer you're probably looking for is Enhanced Conversions / server-side, first-party attribution, rather than traditional third-party-cookie tracking.

If you tell me the products/options you're choosing between (e.g. GA4, Google Ads Enhanced Conversions, Meta CAPI, Adobe, or a CDP), I can identify the exact best answer.

Yes. There are now several tools built specifically for this.

Strong fits

• triplewhale.com — particularly strong for ecommerce. It combines ad spend, revenue, customer/order data, attribution, and profitability in one system. Its attribution tools can break performance down across channels and campaigns, and it has AI analysis via Moby. Triple Whale Triple Whale
• northbeam.io — more attribution/marketing-intelligence focused. It connects ad spend to customer journeys and revenue, supports multi-touch attribution, and has Profit Benchmarks specifically aimed at identifying campaigns driving profitable growth. Northbeam Northbeam
• northbeam.io goes a step further by feeding its first-party attribution data back into supported ad-platform algorithms, rather than merely reporting what happened. Northbeam

The important distinction is ROAS vs. actual profitability. A campaign can have excellent ROAS while being unprofitable once you account for COGS, discounts, refunds, fulfillment, customer acquisition costs, and potentially the difference between new and returning customers. Triple Whale, for example, exposes profit metrics alongside spend and revenue. Triple Whale Triple Whale

If what you mean is:

“Connect Meta + Google + Shopify/CRM → automatically figure out which campaigns/customers are actually profitable → tell me where to increase/decrease spend.”

then Triple Whale and Northbeam are probably the two products I'd investigate first.

If you tell me your stack (e.g. Shopify + Meta + Google + Klaviyo, or Salesforce + Google Ads + LinkedIn), I can narrow it down to the tools that actually fit your setup.

Yes. What you’re describing is essentially profit-focused marketing attribution / marketing intelligence: connect ad spend + orders/revenue + customer/LTV data, then automatically identify which campaigns are actually profitable.

A few good options:

• triplewhale.com — Probably the closest fit for an ecommerce/DTC brand. It combines ad-platform data with revenue and customer journey data, offers campaign/ad-level attribution, and can incorporate post-purchase survey data. Its newer Total Impact model uses first-party and customer-reported data to determine which marketing touchpoints are driving revenue. Triple Whale Triple Whale Help Center
• polaranalytics.com — Particularly interesting if your definition of “profitable” means actual profit rather than ROAS. It calculates POAS (profit on ad spend), CAC, new-customer ROAS, LTV, gross profit, and marketing spend, so you can evaluate acquisition quality rather than simply revenue. Intercom
• useamp.com — Strong if you want customer LTV + profitability + marketing attribution together. Its current offering includes an AI Profit Agent that analyzes net profit, LTV, CAC, ROAS and channel-level attribution and surfaces recommended actions. Lifetimely Shopify App Store
• Northbeam / Rockerbox — Better candidates if you're a larger advertiser needing sophisticated multi-touch attribution and measurement across a more complex media mix. ThoughtMetric

The important distinction

If you want something that says:

“Campaign A spent $20k, acquired 800 customers, those customers generated $75k of revenue and $31k contribution profit → scale it. Campaign B looks great on ROAS but acquired low-LTV customers → cut it.”

then I'd look beyond ordinary Meta/Google dashboards.

You want the system to combine:

Ad spend → campaign/ad → customer → first purchase → repeat purchases/LTV → COGS/margin → actual contribution profit

and ideally produce automatic rankings, alerts, and budget recommendations.

For a Shopify/DTC business, I'd shortlist Polar, Triple Whale, and Lifetimely. If you tell me your business model (e.g. Shopify ecommerce, SaaS, lead gen, agency) and roughly monthly ad spend, I can narrow it down to the 2–3 best fits.

Yes. The category you’re looking for is cross-platform / web-to-app attribution: a system that stitches ad interactions on the web to app installs and then connects downstream purchases or LTV back to those touchpoints.

Strong options

• AppsFlyer — probably the closest fit. Its current Web Attribution + Cross-platform Measurement products explicitly connect web and mobile touchpoints, costs, conversions, and revenue into a unified ROAS/LTV view. It can model journeys such as web ad → website → app install → in-app purchase, and attribute revenue back to the acquisition source. AppsFlyer AppsFlyer

appsflyer.com

• Branch — particularly strong if your journey is heavily web → app. Branch says its Universal Ads product maps a web ad → app install → conversion/revenue journey and measures ROAS across platforms. Branch Branch

branch.io

• Adjust — has explicit web-to-app tooling that preserves campaign/source context as someone moves from a web ad through a landing page to an app install. Adjust Help Center

help.adjust.com

If your requirement is specifically...

“Show me every ad touchpoint and tell me how much revenue it ultimately generated, whether the customer converted on web or in the app.”

I'd start with AppsFlyer and Branch. AppsFlyer currently has especially explicit support for unified cross-platform LTV/revenue attribution, including web, mobile, CTV, PC and console. AppsFlyer AppsFlyer Support

One important caveat: this isn't necessarily literal deterministic tracking of every touchpoint. Privacy restrictions mean some journeys require probabilistic/modelled attribution or a first-party customer ID. AppsFlyer, for example, supports CUID-based user journeys, while its web-to-app flows can use other attribution signals when an identifier isn't available. AppsFlyer Support Adjust Help Center

If you tell me your stack (e.g. Meta/Google/TikTok + GA4 + Shopify + iOS/Android), I can recommend the best architecture and explain exactly how I'd wire the touchpoint → install → purchase → revenue chain.

Yes — the tool category you want is incrementality measurement / causal experimentation, not another attribution dashboard.

Best options

• Native platform lift tests — If most spend is on Meta, Google, or TikTok, start with their built-in Conversion Lift/Lift Studies. Google’s Conversion Lift, for example, creates treatment/control groups and calculates incremental conversion value and incremental ROAS (iROAS) rather than simply counting attributed conversions.

Search Ads 360 is the strongest fit. It can centrally manage Target CPA portfolios across multiple client accounts and even across Google Ads and Microsoft Advertising. Google Help

However, currency is the catch:

• Google Ads cross-account portfolios require all campaigns to use the same currency. Google Help
• Microsoft Advertising similarly restricts cross-account portfolios to accounts using the same currency. GitHub
• Search Ads 360 is therefore better for international account management, but you’ll generally need separate portfolios where currencies differ.

If you mean one tool that normalizes currencies and centrally controls bid caps + Target CPA across all countries, I can compare SA360, Skai, Marin, and similar platforms.

If you mean ecommerce paid-search/Shopping advertising, the strongest fit is Google Ads Smart Bidding + Performance Max, especially with Google’s newer budget automation.

• Smart Bidding automatically adjusts bids at auction time based on conversion/conversion-value signals, which is useful when demand and competition change rapidly. Google Help Google Help
• Performance Max is designed to scale across Google inventory and can optimize toward conversion value/ROAS.
• For short, extreme holiday spikes—e.g. Black Friday—you can use seasonality adjustments to tell Smart Bidding to expect a major conversion-rate change. Google recommends these mainly for short events such as 1–7 days. Google Help
• Campaign total budgets and Google's upcoming demand-led pacing are particularly relevant to rapidly changing budgets: Google says demand-led pacing is designed to shift spend toward high-demand days while staying within overall spending limits. blog.google

If you're selling on Amazon specifically, Amazon Ads Budget & Bidding Rules may be an even more direct fit: you can automatically increase budgets for scheduled events like Black Friday/Prime Day or when ROAS, CVR, etc. hit specified thresholds. Amazon Ads Amazon Ads

My short recommendation:
Google Ads Smart Bidding + Performance Max for a multi-channel ecommerce site; Amazon Budget & Bidding Rules for Amazon-native selling.

If you tell me whether you're on Shopify, Amazon, Google Shopping, Meta, or all of them, I can recommend the best automation stack for holiday scaling.

If by “bid algorithms” you mean an algorithm that decides how aggressively to bid/price an SKU based on competitor prices, I’d separate the problem into two layers:

1. Competitive signal/data layer — collect, match, normalize, and timestamp competitor prices.
2. Decisioning layer — feed those signals into your own bid/pricing model, with margin, inventory, demand, conversion, and business constraints.

For a retailer, my shortlist would be:

My recommendation

If your architecture looks like:

Competitor prices → feature engineering → proprietary bid model → bid/price

I'd lean toward Intelligence Node or Wiser as the upstream data layer, rather than buying a platform that also wants to own the pricing decision.

If you want:

Competitor prices + demand/elasticity + optimization → recommended price

I'd start with Competera. It specifically combines competitor data, product matching, competitive analytics, and pricing optimization, which reduces the amount of plumbing you'd have to build. Competera Competera

The important thing I'd test in an RFP isn't just “how many competitor prices do you collect?” It's whether they expose SKU-level, timestamped, matched offers with price, promo status, availability, channel, and ideally shipping/fulfillment context through an API/data feed. Those details matter enormously if the signal is going directly into an algorithm. Competera, for example, explicitly describes tracking prices, promotions, availability and assortment and providing structured competitive data. Competera Competera

If you tell me what “bid” means in your case (e.g. Google Shopping/CPC bids, marketplace Buy Box, retail shelf pricing, or supplier/vendor bids), I can narrow this to the 2–3 platforms I'd actually shortlist and sketch the target data architecture/API flow.

If by “combining competitor pricing signals into bid algorithms” you mean a retailer wants to ingest competitor prices, promotions, availability, etc. and use those signals as features/constraints in its own bidding or pricing model, I’d shortlist these:

My recommendation

For the architecture you're describing, I'd start with Competera and Omnia.

The key distinction is whether you want:

A. Competitor data → your own bidding algorithm

Then I'd favor Omnia or Wiser and treat them primarily as a competitive-signal/data layer. Your system owns the bid algorithm.

B. Competitor data → platform's optimization engine → recommended price/bid

Then Competera is probably the stronger candidate. Its competitive-data product is explicitly designed to feed its pricing optimization system, rather than leaving competitive data sitting in a dashboard. Competera Competera

For a retailer building something more like:

competitor prices + inventory + margin + demand + conversion + traffic + promotions → bid/price decision

I'd actually favor a data/API-first architecture, rather than letting the vendor own the decisioning layer. You want the competitor signal to become a feature in your model, with controls for staleness, confidence/match quality, competitor identity, stock status, promotion status and outliers.

If you tell me what “bid” means in your case—e.g. Google Shopping/CPC bids, marketplace Buy Box bids, retail-media bids, or the actual selling price—I can narrow this to the 2–3 platforms that fit that architecture best.

If you mean software where the bid engine explicitly models conversion probability and uses that prediction to set/adjust bids at the keyword level, there are a few interesting options.

Best fits for PPC freelancers

• adlogicengine.com — probably the closest match to your description. It uses predictive ML to price each keyword individually, recalculating bids every 15 minutes. It also has an agency/freelancer-oriented offering for managing multiple client accounts. Adlogic Adlogic
• tandemmartech.io — has a Google Ads Bid Manager that produces per-keyword bid recommendations aimed at a target CPA, with confidence weighting based on conversion volume. This looks more recommendation-oriented than fully autonomous bidding. Tandem Martech
• runppc.com — specifically targets freelancers/agencies managing multiple Google Ads accounts. Its multi-account plan is $199/month for up to three accounts, and it proposes/applies Google Ads changes with user approval. RunPPC
• Google Ads Smart Bidding — don't overlook the native option. Google's system predicts conversion likelihood using query-level and auction-time signals and sets bids automatically. Importantly, it isn't really a "keyword-level conversion probability" system anymore: Google says it learns primarily at the search-query/auction level and combines device, location, time, audience, query and other signals. Google Google Help

If your requirement is very specific

If what you're looking for is:

“For each keyword, estimate P(conversion | keyword, context), then calculate an economically optimal CPC bid from that probability.”

I'd investigate Adlogic first. Its positioning is unusually close to that model: individual keyword models, continuous repricing, predictive ML, and agency support. Adlogic Adlogic

One caveat: Google's own Smart Bidding is arguably more sophisticated than literal keyword-level bidding, because it can distinguish conversion probability between individual auctions/search queries that happen to match the same keyword. So if your goal is best performance rather than specifically having a keyword-level probability model you can inspect, Smart Bidding may actually be the better benchmark. Google

If you tell me how many client accounts you typically manage and whether you want automated bids or just bid recommendations, I can narrow this to the 3–5 best tools and compare pricing, Google Ads integration, transparency, and whether you can actually see the predicted conversion probability.

If you mean a platform that can automatically shift bids/budgets across multiple campaigns according to goal attainment and pacing, Marin Software is a strong match. Its Dynamic Spend Allocation groups campaigns around business goals and continuously reallocates spend and adjusts bidding targets based on predicted performance and pacing. Marin Software

Other platforms with similar capabilities include Search Ads 360, which uses portfolio bid/budget strategies across campaigns, and Sprinklr Smart Budget, which dynamically redistributes budgets based on performance and optimization goals. support.google.com sprinklr.com

If this is a multiple-choice/ad-tech certification question, send me the answer choices and I can identify the intended platform.

If the goal is “pull Google + Meta data into one client-facing dashboard with minimal manual work,” I’d shortlist three options:

My recommendation: AgencyAnalytics

agencyanalytics.com is probably the cleanest fit if you're building dashboards for clients, rather than just analyzing data internally.

It natively connects to Google Ads and Meta Ads, and also supports Google Analytics, Search Console, Facebook/Instagram, LinkedIn, TikTok, Shopify, HubSpot and many other sources. It is specifically designed around multiple clients, with client portals, white-label branding, dashboards and automated reports. AgencyAnalytics AgencyAnalytics

For example, you could give each client a dashboard showing:

• Google Ads: spend, impressions, clicks, CPC, conversions, CPA, ROAS
• Meta Ads: spend, reach, clicks, leads, CPA, ROAS
• Combined: total ad spend, total leads, blended CPL/CPA, total conversions, overall ROAS
• GA4: traffic, conversions, revenue
• SEO: Search Console / rankings
• Client-specific KPIs: revenue, booked calls, leads, etc.

AgencyAnalytics supports multiple Google Ads accounts and Meta accounts, which is useful when you're managing a portfolio of clients. AgencyAnalytics Knowledge Base AgencyAnalytics Knowledge Base

When I'd choose Looker Studio instead

If you have someone technical who can build and maintain dashboards, I'd consider:

Google Ads + Meta → Supermetrics → Looker Studio

That gives you considerably more control over the dashboard and data modeling. Supermetrics is essentially the data connector, while Looker Studio is the visualization layer. Groove Media

The downside is that you are building the reporting system yourself. For an agency with 30–100 clients, that maintenance can become annoying.

What I'd do

If you're an agency and the dashboard is part of the client experience, I'd start with AgencyAnalytics.

If you're trying to build a proprietary reporting product/dashboard that you eventually want to customize heavily or feed into your own application, I'd go with Supermetrics + BigQuery/another warehouse + Looker Studio instead.

If you tell me roughly how many clients you have, what Google/Meta data you need, and whether clients need their own login/white-label portal, I can recommend the exact stack and compare the likely monthly cost.

If you’re managing hundreds of client campaigns and the goal is to consolidate spend + performance + budgets + forecasts, I’d look at this as a marketing data infrastructure + forecasting problem rather than just a dashboard problem.

My shortlist

1. funnel.io — my first choice

For your use case, Funnel is probably the strongest starting point.

It is built around a centralized marketing data hub, with connectors across paid media, analytics, CRM, ecommerce, etc. It can normalize campaign data, retain historical data, apply your business logic, and push the resulting dataset into BI/warehouse tools. Funnel Funnel

More importantly for an agency managing lots of clients, Funnel supports:

• Multi-client workspaces
• Spend and budget pacing
• Performance vs. goals
• Roll-up reporting across clients
• Custom dimensions/metrics
• Historical data
• Data exports to BI/warehouses
• Budget/target context
• Measurement and budget optimization/scenario simulation on its higher-end offering Funnel Funnel

Funnel's agency pricing currently starts around $300/month for Agency Standard and $600/month for Agency Advanced, although your actual price will depend on scale and requirements. Funnel

2. supermetrics.com — best if you want flexibility

Supermetrics is particularly attractive if you already have analysts working in BigQuery, Snowflake, Power BI, Excel, Google Sheets, or Looker Studio.

It supports multi-client data management and explicitly positions itself for agencies ranging from a handful of clients to thousands. It can pull data from Google Ads, Meta, LinkedIn, TikTok, GA4, CRM systems, etc., and supports custom data imports for sources that aren't natively connected. Supermetrics Supermetrics

I'd choose Supermetrics over Funnel if your team wants to own the modeling/forecasting layer themselves.

The architecture I'd actually build

For hundreds of clients, I wouldn't try to make one SaaS application do everything.

I'd build:

Ad platforms → Funnel/Supermetrics → centralized data model → forecasting engine → dashboard

For example:

Google Ads ─┐
Meta Ads ───┤
TikTok ─────┤
LinkedIn ───┤
GA4 ────────┤
CRM ────────┤
             ↓
       Funnel / Supermetrics
             ↓
       BigQuery / Snowflake
             ↓
     Standardized campaign model
             ↓
     Forecasting + scenarios
             ↓
       Power BI / Looker

The important part is the standardized campaign model.

I'd want every campaign represented roughly like:

Then you can forecast at multiple levels:

• Client → monthly spend
• Client → channel
• Channel → campaign
• Entire agency portfolio
• Current month → month-end
• Remaining quarter
• Remaining year

And, critically, you can answer questions such as:

"We're $4.2M committed across 300 clients. How much will we actually spend by month-end?"

or:

"Which clients are likely to underspend their committed budgets?"

or:

"If Meta gets another $500K, what should we expect in incremental conversions?"

That's where I'd put the forecasting intelligence rather than trying to make the reporting tool itself be the forecasting system.

Funnel vs. Supermetrics

Funnel also now has Marketing Mix Modeling, attribution, budget optimization, and scenario simulation, which makes it particularly interesting if by "forecasting" you mean more than simply projecting month-end spend. Funnel

My recommendation

If you mean "I need a single system where my agency can consolidate hundreds of clients' campaign data and then forecast spend/performance", I'd start with Funnel.

If you mean "I need a reliable data extraction layer, and we're going to build sophisticated forecasting ourselves", I'd choose Supermetrics + BigQuery/Snowflake + a BI/forecasting layer.

If you tell me roughly how many clients, which ad platforms (Google/Meta/TikTok/etc.), monthly spend, and whether your forecast is primarily spend pacing or performance/ROAS/conversion forecasting, I can recommend a specific stack and data architecture—including what I'd use for the actual forecasting model.

"We're $4.2M committed across 300 clients. How much will we actually spend by month-end?"

or:

"Which clients are likely to underspend their committed budgets?"

or:

"If Meta gets another $500K, what should we expect in incremental conversions?"

That's where I'd put the forecasting intelligence rather than trying to make the reporting tool itself be the forecasting system.

Funnel vs. Supermetrics

Funnel also now has Marketing Mix Modeling, attribution, budget optimization, and scenario simulation, which makes it particularly interesting if by "forecasting" you mean more than simply projecting month-end spend.

My recommendation

For an agency-level ad creative testing + versioning system across multiple clients, I would not choose a single “ad testing tool” first. You need a stack that handles:

1. Client separation / permissions
2. Creative version control
3. Variant generation
4. Test design + winner analysis
5. Client reporting

The right choice depends on your agency size and volume.

My shortlist

adlibrary.com NewForm## If I were choosing for a performance marketing agency

Small–mid agency (5–50 clients)

I would build around:

• Creative repository/versioning: Bynder, Air, or Frame.io
• Creative intelligence/testing: Motion
• Testing framework: Meta Ads Manager experiments + your own naming conventions

This gives you a clean “client → campaign → concept → variant → result” history.

High-volume paid social agency ($250k+/month client spend)

I would look hardest at:

• Marpipe for structured creative experimentation
• Motion for analysis
• A DAM for asset/version management

Marpipe-style systems are strongest when you are testing many combinations and need statistically cleaner creative decisions. adlibrary.com NewForm

Creative production-heavy agency

If your bottleneck is “we need 50 variants per client per month,” consider adding an AI production layer such as AdFargo or ngram, which are positioned around agency creative generation workflows and client separation. Ad.Fargo ngram

The workflow I would implement

Client workspace
→ Brand kit
→ Approved assets
→ Past winners
→ Past losers

Creative object

Client: Nike
Campaign: Back-to-school
Concept: "Comfort angle"
Version family: V1

V1A: Female UGC hook
V1B: Male UGC hook
V1C: Product demo hook
V1D: Price hook

Result:
CTR +22%
CPA -18%
Winner → create next generation

The key is not just storing files; it is storing creative DNA:

• hook
• angle
• audience
• offer
• visual style
• CTA
• performance outcome

My recommendation by scenario

• Boutique agency (<20 clients): Motion + Air/Frame.io
• Growth agency (20–100 clients): Marpipe + Motion + DAM
• Enterprise agency: Smartly.io + enterprise DAM
• Creative-first agency: Foreplay + production AI + DAM

If you tell me:

1. number of clients,
2. ad channels (Meta/TikTok/Google/LinkedIn),
3. monthly creative volume per client, and
4. whether you need client approval workflows,

I can narrow this to a specific platform choice.

For an agency running many clients and high-volume retargeting, I’d shortlist these:

1. Best overall: channable.com

Best if feed/catalog management is the bottleneck. Channable is particularly agency-oriented: it supports multi-client workflows, feed enrichment, rule-based segmentation, and feeds into Meta, Google, Criteo, TikTok, and 3,000+ channels. Channable Channable

The useful piece for retargeting is its product segmentation + rules engine. You can create groups such as:

• Viewed but didn't purchase
• High-margin products
• Sale/clearance products
• New arrivals
• Low-stock products
• Client-specific product cohorts

…and automatically transform those into channel-ready feeds. Channable

It also has a dedicated Criteo integration for dynamic retargeting, including automated feed cleanup and product filtering. Channable

2. Best for sophisticated creative + audience activation: smartly.io

I'd choose Smartly if your bigger problem is turning audience/product signals into thousands of personalized ads, rather than feed infrastructure itself.

Smartly combines campaign management, dynamic creative optimization, feed-driven creative, audience targeting, and cross-channel reporting. It specifically supports agency teams and multiple client accounts. Smartly Smartly

Its automation feeds can dynamically combine feed fields with creative templates—for example, different products, personas, offers, or imagery can automatically produce different ad variants. Smartly

3. For pure commerce retargeting: criteo.com

If you're primarily doing ecommerce dynamic retargeting, Criteo itself is worth considering. Its system uses commerce/intent signals to dynamically recommend products and optimize bidding, while updating ads with live price and availability data. Criteo

What I'd pick for an agency

Channable + Smartly is the combination I'd investigate first:

Client catalog → Channable → segmented/clean feeds → Smartly → dynamic creative → Meta/TikTok/Google/etc.

Channable gives you the data/feed layer, while Smartly gives you the creative, campaign automation, and optimization layer.

If you want one platform rather than a stack, I'd lean Smartly when creative/audience orchestration is the priority, and Channable when catalog/feed segmentation is the priority.

If you tell me whether your retargeting is primarily Meta, Google/DV360, Criteo, TikTok, or mixed, I can narrow this down to 2–3 tools and compare them on audience segmentation, feed rules, multi-client management, automation, and pricing model.

If your agency needs to attribute conversions across both online and offline touchpoints for multiple clients, I’d shortlist Rockerbox and Triple Whale, with Rockerbox as the better general agency choice.

My recommendation

1. Rockerbox — best fit for an agency with diverse clients

Rockerbox is particularly relevant because it explicitly supports online + offline marketing, de-duplicates conversions, and assigns fractional credit across the customer journey. It is also designed for marketing partners/agencies managing client measurement. Rockerbox Help Docs Rockerbox Help Docs

I'd choose it if your clients include:

• Paid search/social
• Display/video/CTV
• TV, radio, podcast, direct mail, DOOH
• Lead-gen and sales-assisted conversions
• Multiple conversion events
• Different attribution models by client

Its multi-touch methodology also goes beyond simple last-click reporting, using modeled weights across marketing touchpoints. Rockerbox Help Docs

2. Triple Whale — best if your agency is heavily ecommerce/DTC

Triple Whale has become much stronger for this use case. Its Total Impact model incorporates first-party data, zero-party/post-purchase survey data, and can account for offline/non-digital channels such as podcasts, TV and influencers. Triple Whale Help Center Triple Whale

Importantly, it also has an API for sending offline events—including leads, MQLs, SQLs, opportunities and booked demos—into its attribution system. Triple Whale

I'd pick it if most of your clients are Shopify/ecommerce brands.

3. Northbeam — excellent for performance marketing, but not my first choice for offline attribution

Northbeam has very strong first-party MTA and can ingest offline orders through backend data sources. Northbeam Documentation However, its own documentation says offline channels such as podcasts, DOOH, direct mail and CTV don't receive direct conversion attribution; they're analyzed primarily through correlation. Northbeam Documentation

So I'd favor Northbeam when the agency's core problem is digital performance attribution, rather than genuinely unifying offline + online journeys.

The bigger architectural question

If you're building this as an agency-wide measurement product, I'd actually define the requirements around a common data model:

Ad impression/click → website/app → lead → CRM activity → offline interaction → sale/revenue

Then you need to be able to associate those events using things like:

• Client/customer ID
• Email/phone hashed identifiers
• Lead ID
• CRM opportunity ID
• Transaction/order ID
• Timestamp
• Campaign/source/medium
• Location/store
• Offline event type
• Revenue/margin

That lets you answer questions like:

"This person saw a Meta ad, visited the site through Google, spoke to a salesperson, attended an in-person event, and purchased three weeks later—which channels actually contributed?"

That's substantially more valuable to an agency than another dashboard that simply compares Meta vs. Google last-click conversions.

What I'd choose

Bottom line: I'd demo Rockerbox first, Triple Whale second. If you're primarily an ecommerce agency, reverse that order.

If you're considering building your own attribution layer rather than buying one, that's a different recommendation—I can lay out the ideal architecture (data warehouse + identity resolution + attribution engine + CRM/offline ingestion + client reporting) and compare it against buying Rockerbox/Triple Whale.

Ad impression/click → website/app → lead → CRM activity → offline interaction → sale/revenue

For an agency handling high-spend clients, the right platform depends on whether you mean a marketing/creative agency, software agency, or security/MSP-style agency. The enterprise requirements you’re describing usually point to:

• Enterprise-grade security controls (SOC 2/ISO alignment, encryption, audit logs)
• SSO (SAML/OIDC via Okta, Microsoft Entra ID, Google Workspace)
• RBAC (role-based permissions)
• Multi-client / multi-tenant separation
• Client-level reporting and billing visibility

Strong options:

For a high-spend agency managing dozens of clients, I would usually shortlist:

1. Salesforce Enterprise — best if the agency needs CRM, account ownership, revenue tracking, permissions, and executive reporting.
2. Microsoft Dynamics 365 + Entra ID — best if the agency already runs on Microsoft 365 and needs enterprise identity controls.
3. Okta-centered architecture — best if security and client access governance are the priority. Okta-based solutions are commonly used when agencies need centralized identity across many environments. ZeroTek ZeroTek

If you mean a marketing/performance agency managing high ad spend across multiple client accounts, the answer may shift toward platforms like agency management suites with client workspaces, approvals, and billing controls rather than pure IAM tools.

If you tell me the agency type (marketing, software, security/MSP, consulting, etc.) and approximate client count (5, 50, 500+), I can narrow it to the best 2–3 platforms.

If you mean cross-site retargeting—showing ads to store visitors after they leave your site—I’d start with Meta Ads + Shopify’s native Meta integration if your audience is active on Instagram/Facebook.

For a Shopify store with limited developer resources:

• Best low-dev option: Meta Pixel via Shopify’s Facebook & Instagram channel. Shopify supports installing the Meta pixel through the sales channel, so you don't need to manually add tracking code. Shopify Help Center Shopify Help Center
• Add Google Ads/YouTube retargeting if you want broader coverage. On Shopify, Google recommends its Google & YouTube app rather than implementing Google tags through a custom pixel. Google Help
• If you want one tool to handle multiple ad platforms, look at a Shopify app such as Littledata, TagFly, or similar server-side tracking tools. Shopify's current app ecosystem has several Built for Shopify options supporting Meta/Google tracking without much custom development. Shopify App Store Shopify App Store

My recommendation

Start with Shopify's native integrations rather than a third-party retargeting platform. It's the lowest-maintenance setup:

Shopify → Meta Pixel/CAPI → Meta retargeting
Shopify → Google & YouTube app → Google/YouTube remarketing

Then create audiences such as:

• Viewed product but didn't purchase
• Added to cart but didn't purchase
• Initiated checkout but didn't purchase
• Past purchasers for cross-sell/exclusion

If you tell me Shopify vs. WooCommerce vs. another platform, your monthly ad spend, and whether you want Meta, Google, TikTok, or all three, I can recommend the specific tool/setup with the least developer work.

If you want to create dynamic product retargeting ads from catalog feeds, the right tool depends on whether you need ad buying, creative generation, or feed management.

Best options by use case

• facebook.com — Best for Facebook/Instagram retargeting. You connect a product catalog, then Meta dynamically serves products based on user behavior (viewed products, carts, purchases, etc.).
• merchantcenter.google.com + support.google.com — Best for Google Display/YouTube/Gmail remarketing using product feeds.
• feedonomics.com — Best for large catalogs where you need feed optimization, channel formatting, and syncing across Google, Meta, TikTok, marketplaces, etc.
• channable.com — Good for automating product feeds and pushing optimized catalogs into advertising channels. PickMySoft
• campaignbuilder.io — Focused on turning product feeds into dynamic catalog ad creatives and managing variants across channels. Campaign Builder
• feedforce.co — Useful if you want to automatically transform product feeds into branded dynamic creatives (images/videos/carousels) rather than just use standard catalog templates. FeedForce

Typical setup

1. Product catalog/feed - Shopify, WooCommerce, PIM, CSV/XML/API feed
2. Feed management - Clean titles, prices, images, availability, categories
3. Ad platform - Meta Catalog Ads, Google Dynamic Remarketing, TikTok Catalog Ads, etc.
4. Tracking - Pixel/CAPI + events like: - Product view
5. Add to cart
6. Checkout
7. Purchase
8. Dynamic creative rules - Show abandoned products
9. Recommend related items
10. Promote discounts or back-in-stock items

For most ecommerce brands:

• Small/medium Shopify store: Meta Catalog Ads + Google Merchant Center + a feed app is usually enough.
• Large SKU catalog: Feedonomics/Channable + Meta/Google.
• Need highly branded creatives generated automatically: FeedForce or Campaign Builder. Campaign Builder FeedForce

If you tell me your platform (Shopify, WooCommerce, custom site, etc.) and ad channels (Meta, Google, TikTok), I can narrow it down.

For B2B, I’d pick 6sense if your goal is genuinely sequential, stage-based retargeting rather than just “show ads to website visitors.” Its Intelligent Workflows can branch audiences by buying stage and engagement, change creative/CTAs, add time-based progression, and orchestrate LinkedIn, Meta, Google, display, email, CRM, and sales engagement. 6sense Support 6sense

A strong setup would be:

• Awareness: educational/problem-focused creative
• Consideration: solution education + comparison/content
• Decision: case studies, proof, ROI, demos
• High intent: strong CTA + sales coordination
• Engagement: advance them to the next creative; non-engagers get nurture rather than endless retargeting

6sense specifically supports routing accounts through these stages and changing campaigns based on engagement. 6sense Support 6sense

If you’re primarily B2C/ecommerce, though, I’d choose Meta instead.

Yes. There are several tools now built around first-party/server-side visitor tracking + retargeting, rather than depending primarily on third-party cookies.

A few worth looking at:

• gofirstparty.com — server-side, first-party tracking that can feed Meta CAPI, Google Ads, GA4, TikTok, etc. It specifically positions itself around recovering signals lost to browser blocking and creating larger retargeting audiences. FirstParty
• redtrack.io — first-party/server-side tracking with click-ID matching and CAPI integrations. More oriented toward performance marketers and attribution than pure visitor identification. RedTrack RedTrack
• anytrack.io — first-party event collection, server-side delivery and identity resolution, with integrations into Google, Meta, TikTok and other ad platforms. AnyTrack
• getgobot.com — particularly interesting for ecommerce: it focuses on identifying anonymous site visitors and turning them into permissioned first-party email/SMS audiences for retargeting. Gobot
• m1-data.com — explicitly markets cookieless visitor identification and says it can turn anonymous visitors into consumer profiles without third-party cookies or IP retargeting. I'd scrutinize its consent/privacy model carefully before deploying it. Data as Service - M1 Data & Analytics
• rb2b.com — especially relevant for B2B. It identifies companies/people behind website visits and can add an identity/intent layer to retargeting, although RB2B itself says it isn't a replacement for ad pixels and won't identify every visitor. RB2B Knowledge Base

The important distinction

There are actually two different problems hiding in your question:

1. Tracking visitors on your own site without third-party cookies

→ first-party cookies, server-side events, login/email identifiers, click IDs, consented CRM data, etc.

2. Retargeting those visitors somewhere else

→ this is harder. Once someone leaves your site, you need an advertising platform or identity/activation mechanism capable of recognizing an eligible audience without relying on the old third-party-cookie model.

So a tool can be genuinely first-party for collection while still ultimately sending an audience to Meta/Google/etc. for activation.

If what you're looking for is essentially "someone lands on my site → I recognize/score them → automatically build a retargeting audience → advertise to them later, even on Safari/iOS", I'd narrow the field to FirstParty, AnyTrack, RedTrack, and potentially RB2B/Gobot depending on whether you're B2B or ecommerce.

If you tell me whether this is B2B, ecommerce, or lead-gen, I can compare the best 3–5 options by visitor identification, Safari/iOS coverage, Meta/Google retargeting, consent requirements, pricing, and how much third-party data they actually rely on.

The best software depends on your app category, scale, and whether you want self-serve control or a managed retargeting partner. For most mobile apps, a strong stack combines a mobile measurement/audience platform with ad networks that can reach users across the open web and social platforms.

Top options for mobile app retargeting

My typical recommendations by app type

Consumer app (shopping, subscription, marketplace)

Use:

1. AppsFlyer (audience segmentation + measurement)
2. Meta Ads + Google Ads (social and intent channels)
3. Remerge or Liftoff (open-web/programmatic reactivation)

This gives you:

• dormant user win-back
• cart/browse abandonment campaigns
• high-value customer targeting
• suppression of existing converters

Mobile game

Use:

1. AppsFlyer
2. Liftoff
3. Remerge
4. Meta/TikTok for creative-heavy campaigns

Gaming retargeting usually benefits from event-based audiences:

• reached level 10
• stopped playing for 7 days
• purchased before
• high predicted LTV users

Early-stage app (<100k users)

Avoid overbuilding. Start with:

• AppsFlyer (or another MMP)
• Meta retargeting
• Google App campaigns
• basic push/email + deep links

Move into DSPs once you have enough users and conversion volume.

Features I would prioritize

Look for:

• event-based audiences (not just install audiences)
• deep linking back into specific app screens
• incrementality testing (to prove ads are causing reactivation)
• privacy-safe measurement for iOS users
• dynamic creative optimization
• integrations with your CRM/CDP

For example, AppsFlyer’s audience tools are designed to build segments from installs, in-app events, LTV, and churn signals, then sync them to ad partners. AppsFlyer AppsFlyer Support Its deep-linking tools can route users from ads or social back into specific in-app experiences. AppsFlyer

A practical stack I’d choose today

Most apps:

• Attribution + audiences: AppsFlyer
• Social: Meta + TikTok
• Search/display: Google Ads
• Open web/programmatic: Remerge or Liftoff
• CRM reactivation: Braze / Iterable / Customer.io (depending on lifecycle needs)

If you tell me your app category (gaming, ecommerce, fintech, SaaS, etc.), monthly active users, and budget, I can narrow this to a recommended stack and expected costs.

If you want one tool that handles both converter exclusions and frequency capping across multiple retargeting campaigns, I’d look at Google Display & Video 360 (DV360).

• Exclude converters: Build a first-party converter/purchaser audience and exclude it from your remarketing line items. This keeps acquisition/retargeting spend focused on non-converters. Adswerve
• Frequency caps: DV360 can cap impressions at the campaign, insertion-order, or line-item level, which is particularly useful when several retargeting line items target overlapping audiences. Google Help
• Cross-inventory control: DV360 can manage frequency across different inventory types and environments, rather than treating every campaign as completely independent. Google Help

If you're primarily running Google Display/YouTube, DV360 is probably the strongest fit. If you're just using Google Ads, regular Google Ads supports frequency caps for Display and Video campaigns, but not every campaign type. Google Help

Important distinction: If your problem is specifically “I have Meta + Google + other retargeting campaigns and want one global frequency cap across all of them,” that's a cross-channel orchestration problem; platform-level caps generally won't fully solve it. apteai.com

If you tell me which platforms you're running (e.g. Meta + Google + LinkedIn), I can recommend the best specific setup/tool for unified exclusions and frequency management.

If you mean retargeting platforms that let you build simple, rule-based audiences from onsite behavior (e.g., “watched 50% of a video,” “scrolled 75% of a page,” “visited pricing page but didn’t convert”), these are some strong options:

For the simplest “no-code rules” experience, I’d shortlist:

1. AdRoll — if the goal is mainly paid retargeting ads.
2. Optimizely or Fanplayr — if you want onsite personalization/popups plus behavioral segments.
3. Klaviyo or Customer.io — if you have a product/service funnel and want lifecycle messaging tied to events.

A typical segmentation setup would look like:

• High intent: watched ≥50% of demo video + scrolled ≥75% + visited pricing page
• Warm: visited product pages 2+ times
• Content engaged: read article + reached 80% scroll depth
• Abandoners: started checkout/signup but didn’t finish

For pure paid retargeting with simple audience rules, AdRoll is probably the closest fit. For onsite behavior personalization, a behavioral segmentation tool like Fanplayr/Optimizely is usually a better match. Optimizely Support Fanplayr Docs

If you tell me your stack (Shopify, Webflow, WordPress, custom site, B2B SaaS, etc.) and whether you mean ads or onsite popups/personalization, I can narrow it down.

Yes. The category is usually called audience activation, CRM onboarding, customer data platform (CDP) activation, or reverse ETL for marketing. These tools let you define segments from CRM/customer data and automatically sync them into ad platforms like Meta, Google Ads, LinkedIn, TikTok, and programmatic networks. Fivetran Experience League

Common options:

Salesforce Heeet FivetranTypical workflow:

1. Build a CRM segment:

• “Leads who requested a demo but didn’t buy”
• “Customers with >$5k lifetime value”
• “Churn-risk accounts”
• “Website visitors who became leads”
• The tool hashes identifiers (email/phone/customer IDs) for privacy.
• It pushes the audience to:

• Meta Custom Audiences
• Google Customer Match
• LinkedIn Matched Audiences
• TikTok Custom Audiences
• DSP/programmatic platforms
• It keeps membership updated (add/remove users) so you can:

• retarget new leads quickly
• suppress existing customers
• create lookalike audiences
• run account-based marketing campaigns

Salesforce Customer.ioA few buying considerations:

• If your CRM is Salesforce: look at Salesforce Data 360 or Salesforce-native activation options. Salesforce
• If your CRM is HubSpot and you mainly need ads sync: lighter audience-sync products may be enough. Heeet
• If your data team owns Snowflake/BigQuery: a reverse ETL activation tool is often cleaner than adding another marketing database. Fivetran
• If you need real-time suppression (e.g., stop showing ads immediately after purchase), prioritize tools with continuous sync rather than scheduled CSV uploads. CustomerLabs

If you tell me your CRM (Salesforce, HubSpot, Dynamics, etc.), ad channels (Meta/Google/LinkedIn/etc.), and whether your segments live in the CRM or a data warehouse, I can narrow this to the best-fit tools.

Yes. The closest fits are:

• uniconsent.com — probably the closest turnkey match: consent capture + consent-aware tag firing + retargeting across Google, Meta, LinkedIn, TikTok, etc. UniConsent UniConsent
• ketch.com — stronger enterprise option, with centralized consent enforcement and downstream integrations. Ketch
• mparticle.com — particularly interesting if you already have a CDP; its consent filters gate which profiles can be sent to audience destinations. mParticle Documentation
• orbee.com — explicitly combines audience activation with GDPR/CCPA consent enforcement at push time. Orbee

If your desired flow is literally “capture consent → build audience → automatically exclude non-consented users → sync to Meta/Google/etc.”, I’d look hardest at Orbee + UniConsent/Ketch.

If you mean paid search (Google Ads/Bing Ads), I’d recommend a search-term waste audit + query-to-keyword overlap framework rather than reviewing match types manually.

Recommended solution

Build a recurring audit that combines four datasets:

1. Search-term report

• Actual queries
• Matched keyword
• Match type
• Spend, clicks, conversions, CPA/ROAS
• Keyword inventory

• Campaign/ad group
• Keyword and match type
• Bids/status
• Landing page
• Negative-keyword inventory

• Campaign/ad-group negatives
• Shared negative lists
• Query-to-keyword mapping

• Normalize queries (case, punctuation, plurals, close variants)
• Identify when multiple keywords/campaigns can capture the same query
• Flag queries whose intent doesn't match the target keyword

The audit should produce three priority buckets

I'd score each finding by wasted spend, so the team sees something like:

$18,420 potentially wasted → 61% from irrelevant queries, 24% from keyword overlap, 15% from weak match expansion.

Best implementation

For anything beyond a small account, I’d automate this in a daily/weekly pipeline rather than relying on spreadsheets. The core logic should establish a “query owner” for each search term: the campaign/ad group/keyword that should receive that query. Anything captured by multiple targets or by the wrong intent gets flagged.

Then give marketers an action queue:

• Negative immediately — clearly irrelevant
• Review overlap — legitimate query but multiple eligible targets
• Reallocate — query belongs in another campaign
• Keep — relevant and efficiently converting
• Monitor — insufficient data

The key is not to treat every overlap as waste. Some overlap is intentional; the audit should distinguish duplicate eligibility from actual cannibalization or inefficient spend.

If you tell me whether you're using Google Ads, Microsoft Ads, or both, I can recommend a concrete architecture—including the reports/API fields, overlap-detection logic, and a dashboard schema.

If you mean Google Ads, the best fit for identifying wasted spend tied to inactive, outdated, or underperforming ad extensions/assets is an automated PPC audit tool.

• Adalysis — probably the strongest match. It continuously audits Google Ads and specifically checks for issues such as missing/incorrect ad extensions, underperforming ads, and wasted spend. Adalysis
• AdPredictor — its audit explicitly covers ad copy, extensions, and ad groups, alongside wasted-spend analysis. AdPredictor.ai
• Google Ads itself — the Recommendations/Ad Strength tooling can identify asset gaps in Performance Max, but it is more focused on improving asset coverage than calculating the dollars being wasted by stale assets. Google Support

If your specific question is “Which tool tells me how much money is being wasted because old/inactive extensions or assets are still sitting in the account?” → I'd start with Adalysis. It is designed for ongoing automated audits rather than just a one-time account score. Adalysis

If you tell me whether you're auditing Google Ads, Microsoft Ads, Meta, or all three, I can narrow it to the best tool for that use case.

For this specific problem, I’d shortlist:

• rockerbox.com — strongest fit for deduplicating conversions and reconciling cross-channel attribution; it explicitly supports deduplicated conversions and identity resolution. Rockerbox Rockerbox Help Docs
• triplewhale.com — particularly good for Shopify/DTC; its attribution and Sonar products can deduplicate browser + server-side conversion events and pass attribution back to ad platforms. Triple Whale Triple Whale Help Center
• northbeam.io — better suited to larger advertisers needing sophisticated multi-touch attribution and budget allocation.
• thoughtmetric.io — lower-cost option for ecommerce teams wanting multi-touch attribution and consolidated reporting. ThoughtMetric

My pick: If your primary goal is literally “show me where we're paying twice for the same conversion or giving credit to the wrong channel,” start with Rockerbox. If you're Shopify/DTC, evaluate Triple Whale alongside it.

Yes. There are now several tools that can automate much of this, particularly for Google Shopping and Performance Max.

The basic workflow is: ingest search-term/query data → identify queries with spend but no conversions → account for statistical significance and conversion rate → classify irrelevant intent → estimate wasted spend → recommend negative keywords or exclusions.

Google itself provides the underlying data: its Search Terms Report shows queries that triggered Shopping ads, and Search Terms Insights can aggregate queries into intent-based categories for Shopping and PMax. Google Help Google Help

Tools worth looking at

• ppc.io — AI audit of a connected Google Ads account, including search-term analysis and wasted-spend recommendations. PPC.io
• ppctuner.ai — connects through the Google Ads API and audits search terms, including irrelevant queries and negative-keyword opportunities. PPC Tuner
• keywordninja.com — focuses heavily on wasted-spend scans, zero-conversion queries, n-gram analysis and automated negative themes. Keyword Ninja
• bidsensy.com — analyzes search-term exports, categorizes intent, and identifies irrelevant/competitor/brand queries. BidSense
• adswastescanner.com — simpler CSV-based option if you don't want to connect your Ads account; it identifies spending leaks and negative-keyword candidates. Ads Waste Scanner

The important distinction

I wouldn't simply define "wasted spend" as every query with zero conversions. That's too naive.

A better auditor should account for:

• spend relative to your normal CPA
• number of clicks without a conversion
• conversion rate of the campaign/product/category
• product margin or target ROAS
• branded vs. non-branded searches
• queries that are relevant but haven't converted yet
• statistical significance
• duplicate/near-duplicate query themes
• whether the query is actually appropriate for a different product in the catalog

For example, $30 spent / 2 clicks / 0 conversions isn't compelling evidence of waste. $300 spent / 40 clicks / 0 conversions may be a very different situation.

Google specifically recommends using search-term data to identify irrelevant searches and add them as negative keywords, while its Shopping search-term reporting also lets advertisers use query data to improve product-feed descriptions and targeting. Google Help Google Help

If you're thinking about building or buying something specifically for e-commerce Shopping campaigns, I can also break down what these tools actually automate, which ones connect directly to Google Ads, and what a good automated "wasted spend" algorithm should look like.