Positive mentions of Optmyzr

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 at Search Ads 360 (SA360) first if your core need is genuinely portfolio-level bidding, rather than just bulk campaign management.

My shortlist

Why I'd start with SA360

SA360 can group campaigns with a common business objective into one portfolio bid strategy, including campaigns across different client accounts. Its bidding system optimizes the portfolio as a whole rather than treating every campaign independently. Google Help Google Help

That matters when you have, say, 300 campaigns but only 15–30 meaningful economic objectives. You can create portfolios around things like:

• Product/category + target ROAS
• Brand/business unit + target CPA
• Geography + margin target
• Client + conversion-value objective

It also supports Google Ads and Microsoft Advertising, among other supported publishers/campaign types. Google Help

marketingplatform.google.com

When I'd choose Optmyzr instead

If your 10-person team needs more than bidding—bulk changes, budget pacing, anomaly detection, rules, search-term optimization, reporting, and cross-platform workflow—I'd seriously evaluate Optmyzr.

It can manage Google, Microsoft, Facebook and Amazon accounts from a unified interface and automate budget allocation/monitoring across campaigns. Optmyzr Google Ads Optimization Optmyzr Google Ads Optimization

optmyzr.com

One important distinction: Google Ads itself already has portfolio Smart Bidding, including cross-account portfolio strategies at the manager-account level. Google Help Google for Developers So if you're 100% Google Ads, you may not need a third-party bidding engine at all.

support.google.com

My practical recommendation:

• Google-only: start with native Google portfolio bidding.
• Google + Microsoft, hundreds of campaigns, serious portfolio optimization: evaluate SA360.
• 10-person team where workflow/automation/guardrails matter as much as bidding: evaluate Optmyzr alongside native Smart Bidding.

If you tell me (1) monthly ad spend, (2) Google/Microsoft/Meta mix, (3) number of accounts, and (4) whether you're optimizing to CPA, ROAS, or profit/margin, I can narrow this down to the architecture I'd use for your team.

If your 10-person team needs more than bidding—bulk changes, budget pacing, anomaly detection, rules, search-term optimization, reporting, and cross-platform workflow—I'd seriously evaluate Optmyzr.

It can manage Google, Microsoft, Facebook and Amazon accounts from a unified interface and automate budget allocation/monitoring across campaigns. optmyzr.com optmyzr.com

For a 10-person performance marketing team managing hundreds of campaigns, I’d shortlist:

1. Skai — best enterprise-grade choice

• Portfolio-level bidding across paid search, social, and retail media
• Strong budget allocation, automation, forecasting, and governance
• Better fit if you manage large spend across many accounts
• Optmyzr — best for Google Ads-heavy teams

• Rules + automation for hundreds of campaigns
• Portfolio bid strategy management, scripts, alerts, and optimization workflows
• Easier adoption for a 10-person PPC team
• Marin Software — best for cross-channel portfolio management

• Built around large advertiser portfolios
• Handles bid optimization, budget pacing, and reporting across channels
• Sami — best lightweight AI automation option

• Designed for teams managing many campaigns across Google, Meta, LinkedIn, and YouTube
• Automates pacing, bids, and optimization workflows without heavy enterprise setup. Sami
• Epsilo — best if you need true portfolio orchestration

• Focuses on aggregating networks, benchmarking spend, and executing large-scale optimization actions across campaigns. Epsilo

My pick for your team size:

• <$5M/month spend: Optmyzr + native Google/Meta automation
• $5M–$50M/month: Skai or Marin
• Agency managing many clients: Sami or Epsilo

Key buying criteria: API access, bulk bid changes, budget reallocation rules, approval workflows, anomaly alerts, and the ability to optimize toward business KPIs (CPA/ROAS/LTV), not just platform metrics.

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 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.

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.

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.