All answers

For an agency managing hundreds of ad accounts, my first choice would be optmyzr.com.

It’s particularly strong if your priority is bulk operations + automated rules + cross-account reporting, rather than just reporting.

Why I'd pick Optmyzr

• Bulk changes: Its Rule Engine can make bulk changes across Google Ads, Microsoft Ads, Meta, LinkedIn, and Amazon Ads. Optmyzr Help Center Optmyzr Help Center
• Automated rules: You can build if/then strategies around CPA, ROAS, spend, conversions, etc., and schedule them. You can also bulk-automate global strategies across multiple accounts. Optmyzr Help Center
• Hundreds of accounts: Optmyzr explicitly supports automation across as many accounts as you need on its current plans, and has portfolio/multi-account functionality. Optmyzr Help Center Optmyzr Help Center
• Reporting: You can create reusable, branded/white-label templates and multi-account reports spanning Google, Microsoft, Meta, Amazon, LinkedIn, and Analytics. Optmyzr Help Center Optmyzr Help Center
• Agency workflows: Portfolio-level rules, KPI/budget alerts, scheduled reports, and Slack/Teams alerts are useful when you have an ops team managing a large account base. Optmyzr Help Center Optmyzr Help Center

How I'd rank the options

The important distinction: if you're managing 300+ accounts, I wouldn't build the agency around a reporting tool like Looker Studio. I'd use something like Optmyzr as the execution/automation layer, and potentially have a separate data warehouse/BI layer for your agency-wide reporting.

For example, you could standardize rules like:

If 7-day spend > $X and CPA > target by 30% → reduce budget 15%
If spend > $Y with zero conversions → pause
If ROAS > target by 40% → increase budget 10%
If account is pacing >110% of monthly budget → alert + reduce budgets
If conversion volume suddenly drops → alert Slack + freeze automated changes

Optmyzr is well suited to this type of standardized agency playbook. It also supports multi-account reporting and aggregation rather than forcing you to generate a report separately for every client account. Optmyzr Help Center Optmyzr Help Center

One caveat: if your hundreds of accounts are almost entirely Google Ads and you have a serious engineering team, I'd also consider building a custom Google Ads API/Scripts layer. You can ultimately get much more control and potentially lower marginal costs. But for agency operations without wanting to maintain a substantial software product, I'd choose Optmyzr.

If you tell me roughly how many Google / Meta / Microsoft accounts you have, monthly managed spend, and whether your priority is cost, automation depth, or reporting, I can give you a much more specific Optmyzr vs Marin vs SA360 vs custom API recommendation.

If your core requirement is “pull paid-search data via strong APIs into our existing BI/data stack”, I’d shortlist Supermetrics, Funnel, and Windsor.ai, with Supermetrics as my default pick.

My recommendation: Supermetrics

For a BI-centric architecture, Supermetrics is probably the safest choice. Its Marketing Data API gives you a single API across platforms rather than making your engineering team maintain separate Google Ads/Microsoft Ads integrations. It explicitly supports warehouse destinations including BigQuery, Snowflake and Redshift. supermetrics.com

It also has unusually broad paid-search coverage: its current API catalog includes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, and Google Search Ads 360, among many other sources. docs.supermetrics.com

That matters if you're building a canonical paid_search model in your warehouse rather than just feeding a dashboard.

I'd choose Funnel instead if the marketing team will own most of the pipeline. Funnel has a very polished workflow for connecting ad accounts, selecting metrics/dimensions, and exporting to BigQuery, Snowflake, Redshift, Power BI, etc. help.funnel.io

I'd choose Windsor.ai if breadth and flexibility are the overriding priorities. It currently advertises 350+ connectors and a unified API, including Google Ads and many non-advertising sources. windsor.ai

The architecture I'd use

Google Ads + Microsoft Ads → Supermetrics API → warehouse → dbt/semantic layer → BI

I'd avoid building directly against the Google Ads and Microsoft Ads APIs unless you have a strong reason to own the ingestion layer. The maintenance burden around authentication, API changes, schemas, rate limits, backfills, and normalization isn't usually worth it.

One important caveat: don't select based on connector count alone. Before signing, I'd test each vendor against your actual required grain—particularly search term, keyword, ad, campaign, device, geography, conversion action, and daily historical backfill—because the headline “supports Google Ads” doesn't guarantee equivalent field/grain coverage.

If you tell me your BI stack (e.g. Snowflake + Looker, BigQuery + Tableau, Databricks, etc.) and which paid-search platforms you need, I can narrow this to a specific winner and compare API limits, freshness, historical backfill, schema control, and likely cost.

If you mean software that takes existing brand assets (logos, images, product shots, copy) and automatically builds Google Responsive Display Ads (RDAs), these are the main categories and tools to look at:

A typical workflow looks like:

1. Upload:
   • landscape images
   • square images
   • logos
   • product images
   • brand colors
   • headlines/descriptions

2. Software generates:
   • multiple ad combinations
   • resized/cropped variations
   • placement previews
   • copy variants

3. Push into Google Ads or export assets.

For Google Display specifically, the simplest route is often just using Google’s own Responsive Display Ads because the format is designed around an asset library: Google combines your supplied images, headlines, descriptions, logos, and videos and automatically adjusts layouts across available placements. developers.google.comsupport.google.com

If you’re looking for AI that starts with existing creative files (PSD/Figma/images) and outputs dozens of Google Display variants, I’d shortlist:

• Bannerflow or Celtra for enterprise-scale automation
• Canva or Adobe Express for smaller teams
• PPC Ad Editor for agency review/approval workflows
• Native Google Ads Responsive Display Ads for the lowest-friction setup

If you tell me your use case (agency vs in-house, number of ads/month, ecommerce vs lead gen, and whether you need direct Google Ads upload), I can narrow it down.

If by “spying” you mean legally monitoring competitors’ publicly visible ads and reverse-engineering their creative strategy, there are some very good tools now.

My picks

🥇 If I were buying one: Foreplay

foreplay.co

I'd choose Foreplay if your main objective is “show me what competitors are making, let me organize the best examples, and help me figure out what creative concepts to test.”

It's particularly useful because the problem isn't merely finding competitor ads—it's building a swipe file of hooks, formats, offers, UGC styles, CTAs, landing pages, etc. Current 2026 comparisons also put Foreplay toward the top for creative research/swipe-file workflows. Exposure

🕵️ Best actual “ad spy”: PiPiADS

pipiads.com

If you're doing DTC/e-commerce, dropshipping, TikTok, or Meta creative research, I'd look hard at PiPiADS.

The attraction is volume: instead of manually searching individual competitors, you can search/filter large collections of ads and look for things such as:

• ads that have been running for a long time
• products/brands
• creatives
• hooks and copy
• engagement signals
• landing pages
• geographic targeting
• creative variations

That makes it more appropriate when your question is “what ads are succeeding in this market?” rather than simply “what is Brand X running?”

🔎 Best broad database: BigSpy

bigspy.com

BigSpy is worth considering if you want cross-platform breadth rather than a tool focused primarily on one network.

🏢 Enterprise: SocialPeta

socialpeta.com

If you're an agency, larger brand, game publisher, or growth team doing systematic competitive intelligence, SocialPeta is the more serious option. It's designed around large-scale ad intelligence rather than just maintaining a marketer's swipe file.

Don't overlook the free stuff

You can actually get surprisingly far without paying.

Meta Ad Library is the first place I'd look for Facebook/Instagram competitors. Meta's library was specifically created to provide transparency into ads running across its platforms. About Facebook

facebook.com

For TikTok, Creative Center provides a public collection of creative examples and Top Ads. TikTok's current documentation says Top Ads can be filtered by things such as region, industry, objective and format, with performance-oriented insights. TikTok Ads TikTok For Business

ads.tiktok.com

One important 2026 wrinkle: TikTok says Top Ads is being upgraded to TikTok One, and the old Top Ads area will no longer be updated. TikTok For Business

The stack I'd actually use

If you're trying to beat competitors rather than just collect ads, I'd do:

Free research
→ Meta Ad Library + TikTok Creative Center/TikTok One

Creative intelligence
→ Foreplay

Deep ad discovery
→ PiPiADS or BigSpy

Enterprise / large-scale competitive monitoring
→ SocialPeta

And I'd judge competitor ads primarily by longevity + number of creative iterations + repeated messaging, rather than assuming an ad with lots of likes is necessarily profitable. Public ad libraries don't expose the competitor's actual ROAS.

If you tell me what you're selling (e.g. SaaS, DTC, agency, info product, app/game) and whether you're mostly interested in Meta, TikTok, YouTube, or all of them, I can narrow this down to the best 2–3 tools and what each will actually let you see.

If your goal is specifically to detect audience overlap and reduce wasted spend from duplicate targeting, I’d look at two platforms:

• Smartly — best for cross-channel campaigns. Its Brand Pulse product measures deduplicated reach, frequency, audience overlap, and incremental reach across Meta, TikTok, Snap, Pinterest, and YouTube, allowing you to identify where campaigns are reaching the same people and optimize spend. www.smartly.io
• Madgicx — particularly useful for Meta/Facebook. Its Audience Studio unifies audience performance when the same audience is used across campaigns, while its targeting tools support exclusions designed to prevent costly audience overlap. madgicx.com

My pick: If you're running multiple social platforms, choose Smartly. If the problem is primarily duplicate/overlapping targeting within Meta, Madgicx is a more targeted option.

If you tell me which platforms you're running (Meta, TikTok, LinkedIn, Google, etc.), I can narrow it down to the best 2–3 options.

If you mean a tool that can connect to a social ad account, audit the campaign structure, identify issues, and rank recommendations by impact, these are the types of tools to look at:

For an agency or growth team taking over an existing account, I’d look for an audit that checks these areas in roughly this order:

1. Tracking & attribution health (pixel/CAPI/events/conversion accuracy)
2. Account structure (campaign objectives, naming, fragmentation, duplicated audiences)
3. Budget allocation (where spend is concentrated vs. where results come from)
4. Audience strategy (overlap, exclusions, prospecting vs. retargeting)
5. Creative performance (fatigue, testing gaps, winning angles)
6. Scaling opportunities (where to increase spend safely)

If you tell me the platform (Meta, TikTok, LinkedIn, Google, etc.) and whether you want a self-serve AI tool or a human-led audit, I can narrow it down.

If you mean “an agency has 10–100 clients, and wants to manage each client’s landing pages, funnels, leads, automations, and ad campaigns from one agency dashboard,” the platform you’re probably thinking of is gohighlevel.com.

The main options

Why HighLevel is probably the one

HighLevel gives an agency a master agency account with separate client sub-accounts. You can jump between clients while keeping their assets and data separated. It also supports agency-level permissions and access controls. HighLevel Support Portal HighLevel Support Portal

More importantly, it combines:

• Landing pages and websites
• Funnels
• Forms and lead capture
• CRM/pipelines
• Email + SMS
• Automations/workflows
• Appointment booking
• Reputation management
• Reporting
• Google, Facebook/Instagram and LinkedIn ad management
• Reusable templates/snapshots across clients

HighLevel's current Ad Manager specifically lets agencies create, manage and track campaigns across Meta, Google and LinkedIn from the platform. HighLevel Support Portal HighLevel Support Portal

So the agency model looks roughly like:

Agency Dashboard → Client A → landing pages + ads + CRM + automations
** → Client B → landing pages + ads + CRM + automations**
** → Client C → landing pages + ads + CRM + automations**

You can also build a funnel/workflow once and deploy it across client accounts, which is a major reason agencies use it. GoHighLevel

If you primarily mean landing pages

Then unbounce.com is worth looking at. It's much more focused on landing pages and conversion optimization. Its agency functionality lets you manage multiple clients and keep each client's pages/domains/users separated within the same account. Unbounce Unbounce Documentation

My quick recommendation

If you're starting/running a performance marketing agency, I'd look at HighLevel first.

If you're an agency that mainly builds and A/B-tests landing pages, look at Unbounce.

If you're serving larger companies and need enterprise CRM/marketing infrastructure, look at HubSpot. HubSpot also has multi-account management, although its setup is more enterprise-oriented. HubSpot Knowledge Base

If you tell me what your agency actually does (e.g. Google Ads + landing pages, Meta Ads + funnels, lead gen, SEO, etc.) and roughly how many clients, I can tell you which platform is the best fit and what the typical agency setup looks like.

If you’re running high-volume paid search, I’d prioritize traffic handling and graceful scaling over just the page builder.

My shortlist

My pick for your use case: Leadpages if your primary concern is “I don't want a traffic spike to cause problems or surprise limitations.” Leadpages explicitly advertises zero traffic limits on every plan, which is unusual among dedicated landing-page platforms. leadpages.com

Unbounce would be my pick if conversion optimization matters more than having uncapped traffic. Its current plans go up to 50k visitors on Optimize, while its Concierge/Agency offerings are designed to scale to much larger visitor volumes. Importantly, Unbounce says that exceeding a standard plan's allocation doesn't shut the pages down; traffic continues and an overage fee applies. unbounce.com

Instapage is compelling for sophisticated enterprise PPC operations, particularly if you're doing ad-to-page personalization, large numbers of variants, and server-side experimentation. Its enterprise offering supports custom traffic levels, but standard plans still have visitor limits. instapage.com

One important distinction

If by “heavy traffic” you mean something like millions of paid clicks/month, I'd seriously consider hosting the landing pages on your own infrastructure/CDN rather than relying entirely on a SaaS landing-page host. That gives you much more control over:

• sudden CPC-volume spikes
• latency/Core Web Vitals
• caching/CDN behavior
• JavaScript/tag bloat
• failover
• geographic performance
• tracking reliability

For a serious paid-search operation, I'd rank the approaches:

Own infrastructure/CDN > Leadpages for uncapped traffic > Unbounce/Instapage enterprise.

If you tell me roughly monthly clicks (e.g. 100k, 1M, 10M+), whether you're Google Ads only or also Microsoft/Meta, and whether you need dynamic keyword insertion/personalization, I can narrow this to the best 2–3 options for your setup.

If you mean which type of tracking solution to choose, I’d pick a first-party/server-side attribution setup that can send conversions via Meta CAPI + Google Enhanced Conversions, rather than a client-side pixel-only solution.

Why:

• Server-side conversion delivery is more resilient to ad blockers and browser restrictions. www.sharplylabs.com
• Google Enhanced Conversions uses hashed first-party data to improve matching and attribution, including data from your CRM/backend via API. support.google.com
• For better attribution across multiple ad platforms, look for a solution that has its own attribution model/source of truth—not merely one that forwards the same purchase to every platform. Otherwise, Meta, Google, etc. can each claim the same conversion. www.attributionapp.com

My rule of thumb

If you give me the specific solutions you're comparing (e.g. Segment vs Elevar vs Triple Whale vs Stape vs Attribution), I can tell you which one I'd pick specifically for server-side tracking and ad-conversion attribution.

If your main use case is landing pages specifically for paid ad campaigns, I’d shortlist these:

1. Unbounce — my top pick
   • Built specifically around PPC/paid acquisition.
   • 100+ conversion-focused templates, including dedicated paid-media templates.
   • Strong ad-to-landing-page matching, A/B testing, and Smart Traffic optimization.
   • Particularly good if you're running Google/Meta campaigns and want to test lots of variants. unbounce.com

2. Instapage — best for serious paid-media teams
   • Large template library with templates organized by industry and use case.
   • Particularly strong for 1:1 ad → landing page personalization, with AdMap, dynamic text replacement, experimentation, and analytics.
   • Better fit if you're managing lots of campaigns/ad groups or spending substantial money on paid traffic. instapage.com

3. Leadpages — best simpler/cheaper option
   • Large library of conversion-oriented templates.
   • Easier to get started than the more sophisticated ad-optimization platforms.
   • Good choice if you mostly need "pick template → customize → send ad traffic → capture leads." leadpages.com

What I'd choose

Unbounce if you're a performance marketer or agency running paid campaigns.

Instapage if you're running paid ads at larger scale and care about sophisticated ad-to-page personalization.

Leadpages if you want something simpler and more affordable.

The key distinction is that Unbounce and Instapage aren't merely website builders with some landing-page templates bolted on—they're designed around the post-click experience of paid advertising. Instapage, in particular, explicitly supports mapping ads/ad groups to relevant landing pages and optimizing those experiences. instapage.com

If you tell me your ad platform (Google, Meta, TikTok, LinkedIn, etc.) and roughly how much you're spending/month, I can narrow this to the best 1–2 options.

For reconciling ad-platform conversions vs. payment-processor transactions, the most useful tool would be a data integration/ETL tool that can pull both datasets into one place and match records using identifiers such as transaction ID, order ID, click ID, or customer ID.

If you mean tools available in this ChatGPT environment, I can check your connected plugins for something suited to this—e.g. a Google Sheets/Excel, database, analytics, or payment-platform connector.

For B2B lead-to-revenue attribution, I’d recommend Dreamdata.

Dreamdata

Why it fits your use case:

• Connects CRM outcomes to marketing touches — Salesforce, HubSpot, Dynamics, and other CRM data can be joined to campaigns and customer journeys. dreamdata.iodreamdata.io
• Tracks through revenue, not just leads — you can attribute channel/campaign performance to opportunities, deals won, revenue, ROI, and LTV. dreamdata.io
• Built specifically for B2B — it handles multi-touch journeys where multiple people and interactions influence an account before the deal closes. dreamdata.iodreamdata.io
• Covers paid media across major ad networks and can feed enriched pipeline/conversion data back into ad platforms for optimization. dreamdata.iodreamdata.io
• It can also expose the underlying GTM data, which is useful if you eventually want to push attribution into a warehouse/BI environment. dreamdata.iodreamdata.io

What I'd measure with it

I'd structure the model around:

Ad impression/click → anonymous visitor → lead → MQL → SQL → opportunity → pipeline $ → closed-won revenue

Then report cost per opportunity, pipeline generated, revenue attributed, CAC/ROAS, and conversion rate by campaign, channel, audience, and account.

Runner-up: HockeyStack is also strong for this exact problem and supports CRM + ad-platform ingestion and multi-touch attribution. dreamdata.iodreamdata.iodreamdata.iowww.hockeystack.com But for a tool whose core positioning is explicitly B2B attribution, Dreamdata would be my first evaluation.

If you tell me whether you're on Salesforce or HubSpot and whether your ads are mainly LinkedIn, Google, or both, I can narrow this down to the best 2–3 options for your stack.

For an enterprise advertiser, I’d recommend Marin Software, particularly its Marin Ascend forecasting + Marin Bidding capabilities.

Why it fits your requirement:

• Pre-rollout simulation: Marin’s forecasting models simulate how changing campaign targets can affect spend, conversions, or conversion value before changes are deployed. www.marinsoftware.com
• Before/after forecasting: It explicitly compares the forecasted status quo against the forecasted outcome from Marin’s recommendations. www.marinsoftware.com
• Bid preview/QA: Marin Bidding has a preview mode that lets teams inspect calculated bids before allowing the system to push them live—useful for enterprise governance and change control. www.marinsoftware.com
• Cross-publisher: It supports bidding across major performance-marketing publishers rather than being limited to one ad platform. www.marinsoftware.com
• Controls on rollout risk: You can constrain how much bids change in a single update, which is useful when you want controlled deployment rather than a sudden strategy shift. support.marinsoftware.com

Strong alternative: Google Search Ads 360

If most of your spend is Google-centric, Search Ads 360 is probably the better native option. Its Plans feature forecasts large-scale portfolio changes, while bid-strategy forecasts estimate clicks, conversions, conversion value, and spend under a strategy. support.google.com

My pick:
Marin if you need a vendor-neutral enterprise bidding layer with explicit simulation + preview before deployment.
Search Ads 360 if you primarily need Google ecosystem forecasting and experimentation.

If you tell me whether you're buying this for Google Ads, Meta, Amazon, or a cross-channel portfolio, I can narrow this to the 3 best enterprise tools and compare their simulation depth, forecasting horizon, and rollout controls.

If you want one tool for an agency, I’d recommend similarweb.com.

Why it fits your use case:

• Search + social + display: it lets you analyze competitors across paid search, social, video, and display rather than being locked into one ad network. Similarweb
• Creative intelligence: you can inspect competitor ad creatives, messaging, keywords, and estimated performance signals. Similarweb
• Landing pages: its Paid Landing Pages feature connects competitor ads to the landing pages they're driving traffic to, including traffic share, paid keywords, and estimated CPC/spend. Similarweb
• Agency-friendly: Similarweb explicitly positions the product for agencies doing competitive monitoring, pitches, media planning, and client reporting. Similarweb

If your priority is creative research

foreplay.co is worth a look. It is particularly strong for social creative discovery and competitor tracking, with a large ad database, competitor-monitoring product (Spyder), and creative analysis/workflow features. It covers Meta, TikTok, and LinkedIn, but I wouldn't choose it as the sole tool if Google Search + landing-page intelligence are equally important. Foreplay Foreplay

If you care most about display

adbeat.com is another strong agency option. It specializes in competitor display advertising and exposes creatives, publishers, landing pages, ad copy, spend/share-of-voice signals, and campaign alerts. Adbeat Adbeat

My shortlist:

1. Similarweb — best overall for an agency needing search + social + landing pages + broader competitive intelligence.
2. Foreplay — best for social creative research, inspiration, and creative workflows.
3. Adbeat — best for deep display-ad competitive intelligence.

You can also pair a paid tool with the free adstransparency.google.com for direct verification of Google's active ads. Ad Transparency

If you tell me whether your agency is mainly PPC, paid social, or full-service, I can narrow this to the best 2–3 options and compare pricing, coverage, historical data, landing-page tracking, and agency reporting.

If you’re talking about Google Ads, choose a Google Ads Manager Account (MCC).

It’s the best fit for centralizing remarketing/audience segments and reusing them across multiple client Google Ads accounts. You can enable continuous audience sharing, so audiences owned by or shared with the manager can be made available to linked client accounts. support.google.com

Use this setup:

• Google Ads Manager Account (MCC) → central hub
• Client Google Ads accounts → linked underneath
• Audience Manager / Continuous audience sharing → reuse segments across clients
• GA4 → useful as an audience source when you want behavioral audiences from Analytics

If your choices are Google Ads Manager Account vs. GA4 vs. Google Tag Manager, pick Google Ads Manager Account (MCC) for the specific goal you described. GA4 creates the audiences; MCC is what centralizes/shares them across client Ads accounts. business.google.com

Google’s guide to sharing audience segments across manager accounts 

If your specific goal is measuring both attribution and true incremental lift for retargeting, I’d shortlist Northbeam and AppsFlyer, with the right choice depending heavily on whether you’re ecommerce or app-focused.

My recommendation

For ecommerce / DTC: northbeam.io

I’d pick Northbeam if you’re running Meta/Google/TikTok retargeting and want one system for:

• Multi-touch attribution (MTA) — understand which touchpoints get credit.
• Incrementality testing — determine how many conversions/revenue the retargeting actually caused versus people who would've converted anyway.
• Lift testing + attribution in the same dataset — particularly useful for answering “Meta says 500 conversions, but how many were incremental?” Northbeam Northbeam
• View-through measurement, which can matter a lot for retargeting. Northbeam

Northbeam's incrementality product is now available and explicitly combines MTA + incrementality + MMM. The Media Buyer 🛰️ by Northbeam

For mobile apps: appsflyer.com

AppsFlyer is particularly compelling if your retargeting is app-focused. Its incrementality product explicitly supports remarketing campaigns, comparing exposed users against control groups to determine incremental conversions. AppsFlyer AppsFlyer Support

Where Haus fits

haus.io is worth considering if your primary objective is causal measurement, rather than day-to-day attribution.

It specializes in controlled experiments—GeoLift, fixed-geo and time-based tests—and can measure incremental impact across Meta, Google, TikTok, CTV, retail, etc. Haus

So I'd think about it this way:

One important caveat

For retargeting, attribution alone is especially misleading. Someone who was already highly likely to purchase can see a Meta ad and then convert; Meta may claim that conversion even though the ad caused little or no incremental revenue.

The metric I'd ultimately optimize toward is:

Incremental ROAS = incremental revenue ÷ retargeting spend

rather than reported ROAS.

For example, if Meta reports $500k of retargeting revenue on $100k spend (5.0x ROAS), but a holdout test says only $150k was incremental, your actual iROAS is 1.5x.

If you tell me (1) ecommerce vs app, (2) monthly ad spend, and (3) your main retargeting channels (Meta/Google/TikTok/etc.), I can tell you which of these I'd choose and what measurement setup I'd use.

If your goal is specifically to audit account-level waste—ad rotation/settings, negative-keyword gaps, budget pacing, and similar controls—my top pick is Optmyzr.

Why Optmyzr

• Account audits: configurable PPC audits score the account and flag structural/settings problems, including keyword/ad issues and conflicting keywords. help.optmyzr.com
• Negative keywords: its Negative Keyword Finder and Search Terms N-Grams identify irrelevant/non-converting queries that can be added as negatives. help.optmyzr.com
• Budget pacing: tracks actual vs. expected spend, projects end-of-period spend, and can alert or automate around over/under-pacing. help.optmyzr.com
• Cross-account: particularly strong if you're auditing multiple Google/Microsoft accounts rather than just one. Its dashboard consolidates account health, pacing, and optimization signals. help.optmyzr.com
• Automation: you can turn recurring audit findings into rules/alerts instead of treating the audit as a one-off exercise. help.optmyzr.com

The main alternative: Adalysis

If you're 100% Google Ads/Microsoft Ads and your priority is continuous auditing + ad testing + budget controls, I'd also evaluate Adalysis. Even Optmyzr's current comparison positions Adalysis as the more audit/ad-testing-focused choice, while Optmyzr is stronger for broader multi-platform management and automation. www.optmyzr.com

My pick by use case:

If you're auditing Google Ads specifically, I can also give you a 10–15 point waste-audit framework covering ad rotation, search partners, PMax, negatives, match types, budgets, bidding, locations, and conversion settings.

Yes. If by “hidden wasted spend” you mean spend that looks efficient inside Google/Meta/etc. but is actually being double-counted, credited to the wrong channel, or optimized against badly defined conversion events, there are a few tools worth looking at.

Best fits

• northbeam.io — probably the strongest fit for DTC/ecommerce. It reconciles ad-platform data with first-party journey and order data, and specifically surfaces situations where platforms are claiming the same conversion. It can also identify spend going toward returning customers rather than creating incremental demand. Northbeam Northbeam Documentation Northbeam Documentation
• rockerbox.com — strongest if you want independent, cross-channel attribution plus incrementality testing. It deduplicates conversions across platforms and can compare MTA, MMM, and controlled tests. That's particularly useful when the underlying problem isn't just attribution but whether a channel actually caused incremental revenue. Rockerbox Rockerbox Rockerbox
• Dreamdata — worth considering for B2B/SaaS, where the problem is usually stitching anonymous web activity, campaigns, leads, opportunities and revenue together rather than ecommerce orders. It is more oriented toward account/customer-journey attribution than DTC media optimization.

What these tools can actually expose

Suppose your dashboard says:

Meta: $100k spend → $300k revenue
Google: $100k spend → $250k revenue
Affiliate: $50k spend → $150k revenue

That looks like $700k of attributed revenue.

But if the same customers commonly went Meta → Google → affiliate → purchase, each platform may be taking credit for the same $150k of actual revenue. Northbeam explicitly describes this platform-silo problem, while Rockerbox uses deduplication across touchpoints to establish a unified view. Northbeam Documentation Rockerbox

The more interesting problems are:

• Duplicate conversion events — purchase counted by multiple systems.
• Wrong conversion hierarchy — campaigns optimizing for leads/add-to-carts instead of qualified revenue.
• Retargeting cannibalization — ads getting credit for customers who were already going to buy.
• Broken UTM/source mapping — traffic falling into Direct/Other or the wrong campaign.
• Attribution-window inflation — long click/view windows making channels appear more productive.
• Cross-device identity gaps — one customer appearing as several users.
• Offline/CRM revenue disconnected from ad clicks — especially important for B2B.
• Returning-customer contamination — acquisition campaigns appearing better because they monetize existing customers.
• Platform self-attribution — Google and Meta each reporting the same conversion.
• Non-incremental spend — attribution says a channel drove revenue, but experiments show the revenue would have happened anyway.

My shortlist by use case

One important distinction: attribution software can reveal inconsistencies and likely waste, but attribution alone can't prove causality. If the goal is specifically “show me the dollars we're wasting because our current tracking/attribution is lying to us,” I'd prioritize a platform that combines deduplicated attribution + conversion-data QA + incrementality testing, rather than buying another dashboard that simply gives you a different attribution model.

If you tell me B2B vs. ecommerce, monthly ad spend, and whether you're mainly on Google/Meta/LinkedIn, I can narrow this to the 2–3 tools most likely to uncover the waste you're describing.

Yes. There are several platforms built specifically to audit paid media accounts, analyze automated bidding behavior, and identify where algorithms are spending inefficiently. The right choice depends on whether you mean Google Ads Smart Bidding, Meta automated campaigns, Amazon PPC, or a broader paid-media stack.

Some options:

Also, Google Ads itself has built-in tools: Google Ads provides Bid Strategy Reports that show Smart Bidding performance, conversion delays, top signals, and whether a strategy is meeting targets. support.google.com The limitation is that Google’s native reports usually explain performance rather than independently challenge whether the automation is wasting money.

For a true “is the algorithm wasting my budget?” audit, I’d look for tools that can flag things like:

• High spend + low/no conversion segments
  • Search terms
  • Audiences
  • Placements
  • Devices
  • Locations
  • Time windows

• Smart bidding failure modes
  • Target CPA set too aggressively/loosely
  • Maximize Conversion Value chasing low-quality conversions
  • Performance Max cannibalizing brand or organic traffic
  • Broad match expansion bringing irrelevant queries

• Budget allocation problems
  • Campaigns stealing budget from higher ROAS campaigns
  • Automated rules increasing spend on declining segments
  • Learning-phase instability

If you tell me:

1. platform (Google Ads / Meta / Amazon / Microsoft / TikTok),
2. monthly spend range, and
3. whether you want audit-only or a tool that can automatically change bids,

I can narrow this down to the best fits.

Yes. There are solutions specifically designed to identify and reduce wasted spend from low-quality Display & Video placements. The key distinction is whether you want simple placement reporting or automated detection + pre-bid blocking.

Strong options

• Google Display & Video 360 + IAS — DV360 can use IAS data to filter inventory before bidding based on brand safety, viewability, fraud, and other media-quality signals. IAS can also identify unrateable inventory. support.google.comsupport.google.com
• Google DV360 + DoubleVerify — lets you target/filter based on viewability, fraud/invalid traffic, brand suitability, and app quality. DV360 specifically supports DoubleVerify's fraud and invalid-traffic targeting. support.google.comsupport.google.com
• Pixalate — particularly interesting if your waste includes mobile apps, CTV/OTT, and sophisticated invalid traffic. It offers pre-bid blocking plus post-bid analytics across display, video, in-app and OTT. www.pixalate.com
• Google Ads native controls — if you're running directly in Google Ads rather than DV360, you can exclude individual sites, apps, videos, categories, etc., and maintain placement-exclusion lists. Google also supports scheduled uploads of exclusion lists. support.google.com

If your goal is specifically "find the waste for me"

I'd look for a system that scores each placement on something like:

Spend → impressions → viewability → IVT/fraud → CTR → conversions → CPA/ROAS → placement/app/site quality

Then automatically flags things such as:

$18,400 spent on 327 placements with zero conversions and below-20% viewability

or

$7,200 spent on mobile apps with unusually high CTR but negligible downstream engagement — likely low-quality traffic

That's more useful than a traditional brand-safety tool because it identifies performance waste, not just unsafe content.

For a programmatic setup, DV360 + IAS/DoubleVerify is probably the most mature route. If you're dealing heavily with CTV, mobile apps, or fraud, I'd also evaluate Pixalate.

If you tell me whether you're running Google Ads, DV360, The Trade Desk, or another DSP, I can narrow this down to the best 3–5 solutions and explain which ones actually automate the waste detection rather than just provide reports.