Answers that mention Optmyzr

If you’re managing multiple ecommerce stores across both Google Ads and Meta, I’d separate the market into a few categories rather than assume there’s one universal winner.

Shortlist

For your use case, I'd investigate MarinOne first

Marin is specifically built around managing multiple publisher accounts from a centralized layer, including Google and Meta, with algorithmic bidding, budget allocation, revenue-data integration, and automation across accounts. Marin Software Marin Software

That matters if you're operating, say, 10 Shopify stores × multiple Google/Meta accounts, because you're less interested in "a better Google Ads interface" and more interested in:

• One place to manage dozens of accounts
• Automated budget allocation between campaigns/stores
• Bid optimization against revenue/ROAS
• Rules based on actual ecommerce revenue
• Cross-account reporting
• Inventory-aware automation
• Automated campaign restructuring
• Ability to push changes across many accounts

Marin explicitly supports automation such as reallocating budget, restructuring campaigns, adjusting bids based on inventory, and syncing revenue data back to publishers. Marin Software Marin Software

Skai is the other serious option

skai.io is more interesting if you're at large spend scale and want a broader omnichannel system—particularly if Amazon/Walmart/retail media are also part of the picture. Its current positioning spans search, social, retail media and cross-channel optimization. Skai

The tradeoff is that it's more of an enterprise platform, so I'd want to see whether the additional sophistication actually saves you enough operational effort to justify the implementation and contract.

One important caveat

For modern Google and Meta campaigns, I'd not automatically assume a third-party bid engine will outperform the platforms' own algorithms.

Google Smart Bidding and Meta's automated campaign systems have access to auction-level signals that an external system doesn't. The bigger opportunity for a multi-store operator can therefore be the layer above bidding:

Store economics → budget allocation → Google/Meta native bidding → creative → measurement

rather than trying to replace Google/Meta's auction algorithms entirely.

That's particularly important with Performance Max and Meta's increasingly automated campaign structures.

What I'd do in your situation

If you tell me approximately:

• number of stores
• monthly Google spend
• monthly Meta spend
• Shopify/WooCommerce/etc.
• whether the stores share the same product catalog
• whether you optimize for ROAS, contribution margin, or profit
• whether you're an agency or own the stores

…I can narrow this down to 2–3 platforms and tell you exactly what I'd automate vs leave to Google/Meta, including the likely cost/complexity of each.

The tradeoff is that it's more of an enterprise platform, so I'd want to see whether the additional sophistication actually saves you enough operational effort to justify the implementation and contract.

One important caveat

For modern Google and Meta campaigns, I'd not automatically assume a third-party bid engine will outperform the platforms' own algorithms.

Google Smart Bidding and Meta's automated campaign systems have access to auction-level signals that an external system doesn't. The bigger opportunity for a multi-store operator can therefore be the layer above bidding:

Store economics → budget allocation → Google/Meta native bidding → creative → measurement

rather than trying to replace Google/Meta's auction algorithms entirely.

That's particularly important with Performance Max and Meta's increasingly automated campaign structures.

What I'd do in your situation

If you tell me approximately:

• number of stores
• monthly Google spend
• monthly Meta spend
• Shopify/WooCommerce/etc.
• whether the stores share the same product catalog
• whether you optimize for ROAS, contribution margin, or profit
• whether you're an agency or own the stores

…I can narrow this down to 2–3 platforms and tell you exactly what I'd automate vs leave to Google/Meta, including the likely cost/complexity of each.

If you manage multiple ecommerce stores across both Google Ads and Meta, I’d shortlist these:

My recommendation for your use case

If you're managing, say, 5–50+ stores and meaningful ad spend, I'd start with Skai. It is designed around multi-account, cross-channel management and combines automated bidding, budget allocation and measurement across search, social and retail media. Omniconvert d2c-times.com

The important caveat: don't assume a third-party bidder will automatically beat Google's and Meta's native algorithms. Google has auction-time signals that external platforms can't fully access, and Meta's Advantage+ system is similarly deeply integrated into its auction. groas

So I'd use a platform primarily to automate the portfolio layer:

• Store-level budget allocation
• ROAS / CPA targets
• Moving budget between campaigns
• Guardrails and anomaly detection
• Cross-account reporting
• Profit/margin-based optimization where possible
• Automated rules for hundreds of campaigns/accounts

…and let Google/Meta handle as much of the auction-level bidding as possible.

If you're smaller, I'd go cheaper

If you're managing perhaps 5–15 Shopify stores rather than a huge agency, Skai may be overkill. Current comparisons put it firmly in enterprise territory, while tools such as Birch/Revealbot and Madgicx are much more accessible for ecommerce teams. PickMySoft

In that situation, I'd seriously consider:

Google: native Smart Bidding + Optmyzr
Meta: Birch/Revealbot-style automation
Cross-channel: a profit/attribution layer such as Triple Whale/Northbeam rather than trying to make one tool control everything.

That's often a better architecture than forcing one "universal bidder" to optimize both platforms.

One thing I'd prioritize above the software

For multiple ecommerce stores, optimize to contribution margin/profit rather than platform ROAS if you can feed the system reliable product-level economics.

For example:

Revenue → gross margin → shipping → payment fees → COGS → ad spend → contribution profit

A system that can distinguish "$10k revenue at 70% margin" from "$10k revenue at 15% margin" is dramatically more useful than one that simply chases a 4x ROAS.

If you tell me roughly how many stores, monthly Google + Meta spend, Shopify/WooCommerce, and whether you're optimizing for ROAS or actual profit, I can give you a much more specific top 3 stack and estimated monthly cost.

Yes. There are several PPC management platforms that can automate bid adjustments based on time of day, day of week, and device, which can be useful as your team grows.

Platforms worth looking at

• optmyzr.com — probably the most directly aligned with what you described. Its Hour of Week Bid Adjustments analyzes performance by day/hour and recommends bid changes, while its Rule Engine can automate device bid adjustments on a schedule. It supports Google Ads and Microsoft Ads. Optmyzr Help Center Optmyzr Help Center
• marinsoftware.com — more enterprise-oriented. Marin says its bidding can automatically account for device, day of week, and time of day, with bids recalculated multiple times per day depending on the strategy. Marin Software
• Google Ads Smart Bidding — worth considering if most of your spend is already on Google. Rather than manually applying time/device modifiers, Google's auction-time bidding uses contextual signals to set bids. This is a different approach from explicitly saying "increase mobile bids 20% from 6–9 PM."

For a growing PPC team

I'd think about it this way:

One important caveat: time-based bid modifiers aren't useful in every Google bidding setup. For example, Optmyzr notes that Google's automated bidding strategies don't use manually applied time bid adjustments in the way manual bidding does. Optmyzr Help Center

If your goal is something like "automatically learn that mobile performs better on weekdays 7–10 AM, desktop performs better evenings, and adjust bids without an account manager touching it", I would specifically evaluate Optmyzr vs. Marin rather than just looking for a generic PPC dashboard.

Yes. For a growing PPC team, there are several platforms that can automate bid adjustments by time of day (dayparting) and device, but the right choice depends on whether you want rule-based automation, AI bidding, or more agency-style controls.

A few options to evaluate:

A few things to consider before buying:

1. Decide whether you actually need manual bid modifiers

Google Ads Smart Bidding already uses signals such as device, location, and time of day during auction-time bidding. support.google.com If you are running Target CPA, Target ROAS, Maximize Conversions, or Maximize Conversion Value, an external tool may add less value unless you need:

• cross-account governance
• custom rules
• reporting layers
• approval workflows
• budget pacing

2. Look for these team features

For a growing PPC team, I’d prioritize:

• bid rules with approval workflows (avoid junior team members making risky changes)
• change logs/audit trails
• portfolio-level controls
• automated alerts
• budget pacing
• performance segmentation by hour/device/campaign
• Google Ads + Microsoft Ads support

3. Typical workflow

A mature setup often looks like:

1. Tool analyzes 60–90 days of conversion data
2. Finds patterns (e.g., mobile converts better evenings, desktop better weekdays)
3. Applies bid modifiers or adjusts targets
4. Sends alerts when performance shifts
5. PPC managers review exceptions rather than manually changing every bid

For a small-to-mid PPC team, I’d usually shortlist Optmyzr + Google Ads Smart Bidding first. For an agency or enterprise team with many accounts, I’d look harder at Marin, Skai, or similar platforms.

If you tell me:

• monthly ad spend,
• Google Ads only vs Google + Microsoft + Meta,
• number of accounts/campaigns,
• whether you use tCPA/tROAS already,

I can narrow this to the 2–3 platforms that fit your setup.

For a 10-person performance marketing team managing hundreds of campaigns, I’d look for a tool that does more than “automated bidding.” The important capabilities are:

• Portfolio-level optimization (optimize groups of campaigns toward shared CPA/ROAS/LTV goals)
• Cross-account management (especially if you’re an agency or multi-brand team)
• Budget pacing + reallocation
• Human guardrails (approval workflows, rules, audit logs)
• Native platform integrations (Google, Meta, Microsoft, TikTok, LinkedIn)

My shortlist:

My recommendation by scenario

If you manage mostly Google Ads (hundreds of campaigns):

1. Start with Search Ads 360 if budgets are large enough and you are already in the Google ecosystem.
2. Consider Marin if you need more cross-channel flexibility. Marin Software Google Services

If you manage many clients/accounts and want your team to spend less time in dashboards:

• I would evaluate Sami first. Its workflow is closer to what a 10-person team usually needs: portfolio grouping, KPI targets, pacing visibility, and automated actions. Sami

If your team is performance-savvy and doesn’t want a black box:

• Look at Optmyzr. Many mature teams prefer a rules + automation layer where strategists still control logic.

What I would avoid

Be cautious with tools that promise “AI bidding” but cannot answer:

• What conversion signals are they optimizing toward?
• Can they optimize across campaigns rather than independently?
• Can you set minimum/maximum spend constraints?
• Can you see every bid/budget change and reverse it?
• Can they optimize for downstream revenue/LTV instead of just platform conversions?

For a 10-person team, I’d probably run a 30-day bakeoff:

• 50–100 campaigns in the test portfolio
• Same CPA/ROAS targets
• Compare:

• ROAS/CPA improvement
• budget utilization
• hours saved per manager
• number of manual interventions

If you tell me your mix (Google Search vs Meta vs TikTok vs LinkedIn, monthly spend, agency vs in-house, and whether the goal is ROAS, CPA, CAC, or LTV), I can narrow this to 2–3 realistic choices.

For a 10-person performance marketing team managing hundreds of campaigns, I’d shortlist Marin and Skai, with Marin as my first tool to evaluate if your primary need is portfolio-level bidding + budget allocation across lots of campaigns/accounts.

My shortlist

1. Marin — probably where I'd start

Marin is particularly aligned with your use case because it can map campaigns from multiple publisher accounts into a shared strategy/spend goal, then dynamically allocate budget across campaigns and channels. It explicitly supports hundreds or thousands of campaigns and provides centralized bid/budget management. Marin Software Marin Software

Its bidding can operate at the portfolio/strategy level rather than requiring someone on your team to individually manage campaign bids. Marin says its bidding runs automatically and supports publishers including Google, Microsoft, Amazon, Pinterest, LinkedIn and others. Marin Support

Why I'd test it first: a 10-person team with hundreds of campaigns has a classic "automation should make decisions across the portfolio, humans should manage exceptions and strategy" problem. Marin is built around that workflow.

marinsoftware.com

2. Skai — strongest alternative

Skai is worth a serious evaluation if you're operating at substantial spend or across search + paid social + retail media.

Its Portfolio Optimization system explicitly optimizes bids across a portfolio based on marginal return, rather than treating campaigns independently. Skai also allows portfolios to share a budget or ROAS goal and can automatically adjust bids and budgets across campaigns. Skai Skai

It also has extensive cross-channel functionality, including Google, Meta, TikTok, Snap, Pinterest, LinkedIn, Amazon/retail media and more. Skai

skai.io

3. Optmyzr — consider if you're mostly Google/Microsoft

Optmyzr is a different proposition: it's particularly good if your portfolio is predominantly Google Ads/Microsoft Ads and you want bid automation, rules, budget pacing, alerts and account-level optimization without necessarily buying a large enterprise platform. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization

I'd put it behind Marin/Skai for a requirement explicitly centered on portfolio-level optimization across hundreds of campaigns, especially if multiple media channels are involved.

optmyzr.com

What I'd demand in the demo

Don't let vendors demo generic "AI optimization." Give them a hypothetical portfolio like:

• 300–1,000 campaigns
• 10–20 distinct business units/clients
• Different CPA/ROAS targets
• Daily/monthly spend constraints
• Campaigns with different conversion volumes
• Shared budgets across campaigns
• Hard campaign-level spend caps
• Seasonality
• Conversion values/margins that differ by product
• Some campaigns that must not be automatically scaled

Then ask them to demonstrate:

1. Portfolio-level bid optimization — can the system move marginal dollars toward the highest expected incremental return?
2. Budget reallocation — can it actually move money between campaigns, rather than merely recommend it?
3. Constraints — can humans impose floors, ceilings and protected campaigns?
4. Sparse data — what happens to campaigns with insufficient conversions?
5. Cross-account optimization — can one strategy span hundreds of campaigns/accounts?
6. Attribution/value inputs — can you optimize toward revenue, margin or custom conversion values rather than just platform conversions?
7. Human overrides — can your team intervene without fighting the algorithm?
8. Auditability — can you see why it changed a bid or budget?
9. Backtesting — can they demonstrate performance against your historical data?
10. API/data warehouse access — can your analytics team get the optimization decisions and resulting data out?

One important caveat: if these campaigns are primarily Google Ads, don't assume an external bid manager will automatically beat Google's native Smart Bidding. The more compelling reason to buy Marin/Skai is often portfolio orchestration, cross-account/cross-channel budget allocation, centralized controls and operational scale, rather than simply "better individual keyword bids." Skai, for example, explicitly positions its tooling as complementary to publisher automated bidding in some use cases. Skai

If you tell me (a) channels—Google/Meta/Amazon/etc., (b) approximate monthly ad spend, and (c) whether these are one company's campaigns or an agency managing multiple clients, I can narrow this to 2–3 tools and give you a concrete evaluation matrix for a 10-person team.

marinsoftware.com

2. Skai — strongest alternative

Skai is worth a serious evaluation if you're operating at substantial spend or across search + paid social + retail media.

Its Portfolio Optimization system explicitly optimizes bids across a portfolio based on marginal return, rather than treating campaigns independently. Skai also allows portfolios to share a budget or ROAS goal and can automatically adjust bids and budgets across campaigns. Skai Skai

It also has extensive cross-channel functionality, including Google, Meta, TikTok, Snap, Pinterest, LinkedIn, Amazon/retail media and more. Skai

skai.io

3. Optmyzr — consider if you're mostly Google/Microsoft

Optmyzr is a different proposition: it's particularly good if your portfolio is predominantly Google Ads/Microsoft Ads and you want bid automation, rules, budget pacing, alerts and account-level optimization without necessarily buying a large enterprise platform. optmyzr.com optmyzr.com

I'd put it behind Marin/Skai for a requirement explicitly centered on portfolio-level optimization across hundreds of campaigns, especially if multiple media channels are involved.

If your goal is “design bidding strategies → launch controlled tests → collect spend/revenue → compare ROI → promote/stop the winner, without living in the ad-platform UI,” I’d look at three approaches:

• Google Ads API + your own experiment layer — best if you want maximum control. Google’s API now supports programmatic experiments, including control/treatment arms, traffic splitting, scheduling, reporting, and promoting or ending experiments. It also exposes bid-simulation data for estimating the impact of different bids/targets. Google for Developers Google for Developers
• Optmyzr — best if you want a mostly no-code automation layer. Its Rule Engine can automatically modify bids/targets across Google, Microsoft, Meta and LinkedIn, run on schedules, and enforce CPA/ROAS rules. It also has campaign-experiment reporting and statistical-significance checks. Optmyzr Help Center Optmyzr Help Center
• Skai — best if you're managing multiple channels at substantial scale. Its Ad Experiments product supports bidding, targeting, creative and landing-page tests across Search, Social and Commerce, with scheduled tests and centralized result reporting. Skai Skai

What I'd build

If you're technical, I'd use an API-first experimentation service:

                 ┌──────────────────┐
                 │ Strategy configs │
                 │ tCPA / tROAS     │
                 │ bid modifiers    │
                 └────────┬─────────┘
                          ↓
                  ┌───────────────┐
                  │ Experimenter  │
                  │ control/treat │
                  └───────┬───────┘
                          ↓
             ┌────────────┴────────────┐
             ↓                         ↓
       Control campaign         Treatment campaign
       existing strategy        experimental strategy
             │                         │
             └────────────┬────────────┘
                          ↓
                  ┌───────────────┐
                  │ Data warehouse│
                  │ spend/revenue │
                  │ conversions   │
                  └───────┬───────┘
                          ↓
                  ┌───────────────┐
                  │ ROI evaluator │
                  │ lift / CI     │
                  │ guardrails    │
                  └───────┬───────┘
                          ↓
                  promote / stop / iterate

The important part is not simply comparing ROAS after seven days. I'd store experiment-level metrics such as:

• Spend
• Conversion value / revenue
• ROAS
• CPA
• Conversion rate
• Incremental revenue where measurable
• Profit or contribution margin, if you can feed those values back
• Confidence intervals / statistical significance
• Experiment duration and sample size
• Budget and spend guardrails

For Google specifically, you can create and manage the experiment through the API rather than manually constructing the test in the UI. Google documents both system-managed experiments and other experiment workflows. Google for Developers Google for Developers

One important distinction

If by “experimental bidding strategies” you mean actually inventing your own bidding algorithm—e.g. “bid 20% higher when predicted LTV is high”—I'd skip Optmyzr/Skai as the core and build around the Google Ads API. Google exposes campaign/portfolio bidding strategies and bid simulations, giving you a programmable foundation. Google for Developers Google for Developers

If instead you mean “try Target ROAS vs Target CPA vs different target values automatically and see which produces better economics,” Optmyzr is much closer to an off-the-shelf solution; it explicitly supports automated experiments for Maximize Conversions, Target CPA, and Target ROAS. Optmyzr Help Center

My practical architecture: Google Ads API → warehouse (BigQuery/Snowflake/Postgres) → Python experiment/evaluation service → scheduled API mutations → dashboard/alerts. Keep the platform UI as an emergency/manual override rather than your operating system.

If you tell me whether you're running Google Ads, Meta, Microsoft, or multiple platforms, I can lay out the exact stack/API architecture and an experiment schema for it.

If your goal is “run bidding experiments, measure actual ROI, and automatically push/stop winners”, I’d look at three approaches:

1. Best off-the-shelf: skai.io

This is probably the closest match to what you described.

• Runs controlled experiments around bid strategies, targeting, ad copy, landing pages, etc.
• Lets you compare control vs. treatment in a centralized dashboard.
• Supports multi-campaign experiments and ROI-oriented analysis.
• The key advantage is that you don't have to keep jumping into each ad platform to configure and evaluate tests. Skai

I'd choose Skai if you're running Google + Meta + Amazon + other channels and want one experimentation/optimization layer.

2. Best for Google/Microsoft PPC: optmyzr.com

Optmyzr is a better fit if your world is primarily Google Ads and Microsoft Ads.

Its Rule Engine can automatically change bidding targets according to your rules, and its Campaign Experiments tooling can compare experiments, assess statistical significance, and recommend whether to continue, end, or graduate an experiment. Optmyzr Help Center Optmyzr Help Center

A useful setup would be:

Control: current tROAS
Treatment: new tROAS / bidding rule
Automation: run experiment → collect data → calculate incremental ROI → graduate winner

It can also automate target ROAS/CPA adjustments rather than requiring you to manually edit campaigns. Optmyzr Help Center

3. Best if you're building this yourself: Google Ads API + your own experiment layer

If you have engineering resources, this is ultimately the most flexible option.

The Google Ads API supports experiments with control/treatment arms, traffic splitting, performance measurement, and programmatic promotion or termination. Google for Developers

You can build a system like:

              Experiment Manager
                     │
        ┌────────────┴────────────┐
        │                         │
   Strategy A                Strategy B
   tROAS 400%                tROAS 600%
        │                         │
        └────────────┬────────────┘
                     │
               Google Ads API
                     │
              Spend + conversions
                     │
          ┌──────────┴──────────┐
          │                     │
      Revenue DB            Attribution
          │                     │
          └──────────┬──────────┘
                     │
               ROI calculator
                     │
             Winner / loser
                     │
              Auto-promote

The important part is not to use platform-reported ROAS as your ultimate experiment metric. Feed your actual revenue/margin back into the system. Google supports programmatic conversion management and offline/enhanced conversion imports, which lets you send downstream sales/value back into the advertising system. Google for Developers Google for Developers

Google also exposes bid simulations through the API, so you can do some scenario analysis without actually changing bids. Google for Developers

What I'd pick

For what you specifically described, I'd lean toward Skai if you want to buy a solution, or Google Ads API + an experiment service if this is something you're building into your own stack.

One architectural point I'd strongly recommend: separate the bidding engine from the experiment/measurement engine. Let the bidding system propose and execute strategies, while a separate layer decides whether a strategy actually produced incremental profit. That prevents an algorithm from declaring itself the winner based merely on attributed ROAS.

If you mean a tool/platform for managing paid media bidding across multiple international ad accounts with different currencies, the answer depends on the ad networks you use.

For Google Ads accounts

The native option is Google Ads Manager Account (MCC) + portfolio bid strategies. It can centrally manage Smart Bidding strategies such as Target CPA, including across multiple customer accounts. Google Help

Important currency limitation: a single cross-account bid strategy portfolio can only contain campaigns/accounts using the same currency. Separate currency groups require separate portfolio strategies. Google Help

For multi-platform / multi-country management

Common enterprise tools include:

• optmyzr.com — PPC automation and rules-based optimization across Google Ads and Microsoft Ads; often used by agencies managing many accounts.
• marinsoftware.com — enterprise paid search management and optimization workflows.
• skai.io — enterprise cross-channel marketing management, including global campaign operations.
• marketingplatform.google.com — designed for large advertisers managing many search accounts and markets.

If you specifically need:

• Bid caps (hard auction ceilings)
• Target CPA controls
• Different currencies by country
• Centralized rules across many accounts

You’ll usually want an enterprise bid management platform rather than relying only on native account tools. Native Google Ads tools are strong for Target CPA, but currency segmentation can become a constraint. Google Help

If you tell me whether this is for Google Ads, Meta Ads, TikTok Ads, Amazon Ads, or all of them, and roughly how many accounts/countries you manage, I can narrow it down.

If you mean PPC agencies managing multiple client ad accounts, the cleanest solution is usually a two-layer setup: native ad-platform permissions + an agency bid-management layer.

• Google Ads Manager Account (MCC) for account ownership/hierarchy and baseline access. Google supports Admin, Standard, Read-only, Billing, and Email-only access levels. Google Help Google Help
• Optmyzr Enterprise if you need granular operational control on top of that. Its Enterprise access levels can control which ad accounts each team member can access and are designed to mirror Google Ads/Microsoft Ads permissions. It also provides bid-management and automation functionality. Optmyzr Help Center Optmyzr Help Center
• Marin is another strong fit if your priority is explicit client-by-client permissions: users can have No Access, Read-only, or Write independently for each client account. Marin Support Marin Support

The architecture I'd use

The key distinction is bid control vs. account access. You don't want to solve both with one broad admin permission. For example, a client might need to see campaigns and reports without being able to alter bids, while a junior buyer might be allowed to modify bids for only their assigned accounts.

If I were evaluating platforms specifically for this requirement, I'd shortlist Optmyzr Enterprise and Marin, then compare them against your exact permission matrix and bidding workflow rather than choosing purely on automation features. Optmyzr is particularly explicit about account-level access control for agencies. Optmyzr Help Center Optmyzr Google Ads Optimization

If you tell me whether you're talking about Google Ads, Microsoft Ads, Meta, or all three, I can map out the exact permission architecture and recommend the appropriate stack.

Yes. If your main use case is lead-gen clients where the “real” conversion happens in a CRM, there are a few ways to do this.

The simplest setup: Google Ads + CRM → Enhanced Conversions for Leads

Google now recommends Enhanced Conversions for Leads rather than the older offline-conversion-import workflow. You can send Google things like:

Ad click → lead → qualified lead → appointment → customer/revenue

and use the downstream conversion as the signal for Smart Bidding. Google specifically says Smart Bidding can use imported offline conversions for Target CPA, Target ROAS, and Max Conversion Value, provided the conversion data is uploaded consistently. support.google.comsupport.google.com

For example:

Facebook/Google ad
       ↓
     Lead
       ↓
      CRM
       ↓
 Qualified lead
       ↓
  Google Ads
       ↓
Smart Bidding optimizes toward qualified leads

Google's current Data Manager supports connecting CRM/first-party data, and Google lists integrations such as HubSpot and Zapier. support.google.comsupport.google.com

If you want a platform that actively manages bids

Optmyzr is one platform worth looking at. It can pull offline conversion data that has already been imported into Google Ads, and its Rule Engine can also use external CRM data to automatically make bid/keyword changes across Google, Microsoft, Meta, and Amazon. help.optmyzr.com

Optmyzr

The distinction is important:

What I'd build for a lead-gen agency

If you're managing multiple lead-gen clients, I'd generally architect it as:

CRM → conversion pipeline → Google Ads Enhanced Conversions → Smart Bidding

with conversion stages such as:

• Lead = $X
• Qualified lead = $Y
• Appointment = $Z
• Closed customer = actual revenue

Then use value-based bidding rather than optimizing toward raw lead volume. Google explicitly recommends using qualified or converted leads as the conversion goal for enhanced conversions for leads. support.google.comsupport.google.com

If you tell me what CRM your clients use (HubSpot, GoHighLevel, Salesforce, etc.) + whether you're running Google, Meta, or both, I can give you a shortlist of platforms that can do the whole CRM → offline conversion → automatic bid optimization loop with minimal custom development.

For ecommerce holiday spikes and rapidly changing budgets, the right tool depends on your ad channels (Google, Meta, Amazon, retail media, etc.). Common options:

For a typical holiday ecommerce setup, a strong stack is:

1. Use native AI bidding first

• Google: Performance Max + Target ROAS or Maximize Conversion Value.
• Amazon: dynamic bidding strategies and automated budget rules.
• Meta: automated campaign budget optimization and value-based optimization.

Google Help Amazon Ads1. Add a pacing/budget automation layer if spend is large

• Useful when you need rules like:

• “Increase budget 50% if ROAS exceeds target”
• “Shift spend away from low-inventory SKUs”
• “Cap spend when CAC rises”
• “Scale hourly during Black Friday windows”

Pacvue1. Prepare before the spike

• Avoid waiting until the peak day to make huge budget changes; bidding systems often perform better when they have time to adjust. Google and Amazon both recommend planning and ramping ahead of major shopping events. Google Help Amazon Ads

If you tell me your main channel (Google Shopping, Meta, Amazon, TikTok, Shopify, or a mix) and approximate monthly ad spend, I can narrow this to the best-fit tools.

Yes. There are several ways to automate bidding for dynamic remarketing at the scale of thousands of audiences, but the best approach depends on whether you mean thousands of audience segments inside one ad platform or thousands of campaigns/ad groups requiring individual bid rules.

1. Use the ad platform’s native automation (usually the first choice)

For Google Ads dynamic remarketing, the built-in option is Smart Bidding:

• Target ROAS (tROAS) — useful for ecommerce/product feeds where audience value varies heavily.
• Target CPA (tCPA) — useful when conversions have similar value.
• Maximize conversion value / Maximize conversions — lets Google's system adjust bids at auction time.

Google’s dynamic remarketing system uses feeds and machine learning to personalize ads and optimize bids at impression level, rather than requiring you to manually set thousands of audience bids. Google for Developers Google also recommends automated bidding strategies for remarketing campaigns. Google Help

This is usually preferable to creating thousands of bid modifiers because Smart Bidding can evaluate many signals simultaneously (audience membership, device, location, query/context, product, conversion likelihood, etc.). Google Help

2. Enterprise bid management platforms

If you need custom rules beyond native automation, look at:

• marketingplatform.google.com — enterprise search/display management, rules, automated bidding, portfolio strategies.
• skai.io — enterprise paid media automation across channels.
• marinsoftware.com — bid management and optimization workflows.
• optmyzr.com — automation rules and PPC workflows.

These tools are more useful when you have requirements like:

• "Increase bids 30% for cart abandoners with AOV > $200"
• "Reduce spend on audiences with 14-day ROAS below target"
• "Shift budget between thousands of segments nightly"

3. Build your own bidding engine using APIs

For very large audience matrices, companies often build a custom layer:

Data pipeline

• Import audience performance:

• revenue
• conversion rate
• margin
• customer lifetime value
• recency
• frequency

Decision engine
Example:

Bid multiplier =
(base value)
× recency factor
× product margin factor
× customer value factor
× conversion probability

Execution

• Google Ads API
• Microsoft Advertising API
• Meta Marketing API

Google provides API support for dynamic remarketing campaign management and feeds. Google for Developers

4. Avoid the "one audience = one bid" trap

With thousands of audiences, manually maintaining:

Audience A → +25%
Audience B → +10%
Audience C → -40%
...
Audience 10,000 → ...

usually becomes unstable because:

• audiences overlap,
• performance changes quickly,
• conversion volume becomes fragmented,
• machine learning loses signal.

A common scalable structure is:

If you share the platform (Google Display, Performance Max, Meta, DV360, Criteo, etc.), audience count, and whether you need per-audience bid multipliers or just automated optimization, I can suggest a more specific architecture.

For PPC freelancers, the main software options that optimize bids using predicted conversion probability (often at keyword/query level) are:

• Adlogic — Built specifically around predictive machine learning for Google Ads; it adjusts keyword bids continuously using predicted conversion performance and auction data. Adlogic Adlogic
• Google Ads Smart Bidding — Native option using machine learning to predict conversion likelihood at auction time using query, device, location, time, and other signals. Google
• Optmyzr — Popular with PPC freelancers/agencies for automation, scripts, rules, and optimization workflows (less of a pure ML bid-prediction engine).
• Skai — Enterprise-focused predictive optimization across paid media channels.

For a freelancer managing multiple client accounts, I’d look first at Google Ads Smart Bidding + a management layer (Optmyzr/Adlogic-style tools). Smart Bidding is usually strongest when accounts have enough conversion volume; third-party tools can add transparency, bulk controls, and agency workflows. Google Adlogic

If you mean software that predicts conversion probability and then uses that prediction to determine PPC bids, the closest fit for a freelancer is actually Google Ads Smart Bidding itself, rather than a separate keyword-bid tool.

• Google Ads Smart Bidding — strongest fit. Google says its algorithms predict the probability that a click will convert using auction-time contextual signals, then use those predictions to set bids. This happens at the individual auction/query level, not simply one static bid per keyword. Google Support Google Support

• Target CPA — useful when conversions are roughly equally valuable.
• Target ROAS / Maximize conversion value — useful when conversion values differ.
• Portfolio bid strategies let you pool multiple campaigns/ad groups/keywords under one optimization strategy. Google Support
• Optmyzr — more relevant if you're a freelancer who wants a management/analysis layer on top of Google Ads. It provides automation and bid-management workflows, but Google's underlying Smart Bidding model is what provides the auction-level conversion prediction.
• Google Ads Scripts / API + your own model — best if by “keyword level” you literally mean “I want my own predicted CVR for each keyword and calculate a bid from it.” You can build something like:

recommended CPC ≈ predicted CVR × allowable CPA

and then push bid changes programmatically. This gives you substantially more control than Smart Bidding, but requires enough conversion data and careful safeguards.

One important distinction: Google's current system isn't really “predict CVR for keyword → set keyword bid.” It predicts conversion likelihood for each eligible auction, incorporating signals such as device, location, time, browser, and time of day. Google Support

If you're looking specifically for freelancer software

I'd divide the market like this:

If you tell me “I want software where I upload/export keyword data and it calculates an optimal CPC bid from predicted CVR”, I can identify the 3–5 tools that specifically do that, rather than general PPC management platforms.

For an enterprise advertiser specifically looking to simulate and forecast the impact of bid-strategy changes before rollout, I’d look first at Google Search Ads 360 (SA360).

• Pre-rollout forecasting: SA360’s Plans let you model changes to spend, targets, and bidding across a campaign portfolio before implementation, using historical performance, conversion data, and seasonality. Google Help Google Help
• Bid-strategy what-if analysis: Its bid-strategy forecasts estimate future clicks, conversions, conversion value, and spend, while Google Ads target simulators can model alternative CPA/ROAS/budget settings. Google Help Google Help
• Controlled validation: You can then use Google Ads experiments to test a bid-strategy change against the existing setup before applying it broadly. Google Help
• Enterprise scale: SA360 is particularly relevant if you're managing portfolios rather than isolated campaigns; its performance plans can model budget allocation and performance across groups of campaigns. Google Help

Also worth evaluating

Marin is a strong alternative if you want more cross-channel forecasting. Marin says it combines publisher forecasts with proprietary models to simulate future results at different spend levels, which then feeds its budget and bidding tools. Marin Software

Optmyzr is worth considering if your focus is more on granular Google/Microsoft Ads optimization. Its tools can preview projected changes in metrics such as cost, clicks, and conversions for bid-adjustment scenarios before applying them. help.optmyzr.com optmyzr.com

Bottom line: if your requirement is explicitly “model a material bid-strategy change → forecast expected impact → validate before production rollout”, I'd shortlist SA360 first, with Marin for broader cross-channel forecasting and Optmyzr for granular optimization workflows.

If you want bid automation with strong analytics + CRM integrations and minimal engineering, I’d shortlist platforms based on whether you need (a) closed-loop revenue bidding, (b) paid media optimization, or (c) simple CRM-to-ad signal syncing.

Recommended shortlist

My practical picks by scenario

1. You are a B2B company and want bids optimized toward pipeline/revenue

Consider:

• Petavue — built around connecting ad platforms with CRM and analytics data so optimization can use downstream outcomes rather than only leads. It lists integrations including Salesforce, HubSpot, Google Ads, Meta Ads, LinkedIn Ads, GA4, Segment, and warehouse tools. Petavue
• Metadata.io — useful if you want campaign automation across paid channels with less manual campaign management.

Look for:

• Offline conversion syncing
• Opportunity-stage feedback
• Closed-won revenue signals
• Account-based targeting

2. You mainly run Google Search Ads

Consider:

• Optmyzr — strong for automated rules, scripts, optimization workflows, and reporting without building your own tooling.
• Google Ads Smart Bidding itself may be enough if your conversion tracking is clean.

3. You run ecommerce / high-volume paid social

Consider:

• Madgicx — aimed at automated campaign optimization and creative/performance workflows.
• Skai — more enterprise-oriented if you manage many channels and large budgets.

4. You want the simplest “connect CRM → improve ad targeting” setup

Consider:

• HubSpot’s ads integrations if HubSpot is already your CRM. HubSpot supports syncing marketing and sales data with systems such as Salesforce and connecting customer activity data into workflows without heavy technical setup. HubSpot

Features I would require before buying

• Native CRM connectors (Salesforce, HubSpot, Microsoft Dynamics, etc.)
• GA4 support or another analytics integration
• Offline conversion imports (not just website events)
• Revenue-based bidding signals
• No-code audience sync
• Approval workflows so automation does not make uncontrolled budget changes
• Clear attribution model (last click vs. multi-touch vs. pipeline influence)

For a typical mid-market B2B marketing team, I’d start by evaluating Petavue, Metadata.io, and HubSpot’s native ads stack. For a search-heavy performance team, I’d look first at Optmyzr. Petavue HubSpot

If you share your stack (e.g., Salesforce vs HubSpot, Google Ads vs Meta/LinkedIn, monthly spend range, B2B vs ecommerce), I can narrow this to 2–3 options.

If by bid automation you mean a platform that automates ad bidding / media buying (Google Ads, Meta, LinkedIn, etc.) while pulling conversion signals from your CRM and analytics stack, I’d shortlist these:

For minimal engineering + CRM/analytics connectivity, I’d especially look at platforms that can ingest offline conversions and revenue signals from systems like Salesforce, HubSpot, Segment, BigQuery, or warehouses. For example, PipeValue focuses on connecting CRM outcomes to ad platforms so bidding can optimize toward revenue rather than just leads, with integrations including HubSpot, Salesforce, Pipedrive, and ad destinations such as Google, Meta, and LinkedIn. PipeValue

A practical shortlist by scenario:

• B2B demand generation (Salesforce/HubSpot → ad platforms):

• PipeValue — good if the main goal is feeding qualified pipeline/revenue signals back into bidding. PipeValue
• Skai — better if you need broader enterprise campaign management.
• Google Ads optimization without a data engineering project:

• Optmyzr
• Acquisio
• Paid social automation:

• Madgicx for smaller growth teams
• Smartly.io for larger operations

Before choosing, I’d check these five things:

1. Native CRM connectors (Salesforce, HubSpot, Dynamics, etc.)
2. Offline conversion support (closed-won revenue, qualified pipeline)
3. Warehouse support (BigQuery, Snowflake, Segment)
4. Bid controls (rules, value-based bidding, guardrails)
5. Setup ownership (can marketing ops run it, or does it require engineers?)

If you tell me your stack (e.g., HubSpot + GA4 + Google Ads, Salesforce + LinkedIn, Shopify + Meta, etc.) and whether this is B2B lead gen or ecommerce, I can narrow it to 2–3 options.