ChatGPT answers, unedited

If you want to run experimental bidding strategies, measure incremental ROI, and avoid manually changing Google/Meta/etc. dashboards, you’re usually looking for a combination of:

1. ad platform APIs + an experimentation layer
2. automated bidding/control software
3. a data warehouse + attribution model

The right stack depends on how sophisticated you want to get.

Option 1: No-code / low-code marketing optimization platforms

Good if you want to run tests without building infrastructure.

• Optmyzr — strong for Google Ads automation, scripts, bid experiments, rules, and optimization workflows.
• Skai — enterprise cross-channel optimization (search, social, retail media).
• Marin Software — automated campaign management and optimization.

Typical workflow:

• Define experiment: “Target CPA -15% vs maximize conversions”
• Split campaigns/audiences
• Let platform adjust bids
• Pull revenue/conversion data
• Compare CAC, ROAS, incremental lift

Option 2: Build your own experimentation engine (best for serious scale)

A common architecture:

Ad APIs

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

↓

Data warehouse

• Google BigQuery
• Snowflake

↓

Experimentation layer

• Python/R models
• Bayesian testing
• multi-armed bandits
• causal inference models

↓

Automation

• Push bid changes back through APIs
• Log every change
• Roll back bad experiments

This gives you things like:

• “Increase bids only where marginal ROAS > target”
• “Shift budget automatically toward statistically superior strategies”
• “Test value-based bidding against CPA bidding”

Option 3: AI-driven bid optimization

If you want something closer to an autonomous agent:

• Google Ads Smart Bidding — native automated bidding using conversion/value signals.
• The Trade Desk — programmatic optimization at larger scale.
• Kochava or AppsFlyer — useful for mobile attribution and measuring incrementality.

For ROI comparison, don't rely only on platform ROAS

Platforms often over-credit themselves. Your experiment system should track:

• incremental revenue
• blended CAC
• contribution margin
• customer lifetime value
• holdout/control performance
• time-to-payback

A good experiment setup:

My usual recommendation by company size

<$50k/month ad spend
→ Optmyzr + Looker Studio + warehouse exports

$50k–$1M/month
→ BigQuery + ad APIs + custom experimentation scripts

$1M+/month
→ custom bidding engine, causal measurement, automated budget allocation

If you tell me your channels (Google Ads, Meta, TikTok, Amazon, etc.), monthly spend, and whether you optimize for leads or revenue, I can suggest a more specific stack.

If your goal is “run bidding experiments, measure incremental ROI, and avoid living in Google/Meta/Microsoft UIs,” I’d look at these approaches:

Best fit: Optmyzr

Optmyzr is probably the closest off-the-shelf answer.

It can:

• Create and manage Google Ads experiments and compare treatment vs. control.
• Test Maximize Conversions, Target CPA, Target ROAS, etc. help.optmyzr.comhelp.optmyzr.comhs.optmyzr.comdevelopers.google.com
• Analyze experiments for statistical confidence and recommend whether to graduate, continue, or terminate them. help.optmyzr.comhelp.optmyzr.comhs.optmyzr.comdevelopers.google.com
• Apply winning changes without you having to work through the ad-platform interface.
• Automate target-CPA/ROAS adjustments and other optimization rules. help.optmyzr.com
• Connect directly to Google Ads via its API; it also supports Microsoft, Meta, and Amazon advertising workflows. hs.optmyzr.com

So a workflow could be:

Control: current Target ROAS 400%
Treatment: Target ROAS 500%
→ automatically split traffic
→ collect spend/conversion-value data
→ compare incremental ROAS, CPA, conversion volume, etc.
→ automatically keep/kill/promote the winner.

If you want maximum control: build on the ad APIs

For Google specifically, the Google Ads API has first-class experiment support for testing campaign structure and bidding changes, with control/treatment arms and performance comparison. developers.google.com

I'd choose this route if you're building an internal experimentation system and want to define your own objective, e.g.:

Incremental gross profit / incremental ad spend

rather than simply optimizing platform-reported ROAS.

My recommendation

One important distinction: ROAS isn't necessarily ROI. If you care about actual business profitability, feed the experiment system revenue/margin or customer-value data rather than relying exclusively on the ad platform's reported conversion value.

If you tell me whether you're running Google Ads, Meta, Microsoft, Amazon, or several, I can recommend the best architecture and specific tools for automating the whole experiment → measurement → winner rollout loop.

If your goal is run bidding experiments, measure incremental ROI, and avoid manually changing campaigns, I’d use an experimentation/optimization layer that sits on top of the ad platforms rather than building everything yourself.

Good options:

• Google Ads + Search Ads 360 — strongest if Google is a major channel and you want automated bid strategies, portfolio bidding, and structured experiments.
• Skai — good for cross-channel paid search/social optimization, automated bidding, budget allocation, and experimentation.
• MarinOne — useful for automated bid management and cross-channel campaign optimization.
• Optmyzr — particularly good if you want granular, rule-based automation and the ability to test bidding approaches without constantly operating the platforms manually.
• Madgicx — more oriented toward automated Meta/Google optimization, especially for ecommerce-style workflows.

For actually comparing ROI, though, the important piece is the measurement layer. I’d structure it like:

Ad platforms → automated bidding tool → warehouse (BigQuery/Snowflake/etc.) → attribution/experiment analysis → automated decision

And don't compare strategies solely on platform-reported ROAS. For experiments, ideally compare incremental revenue/profit per dollar, using geo or audience holdouts where possible. Otherwise a strategy can appear to improve ROAS simply by capturing conversions that would have happened anyway.

If you tell me which platforms you're running (Google, Meta, TikTok, Amazon, etc.) and whether your KPI is revenue, CAC, or profit, I can narrow this to the best 2–3 setups.

If your goal is “run bidding experiments, measure incremental ROI, and automatically promote winners without living in Google/Meta UIs,” I’d look at three approaches:

1. Best off-the-shelf: Optmyzr

Optmyzr is probably the closest fit.

It can:

• Create/manage Google Ads bidding experiments.
• Test Maximize Conversions, Target CPA, and Target ROAS against existing strategies. help.optmyzr.com
• Compare experiment vs. control and assess statistical confidence. help.optmyzr.com
• Automatically adjust Target CPA/ROAS using rules.
• Apply winning experiment changes or terminate losing experiments without manually working through the Ads UI. help.optmyzr.comwww.optmyzr.com
• Operate across Google, Microsoft, Amazon and paid-social workflows. help.optmyzr.comwww.optmyzr.com

I'd choose this if: you want something you can deploy quickly rather than building experimentation infrastructure.

2. Best if you're building a proprietary bidding system: Google Ads API

Google's current API has first-class experiment functionality: you can programmatically create experiments, define control/treatment campaigns, schedule them, retrieve experiment statistics, and promote or end experiments. developers.google.com

Importantly, Google's May 2026 API update expanded experiment functionality and added direct experiment reporting, making this substantially more attractive for an automated system. ads-developers.googleblog.com

A good architecture would be:

Experiment service → Google Ads API → control/treatment → warehouse → ROI/incrementality model → winner decision → API promotion

Then your experimentation layer can test things like:

• tROAS 300% vs 400%
• tCPA $50 vs $70
• Max Conversions vs tCPA
• different conversion-value models
• different geographic/audience bidding policies

3. If ROI means incremental ROI, not platform ROAS

This distinction matters a lot.

Don't optimize solely for:

revenue / ad spend

Instead, have your experimentation system estimate:

incremental profit / incremental ad spend

Otherwise you can "prove" that a bidding strategy wins simply because it captures conversions that would have happened anyway.

I'd therefore put an experimentation/measurement layer on top of whichever execution system you choose:

My recommendation: start with Optmyzr if you want this running quickly. If you're managing enough spend/accounts that bidding experimentation itself is becoming a product capability, build around the Google Ads API and keep the experiment/ROI logic in your own infrastructure.

If you tell me roughly monthly ad spend + Google/Meta/both + ecommerce vs lead gen, I can suggest a concrete stack and experiment design.

If your goal is “run bidding experiments, measure incremental ROI, and automatically roll out winners without living inside Google/Meta”, I’d look at these:

Best fit: Optmyzr

Optmyzr is probably the closest match.

• Google Ads experiments: lets you compare control vs. experiment and evaluate metrics including conversion value, CPA and ROAS. help.optmyzr.com
• Automated bid-strategy tests: it can create experiments for Maximize Conversions, Target CPA, and Target ROAS. help.optmyzr.com
• Automated execution: rules can run on schedules and modify campaigns without you manually opening the ad platform. help.optmyzr.com
• Supports Google, Microsoft, Amazon, Meta and LinkedIn, so it's useful if you're eventually running cross-channel tests. www.optmyzr.com
• It can also promote a winning Google experiment back to the original campaign or turn it into a new campaign. help.optmyzr.com

If you're Google-heavy: Google Ads API

If you're technical and want maximum control, I'd actually consider building the experimentation layer directly on the Google Ads API. Google has expanded its API experimentation functionality substantially in 2026, including direct experiment reporting. developers.google.com

That gives you a setup like:

Experiment manager → Google Ads API → randomized control/treatment → revenue/CRM data → ROI calculator → automatic winner promotion

This is preferable if you want to test things like:

• tROAS 300% vs 400% vs 500%
• Maximize Conversion Value vs tROAS
• different bid targets by geography
• budget allocation strategies
• first-party/offline revenue as the optimization signal

If you're heavily Meta-focused

Madgicx is worth evaluating. Its tooling explicitly compares bidding methods and other campaign dimensions based on ROAS, alongside creative and audience testing. academy.madgicx.com

What I'd choose

One important distinction: ROAS isn't necessarily incremental ROI. If you're trying to answer “Did this bidding strategy actually create more profit than the control?”, I'd make the experiment system capture incremental revenue/profit, not just the ad-platform-reported ROAS. That becomes especially important when comparing bidding strategies because the platforms' attribution models can make a strategy look better without actually creating more business.

If your goal is “test bidding strategies, measure actual ROI, and avoid manually operating Google/Meta/TikTok”, I’d look at three levels:

1. Sellforte — probably the closest match to what you described. It can measure incremental ROAS, design experiments, recommend bid/budget changes, and push approved changes directly into Meta, Google, and TikTok through APIs. sellforte.com

Sellforte

2. AppsFlyer Incrementality — better if your primary need is experiment design + causal measurement rather than having an automated media buyer. It supports automated holdouts, cross-network experiments, and incremental-lift measurement. www.appsflyer.com

AppsFlyer Incrementality

3. Google Ads Experiments + API — if most of your spend is Google, you may not need another platform. Google supports controlled experiments specifically for testing bidding strategies, including through its API, so you can automate experiment creation/monitoring rather than manually operating the UI. business.google.com

Google Ads Experiments

What I'd choose

For a serious experimentation setup, I'd structure it as:

Control: current bidding strategy
Treatment: experimental bidding strategy
Keep constant: creative, targeting, landing page, budget constraints where possible
Primary metric: incremental profit / incremental ROAS
Secondary metrics: conversion value, CAC/CPA, spend, volume

That's important because a platform-reported ROAS increase isn't necessarily incremental ROI. Google itself recommends isolating the bid strategy as the single experimental variable when testing value-based bidding.

If you tell me which platforms you spend on (Google, Meta, TikTok, etc.), approximate monthly spend, and whether you're ecommerce or lead-gen, I can narrow this down to the best 2–3 options and tell you what I'd actually deploy.

If your goal is “test bidding strategies, measure incremental ROI, and automate the changes without living in Google/Microsoft Ads”, I’d look at three approaches:

1. Best turnkey option: Optmyzr

This is probably the closest match to what you're describing.

• Runs/organizes Google Ads experiments and compares treatment vs. control.
• Can test Maximize Conversions, Target CPA, Target ROAS, etc. against existing strategies. help.optmyzr.com
• Its Rule Engine can automatically adjust bids, budgets, CPA/ROAS targets, and other campaign parameters on a schedule. help.optmyzr.com
• Supports Google, Microsoft, Amazon and other PPC workflows. help.optmyzr.com
• You can incorporate first-party business data rather than optimizing purely toward platform-reported ROAS. www.optmyzr.com

I'd choose this if: you want something your marketing team can operate without building an internal bidding system.

2. Most control: build on the ad-platform APIs

For Google specifically, the Google Ads API now has native experiment workflows: you can create control/treatment arms, schedule the test, retrieve performance metrics, and then end or promote the winner programmatically. developers.google.com

That gives you a nice architecture:

experiment generator → Google Ads API → traffic split → conversion/revenue data → ROI evaluator → winner/loser decision → API applies result

This is the better route if you want to experiment with your own bidding algorithms, rather than simply testing Google's Smart Bidding variants.

Microsoft also exposes campaign experiments through its API, including budget/traffic splits and limited test periods. learn.microsoft.com

3. More sophisticated cross-channel bidding: Marin Software

Marin is worth considering if you're operating across multiple ad platforms and want an external bidding layer. Its bidding system is specifically designed to calculate bids independently of the native platform bidding systems, including when advertisers want to use more complex revenue/margin data. support.marinsoftware.com

What I'd actually do

If you're serious about ROI rather than just ROAS, I'd structure experiments around something like:

Then evaluate incremental profit / incremental ad spend, not just the platform's reported ROAS.

For example, a strategy that produces:

• +15% revenue
• +8% ROAS
• −3% profit

isn't actually a winner.

My pick: start with Optmyzr if you want this running quickly and don't want to build infrastructure. If you have engineering resources and want genuinely novel bidding strategies, use the Google Ads API's experiment infrastructure + your own evaluation/decision layer. Google explicitly supports programmatic experiments for bidding changes now. developers.google.com

If you tell me whether you're primarily Google, Meta, Microsoft, or multi-platform, I can narrow this down to the best stack and experimentation architecture.

If your goal is “run bidding experiments, measure incremental ROI, and automatically act on winners without living in Google/Meta Ads Manager,” I’d look at these options:

Best fit: Optmyzr

Optmyzr is probably the closest off-the-shelf fit.

• Experiments: can create/manage Google Ads campaign experiments and compare treatment vs. control. help.optmyzr.com
• Automated bidding: supports strategies around Target CPA/ROAS and can automatically adjust targets based on rules. help.optmyzr.com
• Automation across platforms: its Rule Engine supports Google, Microsoft, Amazon and Meta, among others. help.optmyzr.com
• Guardrails: you can make changes automatically or have them presented for review, which is useful when experimenting with aggressive strategies. www.optmyzr.com

A typical setup would be:

Control: current Target ROAS 400%
Treatment A: Target ROAS 350%
Treatment B: Target ROAS 450%
Treatment C: custom bid/target rules

Then evaluate incremental revenue, spend, CPA, ROAS, conversion volume, and confidence, rather than simply picking whichever variant has the highest observed ROAS.

If you want maximum control: Google Ads API

If you're comfortable building software, the Google Ads API is more powerful. Google now supports system-managed and intra-campaign experiments, including bidding experiments, with APIs for creating, running, measuring, and ultimately promoting or ending experiments. developers.google.com

You can build a system like:

Experiment generator → Ads API → randomized/control treatment → warehouse → ROI analysis → winner decision → Ads API promotion

Google also exposes bidding-strategy simulations for Target CPA and Target ROAS, which can be useful for estimating effects before committing to a change. developers.google.com

This is the route I'd choose if experimentation itself is part of your product/data infrastructure.

If you're managing lots of channels: Skai

Skai is worth evaluating for larger/multi-channel programs. Its experimentation product is designed around A/B and pre/post testing across Search, Social, and Commerce, including bidding, targeting, creative, and landing-page tests. skai.io

My recommendation

One important distinction: don't optimize the experiment around reported ROAS alone. For serious bidding experiments, I'd store spend and conversion value at the experiment-arm level and calculate incremental profit/ROI against a randomized control. Otherwise, a bidding strategy can appear to “win” simply by buying more of the easiest conversions.

If you tell me which platforms you're running (Google, Meta, TikTok, etc.) and roughly how much you spend/month, I can narrow this down to the best stack and architecture for you.

If you want to run experimental bidding strategies, measure incremental ROI, and avoid manually operating Google Ads/Meta/etc., you’re usually looking for a marketing optimization layer rather than another ad platform UI.

The right stack depends on how automated you want it:

1. Build a custom experimentation + optimization layer (most flexible)

Good if you have engineering resources.

Core components:

• Ad platform APIs
  • Google Ads API
  • Meta Marketing API
  • Microsoft Ads API
  • TikTok Ads API
• Data warehouse
  • Snowflake, Google BigQuery, or Amazon Redshift
• Experiment engine
  • Bayesian testing, multi-armed bandits, causal impact models
  • Tools/libraries:
    • Optimizely (experimentation)
    • Eppo (experimentation analytics)
    • custom Python/R models
• Decision layer
  • Calculates:
    • target CPA/ROAS changes
    • budget reallocations
    • bid multipliers
    • audience/channel allocation

Example workflow:

Ad APIs → Warehouse → Attribution model → Experiment engine
                                      ↓
                              Bid/budget decisions
                                      ↓
                              Ad APIs push changes

This lets you test things like:

• "Does lowering tCPA by 15% improve profit?"
• "Does shifting 20% budget from Meta to Google increase incremental revenue?"
• "Does a new bidding rule outperform platform Smart Bidding?"

2. Use an enterprise media optimization platform (less engineering)

Better if you want automation quickly.

Options:

• Skai — paid search/social automation, portfolio optimization, experimentation
• Marin Software — search/social campaign management and optimization
• Smartly.io — large-scale paid social automation
• Kenshoo (now part of Skai) — enterprise bid management

These can automate:

• bid rules
• budget pacing
• portfolio optimization
• cross-channel reporting

3. Use algorithmic bidding platforms (closest to "AI trader for ads")

Useful if you have enough conversion volume.

Look at:

• Acquisio — automated PPC optimization
• Shape.io — programmatic-style marketing automation
• Madgicx — Meta-focused automation

These typically work by:

• running many bid/budget variants
• finding statistically better performers
• reallocating spend

4. If your main goal is true ROI measurement, fix attribution first

Many bidding experiments fail because the measurement layer is weak.

Consider:

• Northbeam — incrementality-focused attribution for ecommerce
• Triple Whale — ecommerce analytics/attribution
• Rockerbox — marketing measurement

You want to compare:

Platform ROAS

"Google says this campaign generated $100k"

vs.

Incremental ROAS

"This campaign generated $35k of revenue that would not have happened otherwise"

A practical setup I’d use

For a mid-sized advertiser:

Google Ads API + Meta API
          ↓
       BigQuery
          ↓
   dbt transformation layer
          ↓
 Incrementality + profit model
          ↓
 Python optimization service
          ↓
 API pushes bid/budget changes

Run experiments like:

A few questions would change the recommendation:

1. Which platforms are you optimizing (Google Search, Meta, TikTok, Amazon, LinkedIn)?
2. Are you optimizing for ROAS, CPA, profit margin, LTV, or revenue growth?
3. Rough monthly ad spend ($10k, $100k, $1M+)?
4. Do you have engineering/data resources, or do you want a no-code solution?

If you want to run experimental bidding strategies, measure incremental ROI, and avoid manually touching Google Ads/Meta/etc., you’re usually looking for a combination of:

1. API-based campaign control
2. Experiment orchestration
3. Attribution/measurement
4. A modeling layer for bid optimization

The right stack depends on how sophisticated you want to get.

1. No-code / low-code: easiest way to start

Optmyzr

Good for:

• Automated PPC rules
• Bid testing
• Budget pacing
• Google Ads automation
• Reporting

Works well if you want to test things like:

• Target CPA vs Target ROAS
• Portfolio bidding changes
• Budget allocation strategies

Skai

Good for:

• Multi-channel advertisers
• Automated experimentation
• Large budgets
• Paid search + social management

Marin Software

Good for:

• Enterprise paid search automation
• Bid management
• Cross-channel reporting

2. Programmatic / API-first approach (more control)

Build a system around:

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

Your system can:

1. Create experiment cells:
   • Control: existing bidding strategy
   • Variant A: aggressive ROAS target
   • Variant B: lower CPA target
   • Variant C: custom algorithm

2. Automatically:
   • Change bids/budgets
   • Pull spend/conversion data
   • Calculate lift
   • Roll back losing strategies

Typical architecture:

Ad APIs
   ↓
Experiment Manager
   ↓
Data Warehouse
   ↓
Attribution Model
   ↓
Optimization Algorithm
   ↓
Bid/Budget Changes

Common infrastructure:

• Data warehouse: Snowflake or Google BigQuery
• Pipelines: Airbyte or Fivetran
• Modeling: Python + Bayesian testing / uplift modeling

3. For serious ROI optimization: build a bidding simulator

Instead of immediately changing live bids, create a sandbox:

Inputs

• Historical auctions
• Impression share
• CPC curves
• Conversion rates
• Customer lifetime value
• Seasonality

Test:

• "What happens if we increase bids 15%?"
• "What if we optimize for contribution margin instead of ROAS?"
• "What if we shift 20% budget from search to Meta?"

Methods:

• Bayesian optimization
• Multi-armed bandits
• Reinforcement learning (more advanced)
• Incrementality testing

4. Measurement tools (often the missing piece)

ROAS from ad platforms can be misleading. Add:

• Rockerbox — attribution and incrementality
• Northbeam — ecommerce attribution
• Triple Whale — Shopify/ecommerce analytics

For higher rigor:

• Geo experiments
• Holdout audiences
• Conversion lift tests

A practical stack I’d use

For a growth team spending ~$10k–$500k/month:

• Google Ads API + Meta Marketing API
• BigQuery
• Airbyte/Fivetran
• Python experiment engine
• Looker Studio/Tableau dashboards
• Bayesian ROI comparison model
• Automated rollback rules

For enterprise spend:

• Skai or Marin for execution
• Custom modeling layer for strategy

For a solo marketer/small team:

• Optmyzr + native experiments + strong reporting

The biggest mistake is optimizing for platform-reported ROAS alone. A good system tests incremental profit, not just which bidding setting gets the most attributed conversions.

If your goal is “run bidding experiments, measure incremental ROI, and never touch the ad UI manually,” I’d build around the Google Ads API + an experiment/orchestration layer, rather than buying a generic bid-management tool.

Best setup

1. Google Ads API — execution + experiments

• Create control/treatment experiments programmatically.
• Change bidding strategies or targets in the treatment.
• Schedule, start, monitor, and ultimately promote/end experiments via API.
• Google now supports direct experiment reporting, including treatment vs. control metrics and statistical comparisons such as p-values. developers.google.comdevelopers.google.com
• It also exposes bid simulations for Target CPA/Target ROAS, useful for screening candidate strategies before spending real money. developers.google.com

Google Ads API Experiments documentation

2. Your own experimentation service — decision engine
Have a small service generate experiments like:

Campaign: US / Nonbrand / Search

Control:
  Target ROAS = 500%

Treatment A:
  Target ROAS = 400%

Treatment B:
  Target ROAS = 600%

Treatment C:
  Target CPA = $75

Then automatically:

1. create experiments
2. allocate traffic
3. collect spend/conversion revenue
4. wait for sufficient data
5. calculate incremental ROAS / profit
6. declare winner
7. promote winner or terminate losers
8. log the result for future experiments

Google's API explicitly supports promoting effective treatments back to the base campaign. developers.google.com

For ROI, don't optimize to platform ROAS alone

I'd calculate something like:

Incremental ROI = (incremental conversion value − incremental ad spend) / incremental ad spend

rather than simply comparing reported ROAS. Ideally pull actual revenue/margin from your backend or warehouse, especially if conversion values don't represent contribution margin.

Your experiment database might look like:

One important caveat

Don't make the system flip strategies every day based on noisy short-term ROI. Google's current guidance recommends roughly 4+ weeks for experiments, allowing 1–2 weeks for automated bidding to recalibrate, and generally recommends a 50/50 traffic split when appropriate. developers.google.comdevelopers.google.com

If you're running multiple ad platforms

I'd use:

Ad platform APIs → warehouse → experimentation engine → dashboard

rather than trying to make one third-party bidding product the source of truth.

For example:

• Google Ads API → experiments + bidding changes
• Meta Marketing API → campaign/ad-set changes
• BigQuery/Snowflake → spend + conversion + actual revenue
• Python service → experiment assignment/statistics/decisioning
• Looker/Metabase → results
• Scheduler/Airflow/Dagster → automation

That gives you a reusable framework where the strategy is just a parameter. You can test tROAS targets, tCPA targets, budgets, bid modifiers, campaign structures, etc., without manually operating the platforms.

If Google Ads is your main channel, I'd start with the Google Ads API rather than a third-party tool—the API's experiment functionality has expanded substantially in 2026 and now supports direct experiment reporting. ads-developers.googleblog.com

If your goal is “run bidding experiments, measure ROI, and automatically act on winners without manually touching Google/Meta”, I’d look at three approaches:

1. Best off-the-shelf: Optmyzr

Optmyzr

This is probably the closest match if you want a no/low-code experimentation and automation layer.

• Automates bid/target adjustments on a schedule.
• Lets you build rules around CPA, ROAS, spend, conversion value, etc. help.optmyzr.com
• Its Campaign Experiments tooling can compare experiments against the original campaign and make recommendations based on statistical significance. help.optmyzr.com
• Supports Google, Microsoft, Meta, LinkedIn and Amazon workflows to varying degrees. help.optmyzr.com

Important caveat: it can't directly manipulate Google's underlying Smart Bidding auction-level bids. Instead, it can experiment with things like target CPA/ROAS, budgets, campaign structure, and other strategic inputs. help.optmyzr.com

2. Best if you want rigorous experiments: Google Ads API + your own experiment engine

If you're technical, this is what I'd choose.

Google's API now has native experiments, including system-managed and intra-campaign experiments specifically designed to test campaign/bidding changes. You can programmatically create the control/treatment, schedule it, pull performance, and ultimately end or promote the treatment. developers.google.comwww.marpipe.comdevelopers.google.com

You could build:

Experiment manager → Google Ads API → control/treatment → warehouse → ROI evaluator → automatic winner promotion

For example:

I'd strongly recommend evaluating incremental profit/iROAS, rather than simply platform-reported ROAS. Standard ROAS can credit ads for conversions that would've happened anyway; holdouts/experiments give you a better counterfactual. www.marpipe.com

3. If you need Meta + Google + other platforms

I'd use an automation platform above the ad platforms, rather than trying to build independent bidding logic for every API.

A practical stack would be:

Optmyzr / custom orchestration
↓
Google Ads API + Meta Marketing API + other ad APIs
↓
BigQuery/Snowflake
↓
Experiment evaluation
↓
Automatic promote/rollback

That gives you a single place to say things like:

“Test three target-ROAS strategies for 14 days. Require ≥95% confidence and ≥$5k spend. Optimize for contribution-margin ROAS. Automatically promote a winner if it beats control by ≥10%; otherwise roll back.”

What I'd pick

If you want something working quickly: Optmyzr.

If you're building an internal growth/bidding system: Google Ads API + your own experimentation/evaluation layer.

If ROI is the actual objective: don't optimize purely for reported ROAS. Design the system around incremental ROAS or incremental contribution profit, with control/holdout groups where possible.

Google's current API supports both standard and portfolio bidding strategies, so you can also experiment at different campaign scopes rather than treating every campaign independently. developers.google.com

If you tell me Google Ads only vs. Google + Meta, approximate monthly spend, and whether you have engineering resources, I can narrow this to a concrete stack and architecture.

If you want to run experimental bidding strategies, measure incremental ROI, and avoid manually changing Google Ads/Meta/etc., you’re usually looking for a bid management + experimentation + attribution layer, not just an ad dashboard.

A few good options depending on your setup:

For a more scientific approach, I’d structure it like this:

1. Create experiment buckets
   • Control: existing bidding strategy
   • Variant A: target CPA change
   • Variant B: target ROAS change
   • Variant C: custom algorithm/bid modifier

2. Automate deployment
   • Use an API layer (Google Ads API, Meta Marketing API, etc.)
   • Or a platform like Skai/Optmyzr to push changes

3. Measure incrementality, not just ROAS
   • ROAS can improve because you bought conversions you would have gotten anyway.
   • Track:
     • incremental conversions
     • incremental revenue
     • CAC
     • contribution margin
     • payback period

4. Store experiment data
   • Data warehouse: BigQuery, Snowflake, Redshift
   • Reporting: Looker Studio, Tableau, Power BI
   • Experiment logic: Python/R or a marketing science platform

If you’re running serious experimentation, a common stack is:

Ad platforms → APIs → data warehouse → attribution model → automated bidder → experiment dashboard

For smaller teams:
Optmyzr + Google Ads API + Looker Studio is often enough.

For a growth team spending six figures/month or more:
Skai/Marin + warehouse + custom incrementality testing is usually closer to what you want.

A few details would change the recommendation:

• Which channels (Google Search, Meta, TikTok, Amazon, LinkedIn)?
• Monthly ad spend range?
• Are you optimizing for revenue, leads, subscriptions, or profit/margin?

If your goal is “test bidding strategies automatically, measure ROI, and promote the winner without living in Ads Manager”, I’d look at three approaches:

1. Best turnkey option: Optmyzr

Optmyzr is probably the closest fit if you want a UI rather than building infrastructure.

It can:

• Create experiments for Maximize Conversions, Target CPA, and Target ROAS. help.optmyzr.com
• Compare experiment vs. control and make statistical-significance-based recommendations. help.optmyzr.comhelp.optmyzr.comdevelopers.google.comdevelopers.google.com
• Automatically modify bidding targets through scheduled rules rather than requiring manual bid changes. help.optmyzr.com
• Take actions such as ending an experiment or converting a winner into a campaign. help.optmyzr.comhelp.optmyzr.comdevelopers.google.comdevelopers.google.com

I'd choose this if: you're running Google/Microsoft Ads and want an automation layer you can configure without engineering a system.

2. Best if you want full control: Google Ads API + your own experiment engine

Google's current API has surprisingly good native experimentation support. You can programmatically create control/treatment experiments, split traffic, change bidding strategies, run them, report results, and ultimately promote or end the treatment. developers.google.com

You can also pull bid simulations for campaign, ad-group, keyword, and portfolio bidding changes before actually deploying them. developers.google.com

A sensible architecture is:

Google Ads API → experiment orchestrator → warehouse → ROI/statistics engine → decision rules → Google Ads API

For example:

• Control: Target ROAS 400%
• Treatment A: Target ROAS 500%
• Treatment B: Target ROAS 600%
• Treatment C: Maximize Conversion Value
• 50/50/50/50 traffic allocation
• Run ≥4 weeks
• Calculate revenue, contribution margin, CAC, ROAS and incremental ROAS
• Automatically promote a winner only when it clears your statistical + business thresholds

Google specifically recommends allowing time for automated bidding to recalibrate and generally running experiments long enough to capture weekly cycles and conversion delays. developers.google.com

3. If “ROI” really means incremental ROI

This is the important distinction: platform ROAS isn't necessarily causal ROI.

If you're trying to answer “Did this bidding strategy actually create more profitable revenue?”, I'd add geo/time holdouts or another incrementality framework. Incremental ROAS measures incremental revenue divided by ad spend, and controlled experiments are generally the strongest way to estimate it. www.marpipe.com

So my recommendation would be:

Optmyzr → if you want this working quickly with minimal engineering.

Google Ads API + your own experimentation layer → if you're building a serious internal optimization system and want arbitrary strategies, automated decisioning, and your own ROI model.

API + incrementality testing → if you're optimizing for true business ROI rather than simply maximizing the ROAS reported by the ad platform.

If you tell me whether you're primarily running Google, Meta, or both, I can narrow this down to the best stack and show you what the automated experiment loop should look like.