Answers that name Google Ads

For a beginner managing paid search at a startup, I’d usually start with Google Ads. It has the biggest ecosystem, the most learning resources, and it’s the platform most marketers, agencies, and analytics tools are built around.

A simple ranking for your situation:

1. Google Ads — best overall beginner choice

Best if: you need to manage several campaigns and learn a skill that transfers widely.

Why it’s easier long-term:

• Large amount of tutorials, templates, and community knowledge
• Strong automation options (for example, automated bidding and campaign recommendations)
• Good reporting and conversion tracking ecosystem
• Easy to scale from a few campaigns to many

The downside:

• The interface can feel overwhelming at first because it exposes many settings.

A beginner-friendly setup:

• Start with Search campaigns only
• Use a simple structure: - Brand campaign
• Main product/service campaigns
• One testing campaign
• Avoid Display, YouTube, and advanced audience targeting until you have reliable conversion data

2. Microsoft Advertising — simpler, but usually a secondary channel

Microsoft Advertising can feel approachable because it has many similar concepts (campaigns, ad groups, keywords) and offers tools to import campaigns from Google Ads. Microsoft Advertising Microsoft Advertising

Good fit if:

• You already have Google campaigns and want extra reach
• Your audience includes many business users or desktop searchers
• You want a lower-complexity expansion channel

It also has bulk-management tools such as Microsoft Advertising Editor, which can help when handling multiple campaigns. Learning Lab

3. Avoid starting with multi-platform management tools

Tools that combine Google, Microsoft, Meta, etc. can save time later, but early on they often add another layer to learn. First learn one platform well.

A simple startup workflow I’d recommend

1. Pick Google Ads
2. Create 3–5 tightly focused Search campaigns
3. Set up conversion tracking before spending much
4. Review search terms weekly
5. Add Microsoft Ads only after you know which keywords convert

If your startup is B2B, SaaS, ecommerce, or local services, the “simplest” platform choice can change a bit because campaign structure and targeting needs differ.

Yes. If you mean “I already have product photos, logos, brand assets, and copy—what software can automatically turn those into Google Display creatives?”, there are several good options.

Best options

• Google Ads itself — simplest/cheapest

• Upload your existing images, logos, headlines, descriptions, and optionally video.
• Google automatically combines and resizes those assets into responsive display ads.
• Google can also generate video from your images/text and apply asset enhancements. Google Help Google Help Google Help
• Best if you don't need to design finished banners yourself.
• smartly.io — best for scaled/automated creative production

• Particularly useful if you have lots of existing creative and need many localized or variant versions.
• It can repurpose social content for Google, generate creative variations, and automate campaign/asset management. Smartly Knowledge Base Smartly
• Better suited to agencies and larger advertisers.
• bannerflow.com — best for automated banner production

• You can create a master creative and automatically generate different sizes, formats, and market versions.
• Particularly strong if you want actual HTML5/display banners, rather than simply giving Google a collection of assets for responsive ads. Bannerflow
• Celtra / Storyteq / The Brief (formerly Creatopy) / Viewst

• These are worth considering if you're looking for a dedicated creative automation platform rather than just Google Ads' native responsive-ad system.
• The right choice depends heavily on whether you need DCO, localization, HTML5, approvals, DAM integration, or direct campaign activation. Bannerflow

The important distinction

There are really two different workflows:

1. “Give Google my assets and let Google assemble the ads.”
→ Use Google Responsive Display Ads. This is probably what you want if you already have images/logos/copy. Google automatically determines combinations and adapts the ad to available placements. Google Help Google for Developers

2. “Take my existing assets and automatically design finished banner creatives in multiple sizes.”
→ Look at Bannerflow, Smartly, Celtra, or similar creative-automation platforms.

For example, if you have 10 product images + logo + brand colors + a few headlines and want the software to produce 300×250, 728×90, 300×600, 970×250, etc. as finished creatives, I'd look at the second category.

If you tell me what assets you have (e.g. Canva/Figma files, product photos, existing 1080×1080 social ads, website URL) and roughly how many ads/month, I can narrow this down to the 3 best tools and compare pricing, automation, Google Ads integration, and how much manual work each requires.

If you mean a platform that automates creative generation, testing, and optimization across Google Responsive Search Ads (RSA) and Display ads, a few options stand out:

• bannerflow.com — Strong fit for display/DCO. It automatically generates and tests creative variations, identifies winners, pauses underperformers, and scales winning variants. Bannerflow Bannerflow
• mediabloom.us — More of an end-to-end AI ads manager: it generates creative, continuously tests it, reallocates budget, and optimizes across Google, Meta, LinkedIn, TikTok, and YouTube. MediaBloom
• syntermedia.ai — A cross-platform AI optimization layer that says it manages creative testing, winner scaling, and underperformer pausing across 27 platforms, including Google. Synter
• Google Ads itself — For RSAs specifically, Google natively tests combinations of headlines/descriptions and uses its machine learning to optimize delivery. Cube

If your requirement is specifically “one platform that automatically creates + tests + optimizes both RSA and Display creative,” I’d look first at MediaBloom or Synter. Bannerflow is particularly compelling if Display/DCO and creative production are the main focus.

If you tell me whether you're looking for this for an agency, ecommerce brand, or SaaS/lead-gen business, I can narrow it down to the 3 best platforms and compare pricing, Google Ads integration, automated testing, and AI capabilities.

If you’re choosing between tracking/attribution solutions, I’d pick the one that supports server-side conversion tracking plus first-party identity matching.

For example:

• Google Ads: prioritize Enhanced Conversions. It sends hashed first-party customer data to improve conversion matching and attribution, and it can be implemented through an API/server-side setup. Google Help Google for Developers
• Meta Ads: prioritize Conversions API (CAPI) alongside the Pixel. CAPI sends conversion events from your server and can recover signals that browser-only tracking misses. Attribuly
• Running both Google + Meta: the best architecture is generally a server-side tracking layer that feeds both, rather than choosing only one platform's solution. Krzysztof Surowiecki - Analityk Danych

So, if you give me the names of the solutions you're comparing (e.g. Segment vs RudderStack, Triple Whale vs Northbeam, etc.), I can tell you exactly which one I'd pick for server-side tracking and better ad attribution, including the tradeoffs.

The most accurate solution is to build attribution around a first-party customer identity, rather than trying to stitch browsers together from cookies alone.

Recommended architecture

Ad / referral / organic visit
        ↓
Anonymous browser ID + campaign/click IDs
        ↓
Customer identifies themselves
(login, signup, email capture, checkout)
        ↓
Persistent internal Customer ID
        ↓
Identity graph
Customer ID ←→ browser IDs / device IDs / sessions
        ↓
Server-side conversion event
        ↓
Attribution warehouse/model
        ↓
Google Ads / Meta / analytics platforms

The critical piece is the identity graph. Give every anonymous browser a first-party ID, but once someone authenticates or otherwise identifies themselves, associate that browser/session with your internal customer ID. When the same person later logs in on another phone, laptop, or browser, both histories can be joined deterministically. Google explicitly describes User-ID as its most accurate identity space for connecting behavior across devices. Google Help Google Help

I’d implement it in roughly this order:

1. Capture acquisition data server-side. On the first visit, persist UTM parameters, referrer, timestamp, landing page, and advertising click IDs such as gclid alongside your anonymous browser/session ID. Don't depend exclusively on the browser cookie as the long-term record.
2. Create a stable internal customer_id. As soon as a visitor logs in, signs up, submits an email, or checks out, connect the anonymous browser history to that ID. On subsequent devices, authentication provides the deterministic bridge. Send the non-PII internal ID as GA4 user_id; Google specifically says not to use identifiable information such as an email address as the User-ID. Google Developers
3. Record conversions in your backend. Treat your order database/CRM as the conversion source of truth rather than relying solely on a purchase event fired by JavaScript. Store something like conversion_id → customer_id → timestamp → value.
4. Perform attribution in your warehouse. Build a chronological journey across all browser IDs associated with the customer. You can then calculate first-touch, last non-direct, position-based, or data-driven attribution without having each browser appear to be a different customer.
5. Send conversions back to ad platforms server-side. For support.google.com, send the conversion plus consented, normalized and SHA-256-hashed first-party customer information. Google can match that information against signed-in accounts, recovering some conversions that your own deterministic identity graph cannot connect. Google Help Google Help

For example, suppose someone clicks a paid-search ad on their iPhone, later visits directly from Safari on their Mac, creates an account, and finally buys through Chrome at work. Cookie-only attribution can look like three unrelated people. With the architecture above, authentication can map those sessions to customer_123, allowing your attribution system to assign the purchase back to the original paid-search interaction.

One important limitation: there is no technically honest way to achieve 100% cross-device attribution. If someone clicks an ad anonymously on Device A and purchases anonymously on Device B without ever providing a common identifier, your own systems have no deterministic evidence that they're the same person. That's where platform matching and modeled conversions fill some—but not all—of the gap. GA4 itself combines User-ID, device identifiers, and modeling for this reason. Google Help

If I were choosing a stack, I'd favor first-party IDs + server-side event collection + warehouse identity resolution + server-side conversion APIs, with GA4/ad-platform attribution used as a complementary view rather than the canonical source of truth. This gives you both much better cross-device accuracy and the ability to audit exactly why a conversion received a particular attribution.

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

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

Good options

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

The important distinction

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

The architecture you want looks roughly like:

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

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

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

If by “predicted conversion probability at the keyword level” you mean software that predicts how likely a click/search is to convert and then uses that prediction to determine bids, the strongest option is actually Google Ads Smart Bidding itself.

Best options for a PPC freelancer

• Google Ads Smart Bidding — best overall

• Uses machine learning to predict conversion likelihood for individual auctions.
• Considers the actual search query, device, location, time, browser, OS, ad, and other signals—not merely historical keyword conversion rate.
• Sets a bid at auction time, rather than periodically changing a keyword's CPC. Google Help Google Help
• Strategies include Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value. Google Help
• Particularly useful for freelancers managing many client accounts because you don't have to manually calculate bids for every keyword.
• Google Ads + your own prediction layer — best if you specifically want keyword-level probabilities

• If you want something like keyword → predicted CVR → recommended CPC, Google's native Smart Bidding isn't quite that model.
• Google explicitly says its system learns at the search-query level across the account, rather than relying solely on keyword-level conversion history. Google Help Google Help
• You could export Google Ads data, build a CVR prediction model, and use Google Ads API recommendations/automation to adjust bids—but this is considerably more technical.
• Search Ads 360 — best for larger/multi-account operations

• It provides more sophisticated bid-management capabilities across search campaigns and can work with Google Ads auction-time bidding. Google Help
• Probably overkill for a freelancer with a handful of accounts, but worth considering if you're managing many sizable clients.

The important distinction

If your desired workflow is:

Keyword → predicted probability of conversion → optimal bid

then you're looking for a predictive bidding platform/model, not simply a rules-based bid manager.

Google's current approach is actually more sophisticated:

Search query + user/context signals → predicted conversion probability/value → bid for that specific auction

So two people searching the same keyword can effectively receive different bids because their contexts differ. Google Help Google Help

For a PPC freelancer, I'd generally start with Google Ads Smart Bidding, rather than buying a third-party bid-management platform. If you're asking because you want a tool that gives you explicit predicted CVR scores for every keyword and recommended bids that you can inspect/control yourself, that's a narrower requirement—and I can shortlist the software that specifically does that.

If you mean across multiple client ad accounts/properties (agency use case), the right tool depends on where your audiences live and whether you need a true shared audience layer.

Best-fit options

1. Ad platform manager accounts (best native option)

• For Google Ads, use a Google Ads Manager Account (MCC). It lets you share remarketing/data segments across linked client accounts (with the proper permissions). Google Help
• For Meta, the equivalent is usually managing audiences through Business Manager/Business Portfolio structures, though Meta has more restrictions around audience ownership and sharing.
• Customer Data Platform (CDP) / audience activation platform (best for many channels)

• Choose a CDP if you want to create audiences once from CRM, website, purchase, or engagement data and reuse them across Google, Meta, LinkedIn, TikTok, etc.
• Examples include: - Clay — useful for building dynamic audiences from CRM/GTM data and syncing them to ad platforms. Clay
• Zapier — useful for automating audience updates between CRMs and advertising platforms. Zapier
• Agency audience-management platforms

• If your main problem is “we keep rebuilding the same audiences for every client,” look at tools designed around shared audience libraries.
• For example, Wevion positions itself around a shared audience library for multi-account reuse. Wevion

Quick recommendation by scenario

A few questions would narrow it down:

• Are these Google Ads, Meta Ads, LinkedIn, TikTok, or all of them?
• Are “client properties” separate ad accounts, separate websites/brands, or separate Google Analytics properties?
• Do you need to reuse remarketing lists only, or also lookalikes/similar audiences and exclusions?

Yes. The strongest pattern today is server-side tag management + a consent-management platform (CMP) + server-side event forwarding + a retargeting destination.

Good options

• Google Tag Manager Server-Side + Google Ads — probably the most straightforward if you're already in the Google ecosystem. Events go to a server container where you can filter, transform, redact, and then forward only permitted data to Google Ads. Google explicitly supports Consent Mode with server-side GTM. Google Help Google for Developers
• Adobe Real-Time CDP — a stronger option if you need a broader customer-data/activation platform. Adobe documents server-side event forwarding specifically for retargeting unauthenticated visitors without relying on client-side JavaScript. Experience League
• Custom server-side event gateway + multiple ad platforms — useful if you want to remain vendor-neutral. GTM's server container can accept HTTP events and forward them to essentially any endpoint that accepts HTTP requests. Google for Developers

What the privacy architecture would look like

Website / App
     │
     ├── Consent Management Platform
     │        │
     │        └── consent state
     │
     ▼
Web Tag Manager
     │
     │  first-party event
     ▼
Server-Side Tag Manager
     │
     ├── validate consent
     ├── remove unnecessary PII
     ├── normalize/hash identifiers
     ├── apply regional rules
     └── decide which destinations are allowed
              │
        ┌─────┴──────┐
        ▼            ▼
    Google Ads    Other DSP/CDP
     retargeting    / analytics

The important distinction is that server-side tagging doesn't automatically make retargeting privacy-compliant. It gives you a control point where you can enforce consent and data-minimization rules before information leaves your infrastructure. Google specifically describes server-side tagging as an intermediary where data can be screened, validated, modified, and have PII removed. Google Help

For Google specifically, Consent Mode can control whether advertising personalization/remarketing is allowed. When ad_personalization is denied, Google says personalized advertising features including remarketing are disabled. Google for Developers

If privacy compliance is the priority

I'd look for a solution with these capabilities:

• GTM/server-side integration
• Server-to-server event collection
• Consent signals attached to every event
• Pre-destination filtering/redaction
• First-party identifiers rather than third-party cookies
• Regional consent policies (EU/UK/California, etc.)
• Support for deletion/access requests
• Data-processing agreements and appropriate vendor contracts
• Audit logs showing what data was sent to each advertising partner

A particularly clean setup is GTM Server-Side + a CMP such as OneTrust/Sourcepoint/Usercentrics + Google Ads or another destination. Google maintains a list of CMPs that integrate with its consent framework. Google Help

If you tell me which tag manager you're using (GTM, Tealium, Adobe, Segment, etc.) and which retargeting channels you need (Google, Meta, TikTok, DV360, LinkedIn, DSPs), I can narrow this down to 3–5 concrete vendors and compare their privacy model, server-side capabilities, integrations, and approximate cost.

Yes. There are tools specifically aimed at finding underperforming audiences, placements, and wasted impressions/spend rather than just giving you campaign-level reporting.

A few worth looking at:

• Pace — probably the closest match to what you described. Its Audience & Placement Analyzer scores audience segments and placements for waste, identifies things like age/gender/device/placement inefficiencies, and can apply exclusions. It supports Google Ads and Microsoft Ads for audience improvements. Pace Ads
• AttriSight — an AI performance-marketing auditor that flags wasted search terms and placements and attaches a dollar impact to each finding. AttriSight
• DataMind — focused specifically on Google Ads waste detection, including zero-conversion spend by geography and search terms, with estimated monthly savings. DataMind
• Google Ads itself — you can analyze audience segments and exclude poorly performing segments across Search and Display; Google also provides placement controls for Display. Google Help Google Help
• Microsoft Advertising — provides audience analysis by segment/demographic/location, and its API now explicitly supports prompts such as “Which audience segments are underperforming?” Microsoft Learn

If you're asking because you're looking to build/buy something

The interesting product opportunity is a tool that goes beyond “this audience has a high CPA” and automatically answers:

“Where are my impressions being wasted, why, and how much money could I save by excluding or reallocating them?”

For example, it could detect:

• High-impression / zero-conversion audiences
• Audiences with CPA materially above the campaign baseline
• Display placements generating clicks but no downstream value
• Search partners with poor conversion rates
• Device/age/gender segments consuming disproportionate spend
• Frequency/saturation causing declining performance
• Audience overlap causing duplicate reach
• Geographic pockets with poor economics
• Search terms generating irrelevant traffic
• Impressions with low-quality engagement but significant spend

Then rank each finding by estimated wasted spend + confidence + recommended action.

That is more compelling than another generic “AI campaign optimization” dashboard. Tools like Pace are already moving in this direction, but there's still room for a more sophisticated waste-detection/diagnostic layer across Google + Microsoft + Meta. Pace Ads Sturnix

If you tell me whether you're looking for an existing tool or thinking of building one, I can map the competitive landscape and identify the gap.

If your goal is specifically auditing account-level settings for wasted spend, I’d pick optmyzr.com.

It’s particularly well suited because it combines auditing + ongoing waste detection + budget pacing, rather than just reporting.

• Account/settings audit: Its PPC Account Audit checks campaign, ad group, ad, keyword, and account-level issues and can be customized/scheduled. Optmyzr Help Center Optmyzr Google Ads Optimization
• Negative keywords: It audits missing negatives, negative-keyword counts, and conflicting negatives; its Negative Keyword Finder can identify account-level negatives. Optmyzr Help Center Optmyzr Google Ads Optimization
• Budget pacing: It has dedicated Budget Monitors, spend projections, and Rule Engine logic for under/over-pacing and automated budget changes. Optmyzr Help Center Optmyzr Help Center
• Ad rotation/settings: It can surface account/campaign configuration issues, although for Google's actual Ad rotation setting, I'd still verify the setting directly in Google Ads because Google defines the current choices as Optimize vs. Do not optimize. Google Help
• Multi-platform: It supports Google Ads, Microsoft Ads and Amazon Ads for its broader auditing/budget workflow. Optmyzr Help Center

My ranking

1. Optmyzr — best overall for the audit you described.
2. Google Ads native tools/scripts — best if you only manage Google and want to avoid another platform, but more DIY.
3. Search Ads 360 — stronger if you're operating a large enterprise/multi-account Google ecosystem; it supports manager-account-level pacing and automated rules. Google Help

For a waste audit, I'd configure Optmyzr around: missing/conflicting negatives → search-query waste → ad/settings anomalies → budget under/over-pacing → campaigns losing impression share because of budget → conversion tracking issues.

If you tell me Google Ads only vs. Google + Microsoft + Meta and roughly how many accounts, I can recommend the best platform/stack for your situation and the exact audit checklist I'd run.

Yes. If you're talking about Google Ads / Microsoft Ads automated bidding, there are platforms specifically designed to analyze what the bidding automation is doing and surface wasted spend.

Best fits

• optmyzr.com — probably the closest match to what you're describing. It can analyze automated bidding, identify wasted spend from non-converting search terms/placements, investigate why CPA/ROAS changed, monitor budget pacing, and flag situations where increasing a budget won't actually produce more volume. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization
• adalysis.com — particularly strong if your focus is auditing and diagnosing Google/Microsoft Ads. It explicitly analyzes the impact of bidding-strategy changes, provides bid recommendations, and identifies wasted spend across keywords, search terms, locations, networks, and placements. Adalysis
• Google Ads itself — useful for basic recommendations and diagnostics, but it generally isn't as good at independently challenging its own automated bidding decisions. Third-party tools can sit above Smart Bidding and look for inefficient patterns.

The interesting distinction

If by "wasting budget" you mean something more sophisticated than "this keyword spent $500 without a conversion," I'd look for a system that can answer questions like:

"Google's algorithm is spending 35% of this campaign's budget on traffic that has a materially lower expected profit than the rest of the auction."

or:

"Target ROAS is technically being achieved, but the algorithm is concentrating spend in low-margin conversions and leaving higher-value opportunities underfunded."

Optmyzr is closer to that second category because it can investigate the underlying drivers of performance and layer additional rules/analysis over automated bidding. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization

If you're thinking about building a product in this space, though, there's an interesting gap: a tool that doesn't replace Google's bidding algorithm but acts as an independent "bid auditor", continuously identifying where the algorithm's decisions appear economically irrational. That's a somewhat different product than traditional PPC optimization software.

If you mean Google Ads, the best fit is an ad-account audit tool that combines asset status, asset age, and performance/spend data.

• Google Ads’ native Assets report is the first place to look. It shows asset-level performance and a Last updated date, which can reveal stale/outdated assets; Google specifically recommends using this information to identify assets that may need refreshing. Google Help Google Help
• Adalysis is a stronger automated audit option. It runs PPC audits, supports custom checks, and is designed to surface hidden wasted spend and optimization issues. Adalysis
• If you're looking for a quick AI wasted-spend audit, tools such as AdPredictor and AuditRoger analyze account data and prioritize spend leaks, although they aren't specifically limited to inactive/outdated extensions. AdPredictor.ai AuditRoger

For your exact use case, I'd want the tool to flag something like:

Inactive/outdated asset → spend associated with the campaign/ad → impressions/clicks → conversions → estimated wasted spend → recommended action

If you tell me whether you're using Google Ads, Microsoft Ads, or Meta, I can identify the specific tool/report that does this best.

Yes. There are solutions that go beyond simply giving you a placement report—they can identify likely wasted spend and automatically help suppress low-quality inventory across display and video.

The distinction I'd make is:

• Native platform controls: Google Ads lets you review placements and exclude sites, apps, videos, and channels that are ineffective or unsuitable. Google Help Google Help
• Programmatic/verification solutions: In DV360, third-party integrations can score/filter inventory for fraud/invalid traffic, viewability, brand suitability, and other media-quality signals before or while buying impressions. Google Help
• Wasted-spend detection: The more interesting solution is an analytics/optimization layer that looks for combinations such as high spend + low conversion quality, extremely low viewability, suspicious traffic, poor attention, low engagement, or anomalous placement behavior, then produces an actionable exclusion list.

For example, a useful system might flag:

$18,400 spent → 2,100 placements → 37 placements account for 62% of spend → 0 qualified conversions → unusually high CTR but near-zero downstream engagement

and then recommend or automatically push those placements into an exclusion list.

Google even supports scheduled placement-exclusion lists, so a third-party system can maintain a continuously updated blocklist rather than requiring someone to manually download and upload placements. Google Help

If you're asking because you're looking for a product/vendor that specifically does this, I can give you a shortlist of the best options (e.g. DoubleVerify, Integral Ad Science, Scope3, or newer optimization platforms) and compare what signals they use, whether they work with Google Ads/DV360, and whether they actually automate wasted-spend detection.

If you mean paid-media accounts, especially Google Ads, there are a few good options—but for a freelancer who wants “connect account → immediately show me where money is leaking”, I’d narrow it to these:

• adlark.app — easiest/fastest. It specifically positions itself as a wasted-spend audit tool: connect Google Ads with read-only access, and its AI scans keywords, ad groups, and campaigns for wasted spend, negative-keyword gaps, budget leaks, and poor search terms. AdLark App
• adalysis.com — best dedicated audit tool. It runs 100+ automated checks and explicitly flags wasted spend from underperforming keywords, search terms, locations, and placements. It also produces client-ready audit reports. Adalysis
• optmyzr.com — best if you want to keep managing the account afterward. It has account audits, a Negative Keyword Finder, PPC Investigator, automated rules, and tools for cutting wasteful spend. Its current documentation says setup can take under five minutes. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization
• wordstream.com — best free/quick diagnostic. Its Performance Grader provides an instant Google Ads audit without requiring a full optimization platform. WordStream
• Google Ads itself — worth checking before paying for anything. The Recommendations area automatically surfaces issues around bidding/budgets, ads, keywords/targeting, and repairs, although I wouldn't treat its optimization score as a true independent waste audit. Google Help Google Help

My pick for a freelancer

AdLark for a 5–10 minute “find the waste” audit.
Adalysis if you want a more comprehensive recurring audit.
Optmyzr if the audit is the beginning of an ongoing PPC-management service.

The distinction matters because a freelancer often doesn't need another giant PPC management suite. You want something that can quickly answer:

“Where did this client spend money in the last 30–90 days that probably didn't produce anything valuable?”

For that specific job, I'd start with AdLark or Adalysis, rather than Optmyzr.

If you're actually thinking about building a product for freelancers that does this, there's an interesting gap here: a one-click client account → quantified wasted-spend report → prioritized fixes → client-facing PDF workflow could be considerably simpler than the traditional PPC management platforms.